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Previous Issue

BULLETIN OF THE
NATIONAL NATURAL RESOURCES
MANAGEMENT SYSTEM
NNRMS (B) - 36
Geospatial Applications and
Decision Support Systems
June 2012
NNRMS
Department of Space
Antariksh Bhavan, New BEL Road
Bangalore - 560 231
INDIA
Editorial Advisors
Kiran Kumar AS, Director, SAC
Dadhwal VK, Director, NRSC
Shivakumar SK, Associate Director, ISAC
Editorial Board
Diwakar PG, Director, EOS
Krishnamurthy YVN, Deputy Director, (RSA), NRSC
Saha SK, Dean (Academics), IIRS
Ajai, Group Director, MPSG/EPSA, SAC
Technical Guidance
Shantanu Bhatawdekar, Associate Director (A), EOS
Technical Support and Compilation
Paul MA, Scientist/Engineer SE, EOS
Arunachalam A, Scientist/ Engineer SF, EOS
For details and inputs, please write to
Director
Earth Observations System
ISRO Headquarters
Antariksh Bhavan
New BEL Road
Bangalore 560 231
Email: [email protected]
Fax: 91-80-23413806
Published by
P&PR Unit, ISRO Headquarters on behalf of
National Natural Resources Management System (NNRMS)
ISRO Headquarters
Antariksh Bhavan, New BEL Road
Bangalore 560 231
Designed by
Imagic Creatives Pvt. Ltd., Bangalore 560 071
Printed at
Aditya Printers, Bangalore
PREFACE
Geospatial technology has become an indispensible tool for decision making in many
disciplines of Natural Resources Management. It has created a new wave and has
made a significant impact on the society through varieties of actionable products,
applications and solutions. GIS tools and related technologies have made rapid
strides, in the recent years, and made commendable contributions in many diversified
areas of natural resources management, infrastructure development, societal
applications and disaster management. Improved decision making, near-real time
data processing and solutions have become more realistic with the advancement of
Information and Communication Technology (ICT) and power of computation.
Indian Space Research Organisation (ISRO), Department of Space has been actively
participating in many developmental activities from national to local level in creating
large scale spatial data repository and archival mechanisms. Several Geospatial tools,
data visualisation and decision making tools have been developed and deployed for
operational use. In addition, there are many national level missions and programmes
of ISRO, in which large scale geospatial databases have been created and deployed
on the web for diversified applications. New developments in WebGIS, 3D GIS,
cloud computing, virtual reality applications, location based services and geospatial
modelling are making inroads into decision making arena. The synergy of Remote
Sensing, GIS, GPS and ICT are proving to be most effective in many newer applications
and developmental activities.
The current issue of NNRMS Bulletin highlights some of the important geospatial solutions
and decision support systems of ISRO, Department of Space. Contemporary technologies,
including enterprise and open source solution, are effectively used in developing such
systems. We sincerely hope that this bulletin would benefit scientists, researchers and
academicians who are engaged in similar studies and research activities.
PG Diwakar
Director
Earth Observations System
CONTENTS
Page No.
1
Free and Open Source Software (FOSS) Tools for
Web Based Geospatial Solutions
1-11
Arunachalam A and Diwakar PG
EOS Programme Office, ISRO HQ
2
NRDB – Versatile Cyber Infrastructure for Spatial Data Repository
and Dissemination
13-18
Pushpalata Shah, Rajendra Gaikwad & NRDB Team
Space Applications Centre, Ahmedabad
3
Bhuvan - Gateway to Indian Earth Observation Data Products
and Services
19-27
Team Bhuvan
National Remote Sensing Centre, Hyderabad
4
ISRO's contribution in the Field of Meteorological and
Oceanographic Studies
28-32
Yagna Mankad & Pushpalata Shah
5
India-WRIS WebGIS-Design and Development of Web Enabled
Water Resources Information System of India
33-43
Sharma JR and Project Team
RRSCs / National Remote Sensing Centre, Hyderabad
6
In Season Progressive Assessment of Rain Fed Agricultural
Crop Status in India using Geospatial Technique
44-49
Manab Chakraborty and Panigrahy S
7
Decision Support System for Integrated Development of Apple
Orchards in Himachal Pradesh under the Technology Mission
Sushma Panigrahy1, Bhatt NB1, Oza SR1, Alka Sharma2, Parihar JS1 and Singh H P3
1
2
HP Remote Sensing Cell, Shimla
3
ICAR, New Delhi
50-56
Page No.
8
Spatial Decision Support System for Biodiversity Conservation
Prioritisation using Geospatial Modeling Approach
Sameer Saran1 and Harish Karnatak2
1
Indian Institute of Remote Sensing, Dehradun
2
9
ONERS: Web Based Indigenous Decision Support System
Raja Shekahr SS1, Shantanu Bhatawdekar2, Krishna MurthyYVN1,
Srinivas CV3 and Venkatesan3
1
Regional Centres, NRSC, Hyderabad
2
ISRO HQ, Bangalore
3
Radiological Safety Division, IGCAR, Kalpakkam
10
A Novel Geospatial Framework for Providing Effective Planning
and Developmental Inputs for District Resources Plan
Chutia D, Singh PS, Goswami C, Goswami J, Das R, Rocky P and Sudhakar S
North Eastern Space Applications Centre (NESAC), Umiam
11
12
Decision Support Systems for Production and
Protection Forestry
Varghese AO, Arun Suryavamshi, Rajashekhar SS1, Joshi AK and
Krishnamurthy YVN1
Regional Remote Sensing Center – Central, Nagpur;
1
RC Office, National Remote Sensing Centre, Hyderabad
Development of Flood Hazard Maps for Assam State, India using
Historical Multitemporal Satellite Images
Sharma SVSP, Srinivasa Rao G, Bhatt CM, Manjusree P & Bhanumurthy V
57-67
68-73
74-80
81-86
87-92
Introduction
Geospatial information is a set of data referenced to a physical location or a
place through a set of geographic coordinates, which can be gathered, processed and
visualised on a simple computer system. A Geographic Information System (GIS) is a
combination of data and software tools configured on a computer system that helps to
store, query, analyse and display geographically referenced data. Such data, also known
popularly as geospatial data, describe both locations and characteristics of spatial
features such as roads, land parcels, natural features and others on the Earth’s surface
(Chang, 2006). In recent years, with the advent of cheap and powerful computers, the
consumer demand for such location specific data and information has increased by
many folds that has made GIS tools and technologies more popular than ever before.
(www.physicalgeography.net). This has also enabled the common man to be sensitised
on the subject, including the utilisation of these technologies on a daily basis.
GIS operates on multiple levels; on the most basic level, GIS is used for
computer based cartography, i.e., for mapping; but the real potential of GIS comes
from its ability of using spatial data and statistical methods to analyze, process and
depict geographic information (Sutton et al., 2009). GIS can manage unlimited layers
of geographic data attached with attributes that are stored and organised in database
management systems. Use of databases makes the process of managing geo-data
faster and efficient, besides providing flexibility to query based on keywords, display
and visualisation of required area.
Within the last decade itself, the involvement of
GIS as a key technology for management support and aid in almost all possible fields
has made GIS as a part of common man’s life. The application of GIS software in
several sectors has become very relevant in the recent times (Paul Bolstad, 2008).
Traditionally, GIS as a technology was dominated by a few commercial software
packages and hence the usage, applications and database organization were
dependent on the functionalities provided by such tools. While such tools helped in
addressing various user requirements with regard to data organisation and customised
solutions, there were limitations for which one had to wait for upgrades and
improvements in the software. The traditional model of GIS was more a desktop
version with some geospatial functionalities provided through tools and techniques
in a desktop environment. Slowly this grew into enterprise class of solutions, advanced
2 0 1 2
J U N E
N N R M S
Arunachalam A and Diwakar PG
EOS Programme Office, Indian Space Research Organisation (ISRO)
Headquarters, Bangalore – 560 231, India
B U L L E T I N
-
FREE AND OPEN SOURCE SOFTWARE
(FOSS) TOOLS FOR WEB BASED
GEOSPATIAL SOLUTIONS
facilities with high-end graphics compatibility, multiple users/ licensing facilities and finally the web-based GIS
solutions. Today, we see enterprise class of solutions on client-server systems, which include both thin and thick
client solutions. The technology has grown considerably over the past two decades and the software capabilities
allow wide varieties of possibilities on diversified environment. Setting up of a GIS facility could now be done
with diversified options, ranging from simple desktop GIS solution to high-end enterprise class facility with
little efforts. However, it is to be understood that the geospatial solutions depends on the type of usage, which
may range from database creation, data access, query, visualisation, analysis and other computations. All these
have their own requirements and hence depend upon the hardware, system software and application software
for services and delivery of end products.
During 1990s, ISRO’s efforts related to geospatial information and services were more focussed on
addressing district level problems and solutions through Integrated Mission for Sustainable Development (IMSD)
and National natural Resources Information System (NRIS) programmes. However, since 2000, the country has
seen an all round development with popularisation of geospatial technology and has enabled many departments,
NGOs, academia and industries to adopt the technology in their own way, making a bigger canvas of GIS
applications. At the same time, the synergy of GIS and ICT has brought out a revolution through many new
developments that has allowed dynamic access to geospatial information and at the same time reduced burden
on the users to find their own software solutions. Amongst the many initiatives that took place during this
period are the attempts related to National Spatial Data Infrastructure (NSDI) and National natural Resources
DataBase (NRDB), which not only consolidated the Data and content standards but also paved the way for
organising Spatial Data Infrastructure (SDI) that could be used for national development.
Yet another unique initiative taken up in recent past by ISRO/ DOS has been designing, development
and launching of the image and map data visualization in 2D and 3D, through ‘Bhuvan’. It showcases
Indian imaging capabilities by providing seamless viewing of coarse-resolution to high-resolution raster
images, database cataloguing services, varieties of value added services in 2D/ 3D, vector services, user-friendly
interactive services, etc.
While large scale efforts were initiated towards data standards and generation of content under such
programmes, there were parallel attempts to develop varieties of geospatial solutions/ information systems for
decision making.
While commercially available software tools have been used in the past to develop such
decision support tools and techniques, there were considerable efforts to explore the huge potential of Free and
Open Source Software (FOSS) tools to provide geospatial solutions. Through this review article, an attempt is
made to highlight the power of Free and Open Source Software Tools (FOSS) for developing Web Enabled
Geospatial Information Systems.
Web GIS and Its Need
The WebGIS technology promises the highest potential user base and lowest cost per user. Stimulated
by the widespread availability of the internet and market demand for greater access to geographic information,
GIS solution providers have been quick to release products that harness the power of the internet. Many
solution providers have chosen to exploit the unique characteristics of the World Wide Web, by developing GIS
technology that integrates with web browsers and servers (WebGIS Manual, 2007).
Parallely, FOSS for GIS also
has increased tremendously exploiting the power of internet and broadband services.
There are innumerable applications for geospatial information. It is being widely used in many different
areas, such as natural resources management, conservation projects, environmental impact assessment, disaster
2 0 1 2
management, land management and
cadastre projects. In a few years from now,
J U N E
geospatial data would be integrated in
all types of information systems that
provide visualisation solutions in a
-
web based environment. Some of the
important GIS applications area is shown
B U L L E T I N
in Figure 1.
What is FOSS?
In the context of free and opensource software, ‘free’ refers to the
N N R M S
freedom to copy and re-use the software,
rather than the price of the software
(Ghosh et al., 2002). FOSS is liberally
licensed to grant the right of users to use,
Fig. 1: Wide variety of Web-GIS applications
study, change, and improve its design
through the availability of its source code.
This approach has gained both momentum and acceptance, as the potential benefits are
increasingly being recognized by both individuals and corporations. FOSS is an inclusive term
that covers both ‘free’ and ‘open source’ software, which despite describing similar development
models, have differing cultures and philosophies. While ‘free’ software focuses on the
philosophical freedoms it gives to users, ‘open source’ software focuses on the perceived
strengths of its peer-to-peer development model.
The Open Source Initiative (OSI) maintains the Open Source Definition
(www.opensource.org), which states that for a program to be considered as open source, it
must satisfy the following:
•
Be freely distributable with no requirement for fees or royalties for doing so.
•
Make the source code of the program freely available and distributable.
•
Allow for the modification of the source code and distribution of the modified work
under the same terms as the original.
•
Support the integrity of the author's code. The author may restrict the distribution of
modified source code as long as they allow for the distribution of patches to the code.
•
Not to discriminate against what people or groups may make use of the program.
•
Not to discriminate against how and for what purposes the program will be used.
•
Not to place any additional license on the receiver of a copy of the program.
•
Not to have a license that makes it specific to a single project or product.
•
Not to have a license that places restrictions on other software.
•
Have a license that is technologically neutral.
Hence, FOSS can be defined as software where the developers gain free access to the
software, understand how it works, adapt and improve the code according to specific needs
3
and redistribute code to other users. In simple terms, the source code is available for modification and redistribution
by the general public. The main advantages of using FOSS in the development of WebGIS are the absence of license
fees, vendor independence, access to source code, permission to modify and redistribute the code.
The growing trends of the adoption of open source GIS tools and technologies for Geographic
Information System (GIS) is largely due to the fact that many successful open source software projects have
proven record of performance at acceptable level and sometimes even at exceptional levels. The trend can be
seen more and more through Government and Private organizations support for open source projects and
widespread adoption of open source technologies.
Web based Geospatial Information System - Architecture
The basic architecture of Geospatial Information system is shown in Figure 2. WebGIS can be built using
open source software, proprietary (license required) software, or a combination of the two. Open source GIS
software is rapidly improving and, in most cases, can provide a robust alternative to proprietary software.
The GIS architecture, as shown in Figure 2, basically consists of four major components, which are
described hereunder:
User interface
•
Web browsers: Increasingly popular choice for interaction with the GIS.
•
Desktop software: Used for complex spatial data manipulation and visualization tasks with direct connection
to the GIS server.
•
Mobile devices: Support one-way and two-way data replication tasks.
Web application server
•
HTTP server: The Web server that processes the HTTP requests.
•
Application server: Contains the Web application and supports client-side APIs (such as JavaScript) and
server side logic (such as servlets,
Enterprise JavaBeans (EJBs)) to invoke GIS
server tasks.
• Database connection: Java Database
Connectivity (JDBC) or Open Database
Connectivity (ODBC) API to connect to the
database.
GIS Server
• Provides visualization, spatial data
analysis, mapping, and spatial data
management services.
• Supports complex workflow activities,
including versioning.
Database
• The database server stores the spatial
and non-spatial data and provides
Fig. 2: GIS architecture (adopted from Scott Crowther et al., 2008)
varieties of data management tools.
2 0 1 2
The architecture in Figure 2 is a standard 3-tier architecture, where user interface, middleware
J U N E
(Web application server and GIS server), and database components are modularized.
Open Geospatial Consortium (OGC)
The Open Geospatial Consortium (OGC) is a non-profit, international industry
-
consortium of about 440 companies, government agencies and universities that develop publicly
available interface standards (www.opengeospatial.org). These standards support interoperable
B U L L E T I N
solutions that "geo-enable" the web, wireless and location-based services and mainstream IT.
In general, these standards describe communication protocols between data servers, servers
that provide spatial services, and the client software, which request and display spatial data.
In addition, they define a format for the transmission of spatial data. Some of the important
OGC standards that are required in the development of geospatial and location based
•
N N R M S
services include:
OGC data delivery standards: Web Map Service (WMS), Web Feature Service (WFS), Web
Map Tile Service (WMTS), Web Feature Service -Transactional (WFS-T), and Web Coverage
Service (WCS)
•
OGC data format standards: Simple Feature Specification (SFS), Geography Markup
Language (GML), Keyhole Markup Language (KML)
•
OGC data search standards: Catalogue Service (CSW), Gazetteer Service (WFS-G)
•
Other OGC standards: Web Processing Service (WPS), Coordinate Transformation Service
(CTS), Web Terrain Service (WTS), Styled Layer Descriptor (SLD), Symbology Encoding (SE),
Web Map Context (WMC).
FOSS Tools for WebGIS
WebGIS holds the potential to make distributed geographic information available to
a very large worldwide audience. Users will be able to access GIS applications from their
browsers without having proprietary GIS software in their desktops. The Internet and the
World Wide Web have been widely recognized as an important means to disseminate
information. It has increasingly been recognized that future developments in GIS will center on
WebGIS (Caldeweyher et al., 2007), accessing geospatial data and conducting geospatial
analyses on the Internet. This trend has emerged to overcome several limitations of popular
desktop GIS software packages.
Some of the important softwares for various components of Web based Geospatial
Information Systems are discussed hereunder:
i.
Database
A database is an organized collection of data for multiple purposes, usually in digital
form. The term database implies that the data is managed to certain level of quality, measured
in terms of accuracy, availability, usability, and resilience. This in turn often implies the use of
a general-purpose Database Management System (DBMS). A successful general-purpose DBMS
is designed in such a way that it can satisfy many different applications. Relational Database
Management Systems (RDBMS) are used to store and manage huge volume of data, wherein
data is stored into different tables and relations are established between them using primary
5
keys or foreign keys. DBMSs are packaged as computer software products and some of the well-known products
include the Oracle DBMS, Microsoft Access and SQL Server and the Open source DBMS like MySQL, PostgreSQL,
etc. PostGIS adds the spatial support to the well known PostgreSQL relational database. (Rinaudo et al., 2007).
ii.
Web Server
The primary function of web server is to deliver web pages on request to clients. This means delivery of
HTML documents and any additional content that may be included in the document, such as images, style sheets
and scripts. User initiates the communication by making a request for a specific resource using HTTP through
web browser and the server responds with the content of that resource. Many generic web servers also support
server-side scripting, e.g., Apache HTTP Server.
iii. Application Server
An application server is a software framework that provides an environment where applications can run.
It is dedicated to the efficient execution of procedures (programs, routines, scripts) for supporting the construction
of applications. An application server acts as a set of components accessible to the software developer through
an API defined by the platform. For web applications, these components are usually performed in the same
machine where the web server is running, and their main job is to support the construction of dynamic pages.
Some of the popular application server software are described hereunder:
•
MapServer
MapServer is probably the oldest and the most popular open source Internet Map Server. The platform
was originally developed at the University of Minnesota in 1994 with NASA funding (www.mapserver.org).
MapServer is a CGI (Common Gateway Interface) program. CGI is an early Internet GIS technology. MapServer
is now a project of OSGeo (Open Source Geospatial Foundation). It provides cross platform support for various
OGC standards like WMS, WFS, WCS, SLD, GML, etc. It also supports popular scripting and development
environments like PHP, Python, Perl, Ruby, Java, and .NET. A multitude of raster and vector data formats like TIFF/
GeoTIFF, ESRI Shapefiles, ESRI ArcSDE, Oracle Spatial, MySQL are supported by the MapServer.
In order to install MapServer package, OSGeo4W, the new Windows installer may be downloaded from
http://download.osgeo.org/osgeo4w/osgeo4w-setup.exe and installed along with Apache web server and
configured. Now, using PHP or Java or .Net, the developer can customize his own application.
In addition, lot of applications
packages for MS4W is also available with
rich functionalities. One such example is
Ka-Map. It is an open source project that
is aimed at providing a JavaScript API for
developing highly interactive web
mapping interfaces using features
available in modern web browsers. KaMap can also be downloaded from the
same URL under applications packaged for
MS4W called “Ka-Map Java Script API”
and integrated with Map Server. Now, we
can add the data into Ka-Map frame and
view it on the browser. Figure-3 shows
Fig. 3: Ka-Map integrated map server showing the sample data
the sample data in Ka-Map frame.
2 0 1 2
•
MapGuide
The open source version of Autodesk's MapGuide software is a web-based platform
J U N E
for developing web mapping applications and services. Autodesk created MapGuide originally
as proprietary software. In 2005, Autodesk released MapGuide-10 as open source and made
the source codes available for open source developers (www.mapguide.org). It features an
-
interactive user interface that includes support for feature selection, property inspection, map
tips, and operations such as buffer, select within, and measure. MapGuide supports most
B U L L E T I N
geospatial file formats and standards and can be deployed on Linux or Windows using Apache
or IIS respectively. It also supports extensive APIs for PHP, .NET, Java and JavaScript to allow
applications to be built around it. MapGuide Open Source can provide a very powerful map
engine and advanced client-side map browser tools and technologies (such as AJAX viewer and
vector-based DWF viewer). MapGuide Maestro is an open source GUI tool that can ease the
N N R M S
management of spatial data in MapGuide Open Source.
The bundled package for MapGuide and MapGuide Maestro can be downloaded and
installed from URLs http://mapguide.osgeo.org/downloads and http://trac.osgeo.org/mapguide/wiki/
maestro/ respectively. After configuration of the MapGuide server, five layers namely data, layers,
symbols, maps and layouts can be created under mapguide maestro and data can be added to them.
•
GeoServer
GeoServer is an open source software server written in Java that allows users to share
and edit geospatial data (www.geoserver.org). GeoServer can provide advanced Web mapping
protocols such as OGC's WMS and WFS. GeoServer allows data to be published as maps or
images through WMS or actual geographic features through WFS. Using the WFS-T standard,
GeoServer even allows for the insertion, deletion and updation of data. Output map formats
include JPEG, PNG, SVG and KML. Vector data can be delivered as GML and ESRI Shapefile.
GeoServer can display data on any of the popular mapping applications such as Google Maps,
Google Earth, Yahoo Maps, and Microsoft Virtual Earth Open Layers. It can also provide
transactional editing. GeoServer relies on GeoTools, an open source (LGPL) Java code library,
which provides standards compliant methods for the manipulation of geospatial data. GeoTools
implements specifications of the Open Geospatial Consortium including Simple Features, Grid
Coverage, Styled Layer Descriptor.
In order to use GeoServer, java
needs to be configured in the server. The
Geoserver package can be downloaded
from http://geoserver.org/display/GEOS/
Download and installed. Then, user can
create a workspace in GeoServer and add
data to the workspace. When a new dataset
is added to GeoServer, it is displayed in
default style. However, the developer can
create his own Styled Layer Descriptor (SLD)
and upload it to the GeoServer to visualize
the layer in a particular style. Figure 4
shows the Custom Styled Layer of the
Fig. 4: Sample data displayed using custom SLD in Geoserver
sample data in GeoServer.
7
•
CartoWeb
CartoWeb is comprehensive and ready-to-use Web-GIS software for building advanced and customized
applications. Developed by Camptocamp SA, it is based on the UMN MapServer engine and is released under the
GNU General Public License (GPL). Written using innovative language, PHP5, CartoWeb is highly modular and
customizable due to its object-oriented architecture (www.cartoweb.org). It runs evenly on Windows or Unix-like
platforms and has powerful features, when associated to PostgreSQL/ PostGIS.
User can download and install
CartoWeb from http://cartoweb.org/
downloads.html, with the demo data and
also plug-in.
After configuration of
Cartoweb, one can upload the Map files
into their project. Figure 5 shows the
sample data displayed using Cartoweb.
Open
Source
Developments at ISRO
GIS
ISRO/ DOS has developed many
Web Enabled Geospatial Information
Systems towards spatially enabling the
Fig. 5: Sample data displayed using CartoWeb
society with data and information. These
can be categorised under five categories
viz., Natural resources data and services, Satellite Data visualisation, Atmospheric, Meteorological and Ocean
related services, Disaster Management Support and Planetary Data Services. While some of them are developed
using proprietary software, there are several developments using Open Source Software. Some of the Geospatial
Information Systems developed in Open Source Environment are described hereunder:
•
NNRMS Portal & Natural Resources Database
Natural Resources Database (NRDB) is the spatial data repository of ISRO having natural resources
data generated under various NNRMS (National Natural Resources Management System) programmes.
The access to the databases is channelised through NNRMS portal (www.nnrms.gov.in). Home Page of the
Portal is shown in Figure 6. NNRMS Portal
enables the users to search for a particular
data and provides the visualization of the
dataset as well as the complete metadata
about the data. NNRMS Portal adopts a
multi-tier architecture and the data server;
application server, map server and web
server are installed and configured in
distributed
network
architecture.
The important open source software
components used in the development
are: PostgreSQL as Database server,
Apache as Web server, Mapguide as
Map server and ASP, PHP, HTML as
programming environments.
Fig. 6: Home page of NNRMS portal
2 0 1 2
•
Bhuvan
Bhuvan is an initiative to showcase unique distinctiveness of Indian imaging
J U N E
capabilities, including a few of the thematic information derived from satellite imageries,
which could be of vital importance to user community specific to Indian region. Bhuvan
showcases IRS (Indian Remote Sensing) satellite imagery on a versatile 2D and 3D viewing
-
environment. It displays satellite images of varying resolution of India’s surface, allowing users
to visualize cities and important places of interest (www.bhuvan.isro.gov.in). The home page of
B U L L E T I N
Bhuvan is shown in Figure 7.
Bhuvan 2D was developed totally
using open source software viz., UMN
MapServer for Raster & Vector data
N N R M S
Serving / WMS, Geoserver for Vector
Serving / WFS, Openlayers for Application/
Frontend,
PostgresSQL for Database,
Apache Tomcat for WebServer and the
customization using Javascript and PHP.
Bhuvan 2D also has the special distinction
being declared as ‘OGC Website of the
Month – Dec 2010’.
ISRO carries out Land Use and
Fig. 7: Home page of Bhuvan portal
Land Cover assessment on 1:250,000 scale
to provide rapid assessment of Landuse
and Land Cover assessment and to understand the intra and inter annual changes. Likewise, at
a lesser frequency, 1:50,000 scale land use and land cover maps are also prepared and hosted
on the web for optimum land use planning and management. The land use and land cover
information, earlier hosted through Bhoosampada, is now hosted through Bhuvan.
•
Indian Forest Fire Response and Assessment System
Indian Forest Fire Response and Assessment System (INFFRAS), under Disaster
Management Support Programme of
ISRO, integrate multi-sensor satellite
data and ground data through spatially
and temporally explicit GIS analysis
frame work. INFFRAS is operational
during forest fire season only i.e.
February to June every year (http:/
applications.nrsc.gov.in:15001/Fire/). The
home page of the portal is shown in
Figure 8. The important software
components in the development of
INFFRAS includes: SQL Server as Database
server, Apache as Web server, UMN
Map Server as Internet Map Server, and
PHP & ASP as programming environment
Fig. 8: Home page of INFFRAS
for customisation
9
•
Indian Bio Resource Network
Indian Bio Resource Network
(IBIN)
is
a
system
of
distributed
databases where data is available as
web services. The outputs of Biodiversity
Characterizations at landscape level, a joint
project of ISRO and Department of
Biotechnology (DBT) are major inputs for
IBIN spatial node. The spatial data is
available as OGC compliant Web Map
Service (http://www.ibin.co.in) and the
home page of the portal is shown in
Figure 9. The important open source
software in the development of IBIN are:
Postgre SQL as Database server, POSTGIS
as Spatial Database Engine, Apache as Web
server, UMN Map Server as Internet Map
Server and PHP for customisation.
•
ISRO Data Portal
ISRO Data portal is an effort to
Fig. 9: Home page of IBIN portal
harmonize various geospatial and data
services efforts of IRSO/ DOS (http://
dataportal.isro.gov.in). It provides a well
orchestrated data and services to the user
community from one common frontend.
It helps to discover geospatial data &
services of ISRO and provides information
on Satellite Images, Land & Water, Ocean
& Meteorology, Disaster services and
Planetary Science. Figure 10 shows the
home page of ISRO data portal.
Conclusion
Through this article, an attempt
is made to highlight the various options
for development of open source software
based
Geospatial
information
development through examples from
ISRO/ Department of Space. Web based
Geospatial Information systems are
becoming most popular tools for various
aspects of decision making in the
government,
governmental
Fig. 10: Home page of ISRO data portal
private
and
organisations.
nonThe
2 0 1 2
advantages of open source geospatial solution is in quick implementation, maintenance, online help, multiple developers consortium upgrades, large sized discussion forums and assured
J U N E
source code. However, the important aspect here is to develop trained manpower to maintain
the software, software domain knowledge and art of trouble shooting to achieve best solutions.
-
A wide variety of open source tools are now available for desktop and client-server
solutions, content management, geospatial database management and mobile GIS. Recent
B U L L E T I N
years have seen FOSS becoming more mature, interoperable and user friendly. The Open Source
tools offer many interesting alternatives, such as, sharing software solutions, customisation by
exploring newer solutions through web-based developer community and a host of other parallel
possibilities due to worldwide contributors. Continuous improvements and innovations in GIS
technology, ensures better solutions, database management solution as well as innovations in
N N R M S
visualization and geospatial operations. With the open source software solution becoming
more popular, there is ample scope for many users to adopt and use for their domain specific
applications in a cost effective manner.
Acknowledgement
The authors would like to thank all the contributors and the inputs provided by the
designers of various Geoinformation portals of ISRO, Dept. of Space.
This has enabled
consolidation of various open source development activities.
References
Chang, Kang-tsung, (2006). Introduction to geographic information systems,
Tata McGraw-Hill.
Caldeweyher, D., Zhang, J. and Pham, B. (2007). OpenGIS - Open Source GIS-based web
community information system, International Journal of Geographical Information Science,
20:8, 885 - 898.
Ghosh, R.A., Rudiger Glott, Kreiger, B., Gregario Robles (2002). The Free/Libre and Open Source
Software Survey and Study—FLOSS Final Report. International Institute of Infonomics, University
of Maastricht, Maastricht, The Netherlands.
GIS Institute, (2007). WebGIS Manual
Paul Bolstad (2008). “GIS Fundamentals”, Third Edition, Atlas Books
Rinaudo F, Agosto E, Ardissone P, (2007). GIS and Web-GIS, commercial and open source
platforms: general rules for cultural heritage documentation, XXI International CIPA Symposium,
01-06 Athens, Greece.
Scott Crowther, Abe Guerra, George Raber, Angel Tomala Reyes and Murali Vridhachalam
(2008). www.ibm.com/developerworks.
Sutton T, Dassau O, Sutton M (2009). A Gentle Introduction to GIS.
11
RADAR IMAGING SATELLITE (RISAT-1)
LAUNCHED
The Polar Satellite Launch Vehicle in
its 21st flight (PSLV -C19), launched
the Radar Imaging Satellite (RISAT-1)
satellite into a circular polar sunsynchronous orbit at an altitude of 480
km on April 26, 2012 at 05:47 am.
The satellite has now been placed in
its final Polar Sun-synchronous Orbit
of 536 km height and is providing
high quality data.
RISAT-1 is a state of the art Microwave
Remote Sensing Satellite carrying a
Synthetic Aperture Radar (SAR) payload
operating in C-band (5.35 Ghz), which
enables imaging of the earth surface
features under all weather conditions.
RISAT-1
image showing part of Mumbai (04 May 2012)
RISAT-1 provides cloud penetration
and dawn-dusk imaging capability.
These unique characteristics of C-band
Synthetic Aperture Radar enables applications in agriculture, particularly paddy monitoring in kharif
season and management of natural disasters like flood and cyclone.
Introduction
The National Natural Resources Management System (NNRMS) supports the
national requirements of natural resources management and developmental needs
by generating a proper and systematic inventory of natural resources. In doing so,
NNRMS adopts various advanced technologies of satellite and aerial remote sensing;
Geographical Information Systems (GIS); precise Positioning Systems; database and
networking infrastructure and advanced ground-based survey techniques. NNRMS
standards have been adopted to enable technologies – imaging, GIS, GPS and
applications – thematic mapping, services and outputs etc., to work together. Standards
are important not only to facilitate data sharing and increase interoperability, as is
understood from many international efforts, but also to bring a systematization and
automation into the total NNRMS process of mapping and GIS itself. Towards this the
project has defined standards for all geo-spatial data called NNRMS standards.
Under the NNRMS program, a large volume of spatial and non-spatial data
has been generated. The Natural Resources Data Base (NRDB) is a repository of these
datasets, holding around 1500 thematic maps. The NRDB covers a wide spectrum of
thematic layers dealing with natural resources viz. land use, land cover, soil, soil texture,
soil erosion, reserved and protected forests, forest management boundaries, ground
water potential, drainage, wells, watersheds, surface water, canals, minerals,
geomorphology, structures, slope, lithology, wetlands, wastelands, reservoirs,
desertification status, snow cover, airports, golden quadrilateral, settlements and so
on. It is also planned to augment the repository with datasets from Rajiv Gandhi
National Drinking Water Mission, Kutch development project and city based large scale
mapping data. Most of the spatial data is in vector data format. The entire spatial data
is organized into standard data format as per NRDB data organisation schema and
managed in RDBMS environment.
NRDB is a system of distributed databases where the spatial and non-spatial
database resides at various locations well distributed within the country. However,
the metadata is accessible and sharable from a single portal, the NNRMS portal. The
portal provides a sharable framework with metadata as per NNRMS standards, quality
evaluation procedures and their standardization. The Metadata Explorer utility is
accessible only after a successful login. The login is a process of self-registration to
identify oneself on the portal.
2 0 1 2
J U N E
B U L L E T I N
Pushpalata Shah, Rajendra Gaikwad & NRDB Team
Space Applications Centre (ISRO), Ahmedabad-380115
N N R M S
NRDB – VERSATILE CYBER
INFRASTRUCTURE FOR SPATIAL
DATA REPOSITORY AND
DISSEMINATION
Our country has been keeping pace with technological innovations and the thematic data derived from
remote sensing is widely being used in developmental planning and natural resources management projects.
The NNRMS portal is the gateway to this data. Currently the portal hosts metadata for these layers in a crisp and
synoptic manner along with a feature to interact with the data and have a closer look of the thematic maps. The
data is disseminated through an online data service. This has been established over a countrywide network of
decentralized request processors and distributed delivery points.
NRDB Systems & Network Setup
The NRDB systems are configured in high availability mode and are inter-connected over a complex
network spread across the country. The network has three components; the Internet from where the NNRMS
Portal is hosted, the NRDB private network over which the cluster of systems constituting the Master Data Server
(MDS) are linked and the SPACENET over which the regional nodes (RRSC-South, RRSC-North, RRSC-West, RRSCEast, RRSC-Central, NESAC, NRSC & SAC) are connected (Figure 1). A Toggle Server segregates the Internet and
the NRDB MDS Network physically. At any given point of time, the Toggle server is connected to only one side of
the Network. Another Toggle Server separates the NRDB MDS network and SPACENET.
The SPACENET is used exclusively
for internal routing of product requests
and transmitting status of work order
completion. These are internal to the
process flow and part of the automation
established in request processing and
product delivery. The NRDB network is a
virtual private network different and not
connected with the local network of the
centre. This ensures that the NRDB
network is neither overloaded by the local
area network nor does it get contaminated
by the LAN. The Internet is used for
Fig. 1: NRDB systems & network setup
hosting the products on-line and reaching
to the end users who wish to access and
use the data. Also, all user interaction and
intimation is enabled on the Internet. By
default the web server is placed behind a
standard firewall to keep all intrusions at
bay. The toggle mechanisms are invoked
at well defined time intervals to enable
the process of fetching and pushing
requests and status messages in the form
of xml files. These three exclusive networks
are thereby well defined for functionality
and well segregated for maintaining
the security.
Fig. 2: NRDB data server and geospatial database
The servers are installed as
clusters grouped according to the functionality defined for each of the cluster. The portal cluster hosts the web
based services for metadata searching and viewing. It also supports visualisation of thematic layers with basic
2 0 1 2
functionalities such as pan, zoom and
identify. The web servers are XEON DUAL
J U N E
CPU INTEL type with 4GB Memory
attached with 2TB DAS storage. The Master
Data Server cluster caters to request
-
routing, processing of requests and
houses all the monitoring tools that are
B U L L E T I N
required for managing the pending
requests, network availability, and cluster
availability. The servers are XEON DUAL
CPU INTEL type with 8GB Memory
attached with 10TB storage. This storage
N N R M S
is used for housing the spatial database.
Fig. 3: NRDB overall setup
Figure 2 gives a pictorial depiction of the
portal cluster, master data server cluster including the storage. Similar configuration servers
are setup at regional nodes with a scaled down data storage capacity of 2TB. The total setup
is depicted in Figure 3.
Database Organisation
The spatial layers generated under NNRMS programme are stored in a geo database.
The commercial package of ArcGIS 9.2 and ARCSDE with ORACLE 10g, as backend RDBMS,
has been used for organising the database. This database is referred as Natural Resources
Database (NRDB). The NRDB contents adhere to a naming convention and category coding as
specified in the NNRMS standards document [2]. Naming convention for a subset of the layers
is given in table 1.
Table1: NRDB contents with category coding and naming convention
Sr. No.
Layer
Category
NNRMS Layer Name
Project
Code
1
Village boundaries
01
Village50CENSUSYear
2
Canal
03
Canal50NUISYear
3
Drainage
03
Drainage50NRISYear
4
Rail
02
Rail50NRISYear
5
Roads
02
Road50NRISYear
6
Settlement
17
Settle50CENSUSYear
7
Watershed boundaries
03
Wshed50AISLUS
8
Geomorphology
04
Geom50NRCYear
9
Landuse /Landcover
06
Luse50NRISYear
10
Lithology
04
Litho50NRISYear
•••
•••
•••
•••
NRIS
•••
To simplify the operational activity of organising this huge volume and varied data, a
software tool has been developed in Visual Basic using the object library of ArcGIS 9.2. The
data received from various projects under NNRMS are in ARC Coverage format. Mandatory
inputs required for the software are state name, project name, scale of the data and year of
data creation.
15
Metadata Generation
To simplify and systematize the task of metadata generation, a tool has been developed for handling the
project level and layer level metadata. The project level information along with the project data is stored in an
‘xml’ file through software with manual entries. The entries have to be made at the project level as well as each
layer level. In the second stage, the metadata (describing the spatial features as well as the bit maps) is generated
through another package, which reads the data directly from the geo database and stores all metadata entries
in Oracle. Ingesting the metadata from these xml files along with generation of bit maps and extraction of
metadata related to the layer (e.g. feature type, number of features etc.,) and storing in Oracle DBMS is done
through a customized package, ‘metadata.exe’.
Process Flow
User can browse for the spatial data in the NNRMS portal with simple login procedure. The user is
provided with metadata of the spatial data as well as the view of the spatial data. User can then place the
request for data using the ‘add-to-cart’ feature. The details of the request are stored in an ‘nnn.xml’ file and
email is sent to the user with his request-id number (nrdb_nnn) and confirmation of request received. Automated
script on the Toggle server pulls the request (nnn.xml) file and places it locally. Next, the Toggle server disconnects
from the Internet, connects to MDS and pushes the file to the MDS server.
The
background
process
‘onlinexmltooracle.exe’ on MDS reads this
‘nnn.xml’ file, inserts an encryption key
into the ‘nnn.xml’ file and stores the
contents in the table ‘product_catalogue’
in Oracle. It also does the routing of the
request to regional nodes.
Another background process
‘extract.exe’, looks for requests to be
processed in the ‘product_catalogue’
table. The process picks up such products
sequentially and does the job of
extraction from the geo database. The
product is zipped and encrypted. The
encrypted products are pulled by the
toggle server between MDS and Portal
and pushed to the ftp server (ftp://
w w w. n n r m s . g o v. i n / n r d b f t p s i t e / ) .
Subsequently, an email is sent to the end
user with details of the ftp site, the
decryption key and the metadata of the
product. Similar processes are setup at
regional nodes for processing requests
and generating products and emailing to
user about product availability. Status codes
are maintained in the ‘product catalogue’
table at each stage of the process flow. The
Fig. 4: NNRMS - NRDB process flow
total process flow is depicted in Figure 4.
2 0 1 2
Application Potential / Benefits of NRDB Datasets
The NRDB datasets have a very good potential of use in applications of agriculture,
J U N E
cartography, rural development, Oceanography and Meteorology, Bio Resources, Geology and
mineral Resources, Water Resources, Urban Planning and so on. Such data is required for
cropping system analysis, soil mapping, forest type mapping, bio diversity characterization, bio
-
resources assessment, wasteland mapping, environmental impact analysis, topographic map
updation, generation of digital elevation models, ground water prospects zonation and various
B U L L E T I N
time-series analysis.
Challenges
Creating thematic datasets at the country level covering the vast scope of remote
sensing applications requires subject experts in multiple fields like geology, soil, forests,
N N R M S
wetlands, landuse, agriculture, etc. Again, these subject experts have to be supported with
technology and computer savvy personnel for converting the domain expertise into computer
recognizable language.
When datasets are created by multiple agencies, it becomes very essential to develop
automated algorithms and tools for checking whether the data is as per the defined standards
in terms of data content and data structure as well as identifying missing elements. Further,
the workflows involved in data correction (e.g. seam removal, mosaicing, etc.,) have to be
computerized and automated to handle large volumes of data. Secondly, all the data has to be
brought to a common framework, datum as well as projection system. This has to be done in
a systematic and precise manner. Finally, well defined quality checking procedures have to be
set up before releasing the data for public use. QC rejected data needs to be sent back to the
data creator for rectification.
The most pressing challenges for development of the NRDB-NNRMS setup are summarized
hereunder:
•
Standardization of thematic data content, structure as well as symbology for all categories
in various levels of classification. The standards that are evolved are now being referred as
NNRMS standards.
•
Understanding and adopting geo-database technology for a structured and well defined
data organisation mechanism
•
Setting up protocols for exchange of information through organizational and countrywide
network as well as establishing a decentralized request processing in INTRANET and product
dissemination over INTERNET.
•
Setting up auto fail over mechanisms for the web site as well as the data services.
•
Providing the data and metadata services in a secured manner.
•
Maintaining the cyber infrastructure for smooth operations
•
Regulating and synchronizing the database content uniformly across all regional nodes
and central master server.
17
Conclusion
NRDB is primarily aimed at creating and maintaining a systematic archive of all the digital spatial data
holdings of thematic and base maps generated using remote sensing images and promote/ encourage
its use for government, business and societal needs. NRDB consists of various thematic layers generated
under NNRMS programmes tied through common standards and accessible through a common search engine
over a secured network. The access to these databases are channelized through NNRMS portal. The paper
highlighted the role of cyber-infrastructure to realize the spatial data infrastructure of NNRMS Programmes. The
data from NRDB is being used for various planning and developmental activities, especially for management of
natural resources, disaster management, rural development, land use planning, etc., by government and nongovernment organizations.
References
www.isro.gov.in
www.nnrms.gov.in
2 0 1 2
J U N E
-
BHUVAN – GATEWAY TO INDIAN
EARTH OBSERVATION DATA
PRODUCTS AND SERVICES
Introduction
Bhuvan (the name is derived from the Sanskrit word, which means Earth), a
Geoportal of ISRO and Gateway to Indian Earth Observation Data Products and Services
(http://bhuvan.nrsc.gov.in), is an initiative of Indian Space Research Organisation (ISRO),
Department of Space, Government of India. This is to evince the Indian Earth
Observation capabilities from the Indian Remote Sensing (IRS) series of satellites. The
images showcased on Bhuvan are from multi-sensor, multi-platform and multitemporal domains with capabilities to overlay thematic information, derived from
such imagery as vector layers, on a virtual globe for the benefit of user community.
All the Ministries involved in managing natural resources in the country at
different levels can take benefit of Bhuvan. This one-stop versatile Earth browser can
be of vital use for planners, decision makers, social groups, village communities and
individuals. Bhuvan provides a gateway to explore and discover the virtual Earth in 2Dimensional
and
3-Dimensional
space
with
many
new
possibilities
for value addition at the user end. Towards its enhanced outreach and usage, Bhuvan
is available in three Indian languages too, that is, Hindi, Tamil & Telugu apart from
English. There are plans to make it available in other Indian languages in the near
future (Figure-1).
Apart from its unique visualization capabilities, Bhuvan also facilitates the
users to download the satellite data and products through NRSC Open EO Data
Archive (NOEDA), consume thematic datasets as OGC web services through Bhuvan
and plans are outlined for dynamic user interface for user-data-input, etc. Bhuvan's
versatile tools support development of interactive applications for visualisation,
querying, analysis, applications customisation, and as a browser for participatory
data sharing/ analysis. These capabilities make Bhuvan a unique Gateway to Indian
EO Data Products and Services. Bhuvan has also been recognized by OGC as website
of the month in December, 2010 (http://www.opengeospatial.org/pressroom/
newsletters/201012/#C4).
Significance of Bhuvan
Bhuvan allows scientists, academicians, policy makers, or general public to
N N R M S
B U L L E T I N
Team Bhuvan
National Remote Sensing Centre (NRSC), ISRO, Hyderabad-500625
E-mail: [email protected]
leverage the integration of vast amounts
of geospatial data in an easy-to-use
interface without any additional resources.
The unique features of Bhuvan are:
Availability of uniform high
resolution data (6m for the entire Indian
territory), multi-sensor, multi-temporal,
multi-platform data from IRS series of
satellites,
thematic
information,
Automatic Weather Stations (AWS), Ocean
Services (Potential Fish-catch Zones (PFZ)
information for fishermen community
provided by INCOIS, MoES), Disaster
Services,
Collaboration/
Sharing/
Community Participation (Volunteered
Geographic Information), OGC Web
Services towards Interoperability Online
GIS (Shape file Creation - facilitates the
users to download the delineated/
interpreted content using Bhuvan satellite
data as a shape file), Urban Design Tools
Fig. 1: Multi-lingual Bhuvan
(to build roads, junctions and traffic lights
in an urban setting), Terrain Profile
(displays the terrain elevation profile along a path), Mobile Compatibility (supports Android, Symbian, iOS and
Windows Operating Systems), WMS Manager, Multi-Lingual (EN | HI | TA | TE ) interface and Data Download
(CartoDEM, Ortho rectified AWiFS (56m) and LISS III(24m) data).
Components of Bhuvan
Initial version of Bhuvan was launched on August 12, 2009 and since then, it has taken many steps
forward to reach users with wide range of services and applications. In this time frame, two more versions were
released with several advanced features and now it’s moving towards its fourth release.
Milestones of Bhuvan during the period 2009 -2011:
•
Release 1 – August 12, 2009 - Bhuvan 3D
•
Release 2 – August 14, 2010 – Bhuvan 3D with enhanced functionalities and indigenously developed
Bhuvan 2D
•
Release 3 – April 29, 2011 – Bhuvan with Multilingual Support (4 languages-English, Hindi, Tamil & Telugu)
and enriched features both in 2D and 3D
•
Mobile Version – July 15, 2011
•
High resolution data with the release of RSDP-2011, Bhuvan Discussion Forum, Single Sign-on (SSO) August 12, 2011
•
NRSC Open EO Data Archive (NOEDA) – September 28, 2011 – to provide download of CartoDEM and
AWiFS data at free of cost.
2 0 1 2
• Bhuvan – Thematic Services – December
17, 2011 – to provide thematic datasets
J U N E
as OGC web services to the users towards
interoperability
• Addition of 24m Ortho rectified LISS III
-
data in NOEDA – January 03, 2012
B U L L E T I N
Bhuvan 3D
Bhuvan 3D showcases images in
a multi-sensor, multi-platform and a
multitemporal domain. It lets the user to
Fig. 2: Bhuvan 3D
N N R M S
access, explore and visualise IRS imagery
and a bundle of rich thematic information in 3D landscape. On Bhuvan 3D, users can fly to
different locations on the terrain and experience interesting 3D navigation (Figure 2).
3D Bhuvan has many unique features and is an easy-to-use intuitive interface, where
users can virtually experience the physical characteristics of the terrain, especially the Indian
landscapes. The urban design tools are a magic galore. Here the user can virtually build roads,
junctions and traffic lights in an urban setting. At the moment, Bhuvan 3D requires a 10 MB
plug-in download and is compatible with windows environment.
Bhuvan 2D
In order to meet the requirement of large number of people who need a lighter,
platform independent and simpler browser-based version, Bhuvan-2D was evolved. Development
is carried out using the very robust Open Source Geospatial solutions like UMN MapServer,
GeoServer and POSTGIS with Postgres to organize the satellite imageries and map data along
with myriad information, with no server side dependencies. PHP (widely-used general-purpose
scripting language) and OpenLayers (Open Source javascript library) are also used for making it
more dynamic, interactive and rich in Web application. Thus, the entire development and
deployment of Bhuvan 2D is accomplished using open source solution (Figure 3).
Some of its functional capabilities include map navigation, map panning, and overview
map, drawing line, point & polygon on
the map, linear and area measurement etc.
Also tools providing capabilities of GeoProcessing, GetCapabilities, online shape
file creation & download, simple and
combo graph generation using archived
weather data, Potential Fishing Zone
Information, mailing current location to
other users & allowing Bhuvan imagery to
embed into third party’s web pages
towards the benefit of the common man
Fig. 3: Bhuvan 2D
are being provided. Users can use both
21
2D
and
3D
version
of
Bhuvan
complementing each other with better
benefits and usability.
Pocket Bhuvan (Mobile Version
of Bhuvan)
Mobile
browsing
generally
demands a different method of map
navigation.
OpenLayers
built-in
‘Navigation and TouchNavigation’ controls
are used to handle this appropriately.
Supported Touch events are Map
Dragging
(touchstart/
touchmove/
touchend), Pinch Zoom (multiple touch
events), Tap Panning (support touch
Fig. 4: Pocket Bhuvan
browsers which do not support touch
events).
In Pocket Bhuvan (Figure 4), Zoom In, Zoom Out, ZoomBox, ZoomBoxOut, Navigationmap (Rediffmap as
transparent overlay layer) and search function are presently available. Users can access Pocket Bhuvan by visiting
http://bhuvan.nrsc.gov.in in their mobile and reach Pocket Bhuvan 2D using ‘Enter Bhuvan’ option. The
application checks the http user agent
(computer
or
a
mobile)
and
redirects automatically to Bhuvan or
Pocket Bhuvan based on the http user
agent. This mobile Bhuvan development
and deployment has been realized using
Open source Drupal Content Management
System (CMS) and OpenLayers controls.
NRSC Open EO Data Archive
(Bhuvan-NOEDA)
A new initiative of NRSC/ISRO to
make available the satellite data and
products coarser than 24 m is realized
Fig. 5: NRSC Open EO Data Archive (NOEDA)
using Bhuvan 2D solutions because of its
open standard, modular nature and its
components reusability.
NOEDA facilitates the users to select, browse and download satellite data and products. Through
NOEDA, Bhuvan has laid a step forward in serving much required data to the Scientific and Research community
(Figure 5). At present DEM derived from Cartosat-1 of 1 arc sec, Resourcesat-1 AWiFS Orthoimages (2008-2009)
at 56 m resolution, Resourcesat-1 LISS-III Orthoimages (2008-09) at 24 m resolution for Indian region are
available for download. It gives the option to select the area based on bounding box, Mapsheet (SOI), Tiles, and
2 0 1 2
Interactive Drawing.
Users can see
thumbnail view, metadata (as per
J U N E
NSDI 2.0 standards) and download the
selected tiles.
Bhuvan-Thematic
-
Bhuvan – Thematic Services
services
B U L L E T I N
facilitate the users to select, browse and
query the thematic datasets. Users can
consume these thematic datasets and
integrate into their systems as OGC web
N N R M S
services. Presently Land Use and Land
Fig. 6: Bhuvan-Thematic Services
Cover (50K):2005-06 datasets are
available and it is planned to extend the
services for other themes like land degradation, soil, etc (Figure 6).
It has the options of getting state and district wise statistics, Area of Interest (AOI) based
analysis, URL for WMS/WMTS services, adding external WMS layers and printing based on view. It
is another example of versatility of Bhuvan 2D components. The OGC web services are realized
for interoperability and it is planned to extend them as online Geoprocessing services.
Bhuvan – online Discussion Forum
ISO has defined user experience as “a person's perceptions and responses that result
from the use or anticipated use of a product, system or service". So, user experience is subjective
and focuses on the use. Since its launch, Bhuvan has always strived to reach its users through
rich data and applications, addressing the users’ requirements and queries through mails,
forums, feedbacks, surveys.
To capture all kinds of user experience, address various needs and requirements, share
ideas and post case studies, a dedicated online discussion forum has been evolved using
phpBB, an open source bulletin board.
Various Bhuvan components and features can be accessed by visiting
www.bhuvan.nrsc.gov.in. It is an open visualisation system.
Registration in Bhuvan
is optional. Users can use the Central Authentication Service of Bhuvan for creating an account
on Bhuvan towards Single Sign-On (SSO) developed using open source Java server component
(Jasig). However, some features require registration. Registered users are having privilege to
share and download the data, collaborate with other users, participate in discussions forums etc.
Datasets Available in Bhuvan
Huge volume of multi-temporal Geospatial datasets (Raster and Vector) along with
non-spatial datasets are created and organized on Bhuvan to facilitate this network based
applications development and deployment. The spatial resolutions of raster datasets stored in
Bhuvan varies from 360m to 1m and vector data scale varies from 1:250000 to 1:50000. The
spatial data layers available in Bhuvan are given in the following sub-sections:
23
Raster Datasets
Satellite Imagery
Satellite/Sensor
Spatial Resolution (m)
Oceasnsat-1 OCM
360
Oceasnsat-2 OCM
180
Resourcesat-1 AWiFS
56
Resourcesat-1 LISS III
24
Resourcesat-1 LISS IV Mx
5.8
Casrtosat-1 PAN Merged with Resourcesat-1/2 LISS IV Mx
2.5
Casrtosat-2 PAN Merged with Resourcesat-1/2 LISS IV Mx
1
Thematic Layers
Layer Name
Scale / Spatial Resolution
Land Use/Land Cover
1:250000
NADAMS – NDVI India Mosaic (Year 2002, 2008 & 2009)
56 m
Flood – Krishna, Kosi and Bihar
1:50000
Chlorophyll
1 km
Sea Surface Temperature
1 km
Vector Datasets
Thematic Layers
Theme
Scale
Wasteland
1:50000
Soil
1:50000
Ground Water Prospects
1:50000
Watershed
1:50000
Land Use/Land Cover
1:50000
Base layers
Layer Name
Scale
Administrative Boundary(Country, State, District, Taluk
and Village with Census 2001 information)
1:250000
Towns
1:250000
River
1:250000
Reservoir
1:250000
National Highway
1:250000
Golden Quadrilateral
1:250000
2 0 1 2
•
Weather Information from ISRO’s Automatic Weather Stations (AWS)
•
Potential Fishing Zone Information from Indian National Centre for Ocean Information
J U N E
Other Datasets/Information
Services (INCOIS)
-
• Forest Fire alerts from the Indian Forest
Fire Response and Assessment System
B U L L E T I N
(INFFRAS)
• User shared data(Point of Interest/
Attribute) through crowdsourcing
• Navigation map integration from
N N R M S
Rediffmap
All these datasets are grouped as
services in Bhuvan like Land services,
Fig. 7: Land services (e.g. groundwater prospects map)
Weather services, Ocean services,
Disaster services and Collaboration
services (Figures 7 to 11) to cater to the
scientific community, planners and
administrators for their needs towards
societal good.
Use Cases
Since its advent, Bhuvan is being
referred in several forums, journals for its
usage. Few of the areas where Bhuvan has
been used are: APNIC showcasing school
information using Bhuvan (Sarva Siksha
Fig. 8: Weather services (e.g. temperature, humidity etc)
Abhiyan), India Geoportal towards
National Spatial Data Infrastructure,
INFFRAS - Dissemination and Visualization
of Forest Fire alerts through Bhuvan, MP
Forest Department - Visualization of
information related to Forestry using
Bhuvan
(Prototype
developed),
Rajiv Awas Yojana (RAY) - Technology
demonstrated to NGO (SPARC, Mumbai)
on how to use Bhuvan for delineating
the Slum Boundaries and visualizing
them on Bhuvan. Researchers using
Bhuvan data for various scientific studies
Fig. 9: Ocean services (e.g. potential fishing zone, CHL, SST)
like ‘landslide study’ reported in Current
25
Science, Vol. 98, No. 7, 10 April 2010,
Usage of Bhuvan in ‘Microbiology of
Mangroves’ communicated by author
(Mr.
S.
Gopinath,
University,
Bharathidasan
Tiruchirappalli) to Aquatic
Journal, Usage of Bhuvan in Educational
book covering Himalaya (glaciers and
mountains) and Ganga
river from a
California based charitable non-profit
organization - ‘Self Enquiry Life Fellowship
collaborating with a Varanasi based Trust:
Fig. 10: Services (e.g. flood layer with village boundary)
Vedanidhi Charitable Trust’.
Freely available elevation data
and ortho corrected satellite data
through NOEDA facilitates the student
community, researchers and other
users for various applications. More
than 30,000 data sets have been
downloaded during past few months.
Online
shape
file
creation
utility
available on Bhuvan provides the platform
to the users with multi-temporal data for
various types of resource mapping of
users’ choice.
Future Plans
Many
more
value
added
functions and features are envisaged
Fig. 11: Collaboration services (add content, communities, developer API)
under Bhuvan, which will be added
from time to time. The basic objective
of
Bhuvan
is
to
provide
such
functionalities to engage users in participatory data creation, coupled with tools for scientists to solve simple
problems easily and interactively. To state few of important functions/ features that are planned in
near future are: Dynamic User-data Input for crowd sourcing with necessary validation (through
a volunteered approach-Trusted Users), Routing for Navigation (Proximity analysis), Online Geoprocessing
towards interoperability as a Web Processing Service (WPS), Uniform High resolution data (2.5m)
for entire country, Distributed Architecture for Bhuvan to improve the user experience, Live video
streaming through Bhuvan for monitoring traffic and surveillance activity, Web Catalogue Service (CSW)
of all Indian Earth Observation datasets for evaluation and exploitation, Robust Bhuvan API for
customized add-ons/ Apps, Robust Earth Observation Data Archive and Dissemination System to
2 0 1 2
access land cover change for local to global systems (Enhanced NOEDA), enhanced Bhuvan
Thematic Services to cater to various themes, customized Bhuvan Mobile apps to cater various
J U N E
user needs towards user centric map applications etc.
-
Online References
http://bhuvan.nrsc.gov.in
B U L L E T I N
http://geoserver.org/
http://httpd.apache.org
http://json.org/
N N R M S
http://mapserver.gis.umn.edu
http://mysql.com
http://nnrms.gov.in
http://opengeospatial.org
http://openlayers.org
http://tilecache.org
http://wikipedia.org
http://w3schools.com
http://www.opengeospatial.org/pressroom/newsletters/201012/#C4
27
ISRO'S CONTRIBUTION IN THE FIELD
OF METEOROLOGICAL AND
OCEANOGRAPHIC STUDIES
Yagna Mankad & Pushpalata Shah
Space Applications Centre (ISRO), Ahmadabad-380015
Introduction
Since ages, scientists have been studying oceanographic and climate patterns to
understand the vagaries of nature. Extreme conditions of climate and drastic variations in
ocean current systems lead to gross destruction of property and human beings. Continuous
studies in ocean and climate sciences have brought the awareness of global warning. This
awareness has now led to strategies and policies and plans for combating global warming
and thereby save Mother Earth. Conclusive results in the fields of climate and oceanography
are possible only when respective parameters are measured from varying platforms, at different
scales and in continuous mode. Many models are built around datasets of almost hundred
years to interpret and understand climatology and oceanography. Hence, it becomes extremely
essential to continuously generate and archive oceanographic and climate related data.
Objectives
The study of Indian climatology and improved forecasting models calls for
an end to end program in terms of satellite based observations as well as ground based
observations. This objective has been achieved in the past two and a half decades through
the Indian National Satellite (INSAT) system carrying many meteorological instruments.
Established in 1983, INSAT system is a joint venture of the Department of Space,
Department of Telecommunications, India Meteorological Department and All India Radio
and Doordarshan. Meteorological data from INSAT is used for weather forecasting and
specially designed disaster warning receivers have been installed in vulnerable coastal areas
for direct transmission of warnings against impending disaster like cyclones. At present,
repetitive and synoptic weather system observations over Indian Ocean from geostationary
orbit are available only from INSAT system. INSAT VHRR data is available in near real-time at
90 Meteorological Data Dissemination Centres (MDDC) in various parts of the country. With
the commissioning of direct satellite service for processed VHRR data, MDDC type of data
can be provided at any location in the country.
The INSAT system has been providing valuable climate data covering the Indian sub
continent. Upper winds, sea surface temperature and precipitation index data are regularly
obtained. The products derived from the image data include cloud motion vectors, sea
surface temperature, outgoing long-wave radiation, quantitative precipitation index, upper
troposphere height, solar insolation etc. These products are used for weather forecasting,
both synoptic as well as in numerical weather prediction. The ground segment is covered
2 0 1 2
through the establishment of unmanned data collection platforms like the Automatic Weather
Stations (AWS). Around 1500 AWS stations are installed across the country. These stations
J U N E
measure surface parameters like temperature, pressure, humidity, wind speed and direction,
sunshine hours and rainfall on an hourly basis. This data is received through satellite link at the
data reception server setup at Space Applications Centre, Bopal. Further dissemination of the
-
weather related information is done by Doordarshan Television Channel by displaying INSAT-
B U L L E T I N
VHRR imageries during news coverage and by newspapers as part of weather reporting.
Study of Oceans was initiated through the OCEANSAT series, with the launch of IRS-P4
(OCEANSAT-1) on May 26, 1999. This satellite carries Ocean Colour Monitor (OCM) and a
Multi - frequency Scanning Microwave Radiometer (MSMR) for oceanographic studies. The
Ocean Colour Monitor (OCM) has a solid state camera operating in eight narrow spectral
N N R M S
bands. The OCM camera is used to collect data on chlorophyll concentration, detect and
monitor phytoplankton blooms and obtain data on atmospheric aerosols and suspended
sediments in the water. The MSMR operates in four microwave frequencies, both in vertical and
horizontal polarisation, is used to collect data on sea surface temperature, wind speed, cloud
water content and water vapour content in the atmosphere above the ocean.
OCEANSAT-2, India’s second satellite in the OCEANSAT series, was launched for the
study of the oceans as well as the interaction of oceans and the atmosphere, to facilitate
climatic studies. Additionally it helps in to charting sea levels, a vital indicator of climate
change, on a globe-circling voyage. The major objectives of OCEANSAT-2 are to study surface
winds and ocean surface strata, observation of chlorophyll concentrations, monitoring of
phytoplankton blooms, study of atmospheric aerosols and suspended sediments in the water.
The data is also used to provide information on Potential Fishing Zones to fishermen.
Birth of MOSDAC
Till 2005, the data received from the above mentioned missions were archived and
disseminated by the identified organisation like IMD for weather data and NRSC for IRS data.
But weather is dynamic in nature and hence the near real time data is very important. For this,
a data centre was envisaged. The Standing Committee on Meteorology (SC-M) of National
Natural Resources Management System (NNRMS) has recommended establishing a data archival
and dissemination system for helping Indian users. Subsequently, Meteorological and
Oceanographic Satellite Data Archival Center (MOSDAC) has been established in March 2006
at Space Applications Center – ISRO, Bopal campus, Ahmedabad, to cater to the needs of
research community in the country from meteorological and oceanographic fields.
The major objectives of MOSDAC are:
•
To acquire and process the data from ISRO science missions
•
To disseminate quality data products from ISRO Science missions for meteorology and
oceanography on near real time basis
•
To promote synergy of different sources of satellite data into a practical and usable data
sets for R & D in atmospheric and oceanic studies
•
To promote the use of satellite data through numerical modeling
•
To ensure long term archival, management and services of all ISRO science missions
products and related information
29
The data are acquired at data reception facility established at SAC – Bopal campus. These data then get
archived at archival and dissemination facility established in the same campus. These data products are
disseminated to the users through its web based service http://www.mosdac.gov.in and ftp://ftp.mosdac.gov.in.
The home page is a store house of a good quantity of weather related information. Actual data sets are made
available only to registered users. The users are classified under various categories. Depending on the policy, the
user has access to specific data sets and can place the request. The requested data is made available on an FTP
server through individual accounts. For launch campaign users and under special observation periods, the data
is made available “on-line” i.e. without any request, the data is made available on FTP server.
MOSDAC Setup
Presently, the setup receives the data in real time from Satellites like Kalpana-1 (K1), INSAT-3A and in-situ
observations like Automatic Weather Stations (AWS) and AgroMet Stations (AMS). These data then get processed
and generated products are put in pre-defined “Prod area”. The interface systems configured under archival
setup pulls the data and archives as per the predefined structure. The archived data is then disseminated to the
registered users as per the policy. The
Figure 1 shows the setup configured for
this and Figure 2 shows the data flow
diagram or user connectivity.
Satellite Based Data Sets
Presently, MOSDAC has data from
Kalpana-1 satellite acquired at every half
an hour, where as INSAT-3A data five or
ten acquisitions per day. Some of these
products are depicted in Figure 3.
Fig. 1: Data acquisition, processing, archival and dissemination setup
A cooperative agreement has been
signed
with
EUMETSAT
for
using
meteorological data from Meteosat-5 at 63
degree East in exchange for weather pictures
collected by INSAT. These data sets are made
available for internal consumption through
MOSDAC. Sample products from EUMETSAT
are shown in Figure 4.
In-situ Data Sets
ISRO has taken up indigenous
development of low cost Automatic
Weather Station (AWS) for deployment in
the country in large numbers. The data
collection is proposed to be carried out in
TDMA mode instead of the present
random access mode. The AWS data are
Fig. 2: Data flow diagram
also received at one hour interval and Agro
Met Station (AMS) data are acquired at
half an hour. Presently, data from about 1500 AWS stations and 24 AMS stations are being received at MOSDAC
Data Reception System (MDRS) – SAC. Data Collection Platforms (DCP) services are provided using the Data Relay
2 0 1 2
Transponders of Kalpana-1 and INSAT-3A.
Additionally, IMD has also installed
J U N E
100 meteorological Data Collection
Platforms (DCPs) and other agencies
have installed about 200 DCPs all over
Asia Visible Linear Stretch
Cloud Motion Wind
-
the country. One DCP is also installed
Globe TIR
at Schiramacher, the Indian base station
B U L L E T I N
in Antarctica.
OCEAN Related Products
Some of the ocean related
products derived from Oceansat-2
Water Vapour Wind
Upper Tropospheric Humidity
satellite are depicted in Figure 5.
N N R M S
Sea Surface Temperature
These products will soon be made
available from MOSDAC.
Information Dissemination
For quick dissemination of
warnings against impending disaster
Outgoing Longwave Radiation
Normalised Diff. Veg. Index
Land Surface Temperature
Fig. 3: Sample products from INSAT-3A / KALPANA
from approaching cyclones, specially
designed receivers have been installed at
the vulnerable coastal areas in Andhra
Pradesh, Tamil Nadu, Orissa, West Bengal
and Gujarat for direct transmission of
warnings to the public in general, and
officials in particular, using broadcast
capability of INSAT. IMD's Area Cyclone
Warning Centres generate special
warning bulletins and transmit them
every hour in local languages to the
Near Real Time Imagery
Visualised Products
RGB Composites
affected areas. There are 350 such
receiver stations installed by IMD in the
Fig. 4: Sample products from EUMETSAT
country, out of which, 100 are Digital
CWDS (DCWDS) based on advanced
technology. The DCWDS has been
deployed
with
acknowledgement
transmitters to get confirmation at
transmitting station.
MOSDAC disseminates weather
related information and alerts through its
web site. It provides weather forecasts for
major cities for 24, 48 and 72 hours as
depicted in Figure 6. Similar forecast is
available for densely populated areas
Hourly Analysed Winds
Fig. 5: Sample products from OCEANSAT-2
12 Hourly Analysed Winds
across the country as shown in Figure 7.
Similarly, the research studies on cyclone
31
formation have resulted in a model for
predicting cyclones and forecasting its
path. This event based information is also
hosted from MOSDAC. A sample forecast
is presented in Figure 8.
Future
Megha-Tropiques,
an
Indo-
French Joint Satellite Mission for studying
the water cycle and energy exchanges in
the tropics, was launched on October 12,
2011. The main objective of this mission
is to understand the life cycle of convective
systems that influence the tropical
weather and climate and their role in
associated energy and moisture budget
of the atmosphere in tropical regions. The
Typical Forecast for Ahmedabad city
Fig. 6: Weather forecast disseminated from MOSDAC
satellite will provide scientific data on the
contribution of the water cycle to the
tropical atmosphere, with information on
condensed water in clouds, water vapour
in the atmosphere, precipitation, and
evaporation. With its circular orbit inclined
20 deg to the equator, the MeghaTropiques is a unique satellite for climate
research that should also aid scientists
seeking to refine prediction models.
The SARAL / AltiKa, another IndoFrench joint satellite mission is planned
for studies in the environment monitoring
domain. This will support measurements
of Ocean Surface Topography, surface
wind speed, and surface wave height. The
Fig. 7: All India weather forecast disseminated from MOSDAC
INSAT3D mission is envisaged to provide
an operational, environmental & storm
warning system to protect life & property
and also to monitor earth’s surface and
carryout oceanic observations.
These missions of ISRO will
provide valuable data to the scientific
community in the field of Oceanography
and Meteorology.
References
www.isro.gov.in
www.mosdac.gov.in
Fig. 8: Cyclone related forecast
www.imd.gov.in
Introduction
In the emerging knowledge society and wide spread use to IT tools in different
sectors, up-to-date information on water resources is the vital to support economic
development, improve the quality of life as well as to conserve the nature and the
environment. In this regard an operational water resources information system at
national level is essential for planning and development of the country.
Decision-making processes in water resources management are characterized
by a typical hierarchical structure and a high degree of complexity (Kaden et al., 1989).
Water resources planning require a multi-disciplinary approach that brings together
a collection of technical tools and expertise along with stakeholders of varied interests
and priorities. Generally, the water management scenario is designed and influenced
by a set of linked physical, biological, and socio-economic factors such as surface
water hydrology, groundwater hydrology, climate, soils, topography, land use, water
quality, ecosystems, demographics, institutional arrangements and infrastructure
(Biswas, 1981; Loucks, 1995;Bouwer, 2000; Zalewski, 2002). An information system
is a set of data and functions, which is developed and deployed to meet the needs of
users. Key functions of an information system are viewing, presentation, interpretation,
analysis and modeling of data (Maidment, 1997).
India-WRIS WebGIS portal aims a ‘Single Window’ solution of all water
resources and related data & information in a standardized GIS format in national
framework to all departments, organizations and stakeholders for water resource
assessment, monitoring, water resource planning, development and integrated water
resources management. It provides comprehensive, authoritative and consistent data
of India's water resources along with allied natural resources data & information, web
enabled tools to search, access, visualize, understand, look into context and study the
spatial patterns. The WebGIS portal is designed and developed, keeping in view multistakeholder users from all sections of society, varied and multi-source data input,
current map policy, requirement of regular updates and near real time data accessibility,
data security domains, scale of information and level of access of the portal as well as
download of different GIS maps, data and value added products along with tool kit
for further analysis and value addition under three user categories namely:
•
All General Users (public domain fast track system)- Users are able to visit web
portal and get the snapshots of the data sets on reduced scale of selected database
and tools.
2 0 1 2
J U N E
B U L L E T I N
Sharma JR, Project Director (India-WRIS) and Project Team
RRSCs / NRSC/ ISRO, Hyderabad-500625
Email: [email protected] & [email protected]
N N R M S
INDIA-WRIS WEBGIS
DESIGN AND DEVELOPMENT OF
WEB ENABLED WATER RESOURCES
INFORMATION SYSTEM OF INDIA
•
Premium Users- Users are able to get the access to the India-WRIS web application detail datasets and tools
by registration and password.
•
CWC Intranet Users - These privileged users are able to get the full access to the India-WRIS web application
and database. All the facilities developed are accessible these users.
The information system on water resources has four key elements besides other facilities namely:
1.
Data input/entry/collection system
2.
Data storage, analysis, and transformation into ‘user friendly’ information
3.
Interactive system for geo-visualization and temporal analysis and
4.
Information dissemination system in public domain as downloads and furtherprocessing tools for value
addition and customization.
Storage, processing, retrieval and dissemination of data constitute the most important aspects as the
water resources management being multi stakeholder’s involvement, people’s participation and information
sharing to increase transparency, public awareness, elevating the importance of water information and enlighten
public involvement in water management. The thrust has been towards development of an open source user end
web enabled information system. It provides adequate and contemporary information on the state of water
resources, which are must for planning and water resources management strategy. This, in turn, will ensure
increase in public awareness about the crucial issues related with water and attract their participation in
management, planning and development of water resources of the nation leading towards the holistic goal of
water security.
Water Wealth of India
Water is one of the most important renewable natural resources for supporting life. With the increasing
population of India as well as its all-round development, the utilization of water is also increasing at a fast pace.
On an average, India receives annual precipitation (including snowfall) of about 4000km3. However, there exist
considerable spatial and temporal variations in the distribution of rainfall and hence in availability of water in
time and space across the country. It is estimated that out of the 4000km3 water, 1869km3 is Average annual
potential flow in rivers available as water resource. Out of this total available water resource, only 1123km3 is
utilizable (690km3 from surface water resources and 433km3 from ground water resources). The water demand
in the year 2000 was 634km3 and it is likely to be 1093 km 3 by the year 2025. Due to rapid rise in population and
growing economy of the country, there will be continuous increase in demand for water, and it will become
scarce in the coming decades. (Table 1)
According to the international norms, a country can be categorized as ‘water stressed’ when water
availability is less than 1700m3 per capita per year whereas classified as ‘water scarce’ if it is less than 1000m3
per capita per year. In India, the availability of surface water in the years 1991 and 2001 were 2309m3 and
1902m 3. However, it has been projected that per capita surface water availability is likely to be reduced to
1401m3 and 1191m3 by the years 2025 and 2050, respectively. The per capita water availability in the year 2010
was 1588m3 against 5200m3 of the year 1951 in the country.
2 0 1 2
:
2.4%
✧
Population as % of World Population
:
17.1%
✧
Water as % of World Water
:
4%
✧
Rank in per capita availability
:
132
✧
Rank in water quality
:
122
Average annual rainfall 1160mm ( world average 1110mm)
•
Range of distribution 150 -11690mm
•
Range Rainy days 5-150, most rain 15 days in 100hrs.
•
Range PET 1500-3500mm
•
Per capita water availability (2010) in m 3-1588
N N R M S
•
Table 1:
India’s water wealth
Sl. No.
1
-
Area of the country as % of World Area
B U L L E T I N
✧
J U N E
Water Resources – India at a Glance
Water Resource at a Glance
Quantity
(km3 )
Percentage
Annual precipitation (Including snowfall)
4000
2
Precipitation during monsoon
3000
75
3
Evaporation + Soil water
2131
53.3
4
Average annual potential flow in rivers
1869
46.7
5
Estimated utilizable water resources
1123
28.1
690
17.3
6
7
Surface water
Replenishable groundwater
Current utilization of total
Current utilization of utilizable water
Storage created of utilizable water
Storage (under construction) of utilizable water
Estimated water need in 2050
Estimated deficit
Interlinking can give us
100
433
10.8
634
15.85
634
56.45
225
20.03
171
15.22
1450
129
327
29
200
17.8
Source: Water Resources at a Glance 2011, CWC, New Delhi, (http://www.cwc.nic.in)
Remote Sensing and GIS in Water Resources Studies
The effective utilization of satellite remote sensing and GIS in water resources
information generation and management can be broadly categorized as follows:
•
Water Resources Assessment – It includes snow & glacier studies, surface water mapping
and monitoring, wetlands mapping, runoff & hydrologic modeling and water balance studies.
•
Water Resources Management – It comprises of irrigation water management, salinity
and waterlogged area mapping & monitoring, monitoring new irrigation potential creation,
evapo-transpiration studies, reservoir management, reservoir sedimentation and catchment
area treatment
•
Water Resources Development – It covers interlinking of rivers, ground water prospecting
and recharge - systematic planning & development.
35
•
Watershed Management – It comprises of water harvesting and soil erosion, watershed management and
sustainable action plans for soil and water conservation.
•
Flood Disaster Support – It includes flood disaster monitoring and management, flood forecasting, river
engineering, urban flood management.
•
Environmental Impact Assessment & Management – It is related to hydro-power development and water
quality studies, identification & mapping of over exploited areas and augmentation of the resource.
Water Resources Management - Challenges / Issues in the Country
The major challenges/issues associated with the water resources management and development in the
country are varied and complex and could be categorized as follows:
a.
Natural situation (Tropical Monsoon climate)– Causes large scale spatial and temporal variation in water
availability, recurring droughts and frequent floods.
b.
Human, Managerial and Developmental challenges – There is increasing water demand and falling per
capita availability, water use and energy efficiency, deterioration of water quality, reduction or deterioration
of available resources (loss of surface storage), increasing competition/conflict within sectors, under and
inefficient utilization of irrigation potential, over exploitation and depletion of ground water resources,
waterlogging and soil salinity in irrigated lands, fragmentation of management of water / management of
shared resources, lack of spatial inventory for large number of water infrastructure in the country, (currently
used water resources potential estimates (CWC: 1988 & 1993 ; NCIWRD:1999 are old), significant change
in land use / land cover, demographic and utilization pattern in past few decades.
c.
Climate change impact –Addressing the impact of climate change on water availability and economy.
Analysis of scenarios for impacts on resources and use is required to evaluate water policies.
The Project India –WRIS
In view of the present status of water resources and growing demands of water for meeting
the requirements of the rapidly increasing population as well as the difficulties that are likely to arise
in future, a holistic, well planned long-term strategy is needed for sustainable water resources management in
India. In order to address challenges, the first requirement is reliable and updated information
system and hence, the project “Generation of Database and Implementation of Web enabled
Water resources Information Systemin the Country” (India-WRISWebGIS) is being jointly executed by ISRO and
Central Water Commission.
The objectives of the project are as follows:
•
To collect available data from varied sources, generate database of country’s water resources, organize in
standardized GIS format and provide a thin client, scalable web-enabled information system.
•
To provide easier and faster access and sharing of nationally consistent and authentic water resources data
through a centralized database and application server to all water resources departments / organizations as
decided by CWC.
•
To provide tools to create value added maps by way of multi-layer stacking of GIS database so as to provide
integrated view to the water resources scenarios.
•
To provide foundation for advanced modeling purposes and future Spatial Decision Support Systems (SDSS)
including automated data collection system.
2 0 1 2
Based on the requirements and data availability, comprehensive information have
been collected, categorized and arranged in GIS environment under 12 major and 30 sub
J U N E
information systems besides sub information system having large number of attributes data of
last 5 – 50 years. (Table 2)
I.
BASE DATA INFORMATION SYSTEM
➢
Administrative, Infrastructure, Terrain (DEM)
II.
SURFACE
1.
2.
3.
4.
5.
6.
7.
8.
9.
WATER INFORMATION SYSTEM
Water Resource Region Information System
Basin Information System
Watershed Information System
River Information System
Surface Water Body
Water Resources Projects Information System
Command Area Information System
Minor Irrigation Information System
Canal Information System
III.
GROUND
10.
11.
12.
WATER INFORMATION SYSTEM
Aquifer / Litholog / Information System
Ground Water Level Information System
Ground Water Potential Information System
IV.
HYDRO –
13.
14.
15.
MET INFORMATION SYSTEM
Meteorological Information System
Climate Information System
Hydro-Observation Information System
V.
WATER QUALITY INFORMATION SYSTEM
16. Surface Water Quality Information System
17. Ground Water Quality Information System
VI.
SNOW COVER / GLACIER INFORMATION SYSTEM
18. Snow Cover/Glacier Information System
VII.
INLAND NAVIGATION WATERWAYS INFORMATION SYSTEM
19. Inland Navigation Waterways Information System
VIII.
INTER-BASIN TRANSFER LINKS INFORMATION SYSTEM
20. Inter-basin Transfer Links Information System
IX.
HYDROLOGICAL EXTREMES INFORMATION SYSTEM
21. Flood Information System
22. Drought Information System
23. Extreme Events Information System
X.
LAND RESOURCES INFORMATION SYSTEM
24. Land Use / Land Cover Information System
25. Land Degradation Information System
26. Wasteland Information System
27. Soil Information System
XI.
WATER TOURISM INFORMATION SYSTEM
28. Water Tourism Information System
XII.
SOCIO – ECONOMIC INFORMATION SYSTEM
29. Rural Information System
30. Urban Information System
B U L L E T I N
Main and Sub Information Systems
N N R M S
Sl. No.
-
Table 2: Main and sub Information Systems of India-WRIS
37
India-WRIS Web GIS Application Architecture (Technologies & Tools)
The three components India-WRIS Web GIS Application are:
1.
Database Design & Generation: The database for India-WRIS is highly complex with numerous sources
involved. Much of the data is spatial in nature but the amount of associated data is very large and also having
time series and will further increase exponentially with the passage of time. The creation and management of
such data is a colossal feat in itself and requires state of the art tools. The database standards and relationship
have been developed for all type of data. The database generation software used have the capabilities of
creating maps, viewing or exploring data, editing data, storing, conflation (integrating datasets from different
sources), transforming (into different coordinates systems, different representations, re-sampling, resulting
in new representation/format of the same data), querying, analyzing etc.
The database generation softwares used in India WRIS are AutoCAD Map, ERDAS IMAGINE, ESRI ArcGIS
and IGiS. Along with these, other utilities like MSExcel, Mat lab are used. Since most of the datasets in
India-WRIS are vector in nature, the software that has special tools to handle, edit and manipulate vector
data are given preference. Since the data is massive in size, the software used should have inherent properties
to handle the creation and maintenance of data in multi user environment. ESRI’s ArcGIS suite of products
is especially capable in this regard. ArcGIS also has capabilities to create Geo Databases, which is a RDBMS
file format for GIS data, allows India-WRIS database to be manageable, scalable, compatible and easy to
write queries for fast retrieval of results through web based applications over internet. (Table 3)
2.
Web Application & User Interface Technology: The major user requirement from the web portal is
data dissemination; hence advanced GIS data processing systems at the back end, augmented with the
best database connectivity over the internet is used so that the user is able to get intuitive and real
time information.
User has the facility for data visualization,
analysis on the client side and use further
to create customized reports. Adobe Flex
is able to deliver Rich Internet Application
(RIAs) across the enterprise and over the
web efficiently. Using the Flex API, IndiaWRIS combines GIS based Web services
from ArcGIS Server with other Web
content, which are displayed in simple,
dynamic mapping applications over the
Web. All the published map services are
compliant with OGC standards and the
Fig. 1: Web application architecture
services can be accessed using WMS, WFS,
WCS and KML standard formats. India-
WRIS system is using Oracle 11g, relational database management system (RDBMS) which supports multiuser system. ArcSDE as well as Oracle together used to handle geospatial data and to create multiuser geodatabase. (Figure 1)
3.
Database storage & hosting: In order to ensure reliable and secure 24 x 7 availability of the WebGIS, a
robust hosting architecture has been designed. The same has been replicated at three places namely, RRSC
(West) - Jodhpur, for data generation and s/w development as lead centre; NRSC - Hyderabad for web
hosting and CWC - New Delhi for intranet users and data validation & updation. (Figure 2)
2 0 1 2
Table 3: Tasks and Software/ Technologies Used for India -WRIS
Software / Technologies
2D
Adobe Flex, HTML, PHP
3D
ArcExplorer, .NET, ArcGlobe
-
WebGIS
Front end
J U N E
Tasks
Meta Data
Visual Basic
ERDAS Imagine, ENVI, ArcMap, ArcCatalog, ArcSDE,
AutoCAD 3D, IGiS, Map Window Library, GeodatabaseXML
Publishing / Web
Geodata Services
ArcGIS Server
Geodatabase /Back end
Oracle 11g, MySQL
N N R M S
Data Generation (Digital Image
Processing / GIS Mapping)
.NET(Windows)
/ Flex(Android) / JAVA (Symbian)
B U L L E T I N
Mobile
Designing
the
User
Interface,
Tools
and
Facilities in India-WRIS
WebGIS
Considering large number of
factors such as; type and volume of data,
large number of varied users, ease of
handling, varied nature of internet
connectivity in the country, information
requirement by the users and available
technologies, at most care has been taken
Fig. 2: Web hosting architecture
by the WRIS team to design the user
interface of the portal (Figure 3).
The home page is divided into four sections:
Universal Toolbar
This universal toolbar is present at the top of the page. This has two sections:
First, the toolbar at the top of the page contains the links to popup window having information required by the
user at any point and toolbar is visible at all times. The main links in this toolbar are:
•
About WRIS: This page contains a brief overview of India-WRIS project including its history, scope, vision,
goals, deliverables and time-frame for completion.
•
Accessibility: This provides information for navigating through India-WRIS like screen resolution, keyboard
shortcuts for easy navigation etc.
•
Tools: Numerous tools along with symbols for easy access are described in this part.
•
WRIS Mobile: A precise version of India-WRIS has also been developed for mobile and handheld devices.
This link provides more information and how to access.
•
Publications: Various documents generated for India-WRIS are made available and reports being generated
would be available to the users through this link. The documents are: (1) Software Design, System Architecture
and Data Security; (2) Database Organization and Geo-database Standards; (3) Metadata Standards
39
(4) General Technical Guidelines – An
Overview; (5) Theme-wise Database
Generation Methodology; (6) Quality
Assurance of Database – Standards &
Guidelines; (7) Quality Assurance of
Software – Standards & Guidelines.
• WRIS Education: All information
about how to make use of India-WRIS
information in the best possible ways.
• FAQ: This section contains answer to
common questions and queries about the
project and outcome.
• Feedback: Provides interface to post
user suggestions & feedbacks.
Fig. 3: Home page and front GUI of India-WRIS WebGIS
• Login: For downloads and providing
data inputs, login is provided based on
user categories.
•
Join WRIS: This section provides provision for new user to register and get connected to India-WRIS portal
for updates.
Second, the advanced information toolbar is available right below the banner. It contains links to pages
containing detailed information that a user requires when visiting the home page but may not require
while exploring the other sections of the information system. The links available in this toolbar are:
•
Home: This link leads to the main page of WRIS Portal.
•
Metadata: This link leads the user to the Metadata Explorer, which provides comprehensive information
of the source of spatial and non-spatial data.
•
Report Generation: This section features to automatically generate report of the user defined area /
region containing the all data intotables and maps and allows Save Asand Download in .pdf format.
•
Gallery: This section presents the user with an image gallery of events related to development of
India-WRIS.
•
WRIS Wiki: Comprehensive information for the water resources assets and projects of the country is made
available through WRIS-Wiki application.
•
Data Download: Apart from viewing the available data, the user may also wish to take the data and
perform analysis / do value addition. This link allows the download of GIS layers and associated attributes.
•
Help: A comprehensive and universal help is documented in this section assisted with diagrams, screenshots
and short videos.
•
Search: Consolidated search is provided into the complete information system.
Main Menu Toolbar
This is the heart of India-WRIS information system where all the major links to the various Web GIS
tools are provided in a rich GUI assisted format for easy access and use. The various sections accessible
through this menu are:
2 0 1 2
WRIS Info Discovery
This section aids the user in discovering information contained in India-WRIS pertaining
J U N E
to a particular geographic area. The user can select his area of interest based on the Administrative
units, Hydrological units and Constituency wise and he is presented with a condensed list of all
-
information available in India-WRIS for the area.
WRIS Explorer
B U L L E T I N
This is the crux of India-WRIS WebGIS where all the data can be explored and interpreted
using the various tools available for the purpose. The data in this section can be visualized using
both 2D as well as 3D tools.
WRIS Explorer - Geo Visualization
2D Geo-Visualization -This section provides basic facility to visualize all the layers
N N R M S
together in any combination by turning layers on and off as per the requirements.
3D Geo-Visualization - The 3D application is based on ArcGIS Explorer and its
customized modules. The twin purpose of this application are; 3D Visualization and 3D Analysis.
The system consists of advanced features like: terrain view, fly around/fly to and measurements
over terrain are provided.
Sub-Information Systems
There are 12 major information systems namely, base data, surface water, ground water,
hydro-met, water quality, snow cover/glacier, inland navigation waterways, inter-basin transfer links,
hydrological extremes, land resources, water tourism and socio-economic. These have been further
divided into 30 sub-information systems. Each sub-information system is based on a particular
theme. It contains relevant layers and specially created tools to make the best use of the data.
Temporal Analyst
A large amount of water resources and related data regarding hydrological,
meteorological, pollution, etc., are temporal in nature. In order to represent these datasets, a
separate module has been created where facilities are provided to represent the time series
data using suitable charts, animations and to compare the data across stations or years.
WRIS Connect
Meteorological information, flood forecasts and other water resources projects related
data available in India-WRIS WebGIS could be very much helpful to the user for day to day life.
WRIS Connect has been created with the purpose to keep the user up-to-date with this
information. The main modules in WRIS Connect are –
Hydro Observation Data
This section enables the user to access the latest information from Hydro-Observation
sites of CWC and meteorological information.
AWS Data
Automatic Weather Stations of ISRO and CWC is planned to be made available through
this page.
41
Query Interface
User can have lot of queries answered directly through India-WRIS WebGIS Explorer and associated
available tools. To explore more details, user can place his queries through Query Interface that contains set of
fixed queries on various hydrological parameters. The answers are generated through different permutations
and combinations of these fixed queries. The result of a query is displayed in spatial as well as non-spatial
formats.
Input Data Builder
This module aims at keeping the data content of the various layers of India-WRIS up to date by providing
facilities to the data providing sources to ingest the current attribute data directly into the relevant layers. The
authorized users can enter the respective spatial and non-spatial data in the specified format into the information
system through this facility. The three main modules of Input data builder are Spatial, Non-spatial and Metadata
Input Builder.
Share Success Stories
The objective of this module is to connect people for water resources planning and management by
providing platform to upload the success stories so that others can view, interact and practice.
Create Your WRIS and Hydrology tools
This module provides facilities to the user to make further analysis of the downloaded data, adding new
datasets using available general and hydrology tools.
General Information Toolbar
This toolbar is available in the lower section of the home page and provides links to general information
about India-WRIS as:
•
Visitor Number: Gives the total count of the users visited since the launch. This keeps a track of the users,
their geographic location, time-duration spent on the page etc.
•
Disclaimer: This document outlines the legal implications of downloading and using the data obtained
from India-WRIS.
•
Sitemap: Contains a graphical representation of the pages of India-WRIS along with their interlinking. This
is to assist the user in easy navigation of various sections of the information system.
•
Links: This page lists the useful links related to water, information systems and related documents spread
across the World Wide Web that have been referred to during the development of India-WRIS.
•
Contact Us: Address & contact details of the project director and coordinators of the India-WRIS project are
listed out in this section.
•
Last updated: Displays the date of last modification or updation made in the current online version of IndiaWRIS Web GIS.
Conclusion
The hydrological processes are continuous as well as somewhat complex and therefore, a comprehensive,
reliable and easily accessible Information System having time series data of the hydrological and meteorological
observatories is a pre-requisite for effective management of water resources. The water resources
Information System, India-WRIS, is likely to be fully developed by end of 2012. However, the beta version is
launched in December 2010 and further updated version would be launched in first quarter of 2012.
2 0 1 2
References
Biswas, A. (1981). Integrated water management: Some international dimensions, J. of
J U N E
Hydrology, 51, 1-4, pp. 369-379.
Bouwer, H. (2000). Integrated water management: Emerging issues and challenges, Agricultural
-
Water Management, 45, 3, pp. 217-228.
B U L L E T I N
Ganapathy, C. and Ernest, A. N. S., (2004). Environ. Inf. Arch., 2, pp. 938–945.
Kaden, S., Becker, A. and Gnauck, A. (1989). Decision-support systems for water management.
IAHS Publ. No. 180, pp. 11-21.
Kumar, R., Singh, R. D. and Sharma, K. D. (2005). Water resources of India, Current Science, Vol.
N N R M S
89, No. 5, 10 SEPTEMBER 2005, pp. 794-811.
Lal, M. (2001). Climate change – Implications for India’s water resources. J. India Water Res. Soc.,
21, pp. 101–119.
Loucks, D. (1995). Developing and implementing decision support systems: A critique and a
challenge, Water Resource. Bulletin, 31, 4, pp.571-582.
Maidment, D. (1997). Opportunities for the development of a global water information system.
In : Land and Water Resources Information Systems - FAO Land and Water Bulletin 7 (Proceedings
of a Technical Consultation Rome, Italy, 15-17 December 1997), pp. 115-120.
Zalewski, M. (2002). Ecohydrology- the use of ecological and hydrological processes for
sustainable management of water resources, Hydrological Sciences Journal, 47, 5, pp. 823.
43
IN SEASON PROGRESSIVE ASSESSMENT
OF RAIN FED AGRICULTURAL
CROP STATUS IN INDIA USING
GEOSPATIAL TECHNIQUE
Manab Chakraborty and Panigrahy S
Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area
Space Applications Centre (ISRO), Ahmedabad-380015
Introduction
Timely information of crop prospects like progress of sowing, crop stress, disease/
pest occurrence and crop yield is required for management and planning concerning food
security. In this context, advanced technology like satellite Remote Sensing (RS) and
Geographic Information System (GIS) are being used by many countries. Use of satellite
remote sensing data for crop production forecasting in India is in practice for major crops
since more than two decades (Navalgund et al.,1991). The agriculture area in India is utilized
for crop production throughout the year at some part or other in the country, depending
upon various factors. The crop calendar varies significantly across the country, so also the
crop management practices. Weather, the most dominant variable that affects crop productivity
in India, also varies significantly across the country. Thus, the crop growing environment is
very complex in India, and multisource information is essential to improve the accuracy of
crop assessment. Realising this need, the FASAL (Forecasting Agriculture output using Space,
Agrometeorology, and Land based Observations) concept has been formulated by Department
of Agriculture and Cooperation, Govt. of India that envisages to integrate multisource
information in geospatial domain to monitor crop status as the season progresses (Parihar &
Oza, 2006). The Agrometeorological models have the potential to predict the crop status
from the beginning of the season. With the advent of spatial data products from
meteorological satellites including the rainfall data from KALPANA satellite of India, feasibility
of spatial agrometeorological modeling has been realized. This paper highlights the model
developed and being used to assess weekly status of major crops grown during kharif
season in India.
Soil Moisture Model
Hydrological processes control water-limited ecosystems mainly through the dynamics
of soil moisture. Thus, soil moisture budgeting is one of the most used approach particularly
for the rain fed agriculture crop monitoring. The major crop season in India known as
“kharif” coincides with the South West monsoon period, as the country receives maximum
precipitation during this period. Rainfall based soil moisture model can be effectively used
to assess the crop sowing prospect, moisture stress, flood risk etc.
Several models are found in literature, which simulate the patterns of root zone soil
moisture (Guswa et al., 2003). The complexity of these models depends on the different
levels of detail used in the representation of the main inputs and outputs of soil water,
2 0 1 2
namely, rainfall infiltration, evapotranspiration, and drainage, as well as on the number of soil
layers used in the calculation of the soil water storage (Smith and Marshall, 2009). In principle,
J U N E
the vertical profile of soil moisture in equilibrium with groundwater can be expressed by the
soil moisture characteristic curve regarding the height from the water table (z) as the negative
of the metric potential or suction head. The height corresponding to the field capacity (pF =
-
1.8) is about 1m. Hence if a water table is at about 1m depth, the water content in the top tens
of centimeters of the soil is near the field capacity. When the water content is around the field
B U L L E T I N
capacity, the change of actual evapotranspiration should also be small. Therefore, it seems
sufficient for analysing the surface water budget to predict the soil moisture in the upper layer
in which water content often becomes much lower than the field capacity. One of the simple
approaches for modeling soil moisture, which is reviewed and used extensively, in one or other
N N R M S
form, is the Bucket approach (Manabe, 1969).
Compound Bucket Soil Moisture Model
The above concept has been used to develop a Compound Bucket soil moisture
budgeting approach. The model is a single-layer bucket that describes soil moisture dynamics
at the daily time-scale by assuming the soil as a reservoir to be intermittently filled by rainfall
events. Soil water storage capacity is emptied by deep drainage, and evapotranspiration
processes. Runoff is neglected, as bunded fields dominate the agriculture scenario that prevents
runoff. The model designated as the Compound Bucket Soil Moisture (CBSM) model thus
consists of an Active Surface Soil Layer (ASSL), some tens of centimeters thick, in which water
content often reduces to below the field capacity, and an underlying soil moisture reservoir. If
W (mm) stands for the equivalent depth of liquid water contained in the ASSL with depth D
(cm), the change of W in a day, AW (mm), can be expressed as:
AW = W(t+l) - W(t) = Pr (t) - E(t) - Gd (t) - Rs (f)
Where t is the time in days, P r is the daily precipitation (mm), E is the daily
evapotranspiration (mm), Gd the daily gravity drainage (positive) or capillary rising and Rs the
daily surface runoff (mm). E is expressed as:
E = M - Ep
Where M is min[Wc/σWmax1], E p is the daily potential evapotranspiration (mm), Wmax the
total water-holding capacity and σ is the parameter specifying the resistance.
Salient points of the model are:
•
One-dimensional model (vertical direction).
•
Bunded (20 cm or more) agricultural fields. (No runoff from or to up to this level)
•
Actual Evapotranspiration (AET) is estimated from Climatic PET and using Soil Moisture
Availability, AET/PET Drying Curve, Crop Coefficients (kc).
•
Soil water percolation is computed for a given soil moisture using Saturated and
Unsaturated Hydraulic Conductivities (Ks, Kc) with excess standing water computed as
ponding in agricultural fields (input to flood assessment).
45
Figure 1 shows the model overview.
Geospatial Set Up
Most of the soil moisture models
developed and used are either point based
or lumped in nature. The geospatial
technique opens up the new vistas of
application of such models in spatial/map
domain. Geographic Information System
(GIS) techniques help in integration of a
variety of ancillary data: spatial, non-spatial
and efficient to capture, store, retrieve,
analyse and display of such data. Satellite
Remote Sensing (RS) data with its multispectral imaging, temporal monitoring,
Fig. 1: Overall concept of the Compound Bucket Soil Moisture Model
and large area coverage in different
spatial resolution has the capability of
generating many input layers like land use/cover, cropping pattern, crop calendar, etc. Thus, RS and GIS together
are best-suited approach to develop spatial soil moisture budgeting to assess the rain fed crop prospects in
terms of planting prospects, moisture deficiency, flooding events. The feasibility of geospatial modeling of soil
moisture is enhanced by satellite meteorological products, available in spatial format (Joyce et al., 2004). Thus,
the Compound Bucket Model was set up in spatial domain.
Model inputs in spatial format for set up:
i.
Agriculture area mask (remote sensing land/use cover map)
ii.
Soil texture map (ancillary data)
iii.
Moisture holding values and hydraulic conductivity of soil texture classes (published values)
iv.
Climatic Potential Evapotranspiration data (ancillary published)
In Season Input for Forecast
The model only requires daily rainfall as input to initialize and give the outputs. Rainfall product available
from NOAA/Climate Prediction Center’s has been used initially to calibrate and validate this model. Gridded
binary data are available for download from the ftp server (NOAA/CPC). Resolution of rainfall estimates are 0.1
by 0.1 degree. This data is further adjusted through local calibration using daily rainfall data from India
Meteorological Department (IMD) stations to remove any bias and scaling. With the availability of rainfall maps
from the Indian meteorological satellite KALPANA-1, the same is being calibrated since 2010. This product
is available from the Meteorological and Oceanographic Satellite Data Archival Centre of
SAC/ISRO, India (http://www.mosdac.gov.in).
The model outputs in spatial format are:
(i) Root Zone Soil Moisture (v/v), (i) Ponded Water (in case of wetland rice), (iii) Deep Percolation, (iv)
Estimated Daily AET (v) Crop suitable area and crop sown area
In Season Forecasting of Crop Status
The model outputs are then used to assess the status of crops grown during the kharif season. Currently,
2 0 1 2
the model has been calibrated and validated for two crops i. rice and ii. Coarse cereal ( jowar,
(i)
Specific crop growing district (GIS layer created from crop statistics)
(ii)
Specific crop calendars and crop coefficients (GIS layer from ancillary information)
J U N E
bajra, millets). For this the specific inputs used are:
-
(iii) Average bund height (for wetland rice crop)
The model outputs are logically used to
B U L L E T I N
generate the following information:
Crop sowing suitability: When the
average soil moisture in the period exceeds
a specific value, the area is flagged as
suitable and a random set of pixels is
N N R M S
selected from this, based on the
appropriate cumulative distribution
function (cdf) of the crop calendar. The
(randomly) selected areas are added
cumulatively to give crop suitable area,
from which crop-sown area is computed
by ratio method – compute fraction of total
crop area (population) suitable multiplied
by maximum possible crop area (in ha).
Crop moisture stress: The area
identified under a specific already sown,
is flagged as stress, if moisture status falls
below the threshold level (different for
rice and coarse cereals).
Flood probability/occurrence: In
the area already flagged as rice crop, when
the moisture exceeds the field capacity
(overflowing the bund), it is flagged as
flood condition. Prolonged weeks under
this are flagged as flood affected.
The model was calibrated using
reported field measured soil moisture in
Fig. 2: Comparison of average available soil moisture for August 2009 and
2008 derived using CBSM model. Reduction of bluish areas, particularly in
the Gangetic planes, summarise the moisture deficiency situation in kharif
2009. This had led to significant reduction in rice areas.
top
30cm
under
saturated
and
unsaturated conditions (percolation and
ET loss) for measured moisture holding
capacities of the soil and PET. The results were validated for rice crop (bunded).
Results
The ASM (Available Soil Moisture) is the first output of the model, which forms the
basis for further prediction of crop sowing status. Results of ASM obtained for 2008 (a normal
monsoon year) and 2009 (drought year) for the month of August is shown in Figure 2. By
August, in general, major percentage of rice crop gets planted. One can very clearly observe the
47
deficit during 2009 in the Indo-Gangetic
region. Using crop specific inputs, the
ASM was converted to progress of
planting of rice crop. Figure 3 shows the
model predicted rice areas planted and
their spatial distribution by the end of
August in India during 2008, and 2009.
The cumulative graph of progress of
planting of rice from June to August
(model output) along with actual acreage
(obtained using RS data under FASAL
project by same time frame) is shown in
Figure 4.
Predicting Flood Risk Area for
Rice
Since a bund height provision has
been made in the model, it is feasible to
assess the overflow over the bund in case
of heavy and cumulative rainfall-an
indicator of flooding/inundation of fields
(Figure 5).
Conclusion
Root zone soil moisture is one of
the most important indicators for rain fed
crop production prospects. Rainfall based
soil moisture modeling is one of the
robust methods. Satellite derived rainfall
product, now available from many sources,
along with traditional remote sensing data
has opened up the possibility of
geospatial modeling of moisture status.
The rainfall product from India’s KALPANA
Fig. 3: Spatial distribution pattern of total rice area planted by end of August in two
different crop years predicted by CBSM model (2009: deficit year, 2008: normal year)
satellite enhances the scope of utilizing
such model operationally. Currently,
operational forecasting is done on planting/sowing progress of two crops: rice and coarse cereal (as a group).
Further work on quantification of moisture stress, flood on crop yield is in progress.
Acknowledgements
The work has been carried out as a Research and Development activity of SAC. Authors are grateful to
Dr R R Navalgund, Director, SAC and Dr J S Parihar, Dy. Director for their encouragement and support
for this activity.
References
Guswa, A. J., Celia, M. A. and Rodriguez-Iturbe, I., (2003). Models of soil moisture dynamics in ecohydrology:
A comparative study, Water Resour. Res., 38(9), 1166, doi: 10.1029/2001WR000826.
2 0 1 2
Joyce, R.J., Janowiak, J.E., Arkin, P. A. and
Xie, P., (2004). CMORPH: A Method that
J U N E
Produces Global Precipitation Estimates
from Passive Microwave and Infrared Data
at High Spatial and Temporal Resolution.
Rainfall
data
((http://
B U L L E T I N
KALPANA
-
J. Hydrometeorology, 5, 487-503
www.mosdac.gov.in)
Manabe, S. (1969). Climate and the Ocean
Circulation:
1.
The
Atmospheric
Circulation and the Hydrology of the
N N R M S
Earth's Surface. Monthly Weather Review,
97(11), 739-774.
Navalgund, R.R., Parihar J.S., Ajai and
Fig. 4: Cumulative weekly progress of rice planting predicted by Model for
a normal year (2008) and a drought year (2009) from June to August in
comparison to actual estimate using satellite data.
Nageshwara Rao, P.P. (1991). Crop
inventory using remotely sensed data.
Current Science, vol. 61, pp 162-171
NOAA
/CPC
Rainfall
data;
ftp://
ftp.cpc.ncep.noaa.gov/fews/S.Asia/data/
Parihar, J.S., and Oza, M. P. (2006). FASAL:
an
integrated
approach
for
crop
assessment and production forecasting.
Agriculture and Hydrology Applications of
Remote Sensing, edited by Robert J.
Kuligowski, Jai S. Parihar, Genya Saito,
Proc. of SPIE Vol. 6411, 641101,
(2006) • 0277-786X/06/$15 doi: 10.1117/
12.713157 Proc. of SPIE Vol. 6411
Fig. 5: Categories of flood risk predicted by model for rice fields during
June 14-17, 2008
641101-1.
Smith, T.J. and Marshall, L.A., (2009). A
conceptual precipitation-runoff modeling suite: Model selection, calibration and predictive
uncertainty assessment, 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July
2009, pp 3556-3562, http://mssanz.org.au/modsim09
49
DECISION SUPPORT SYSTEM FOR
INTEGRATED DEVELOPMENT OF APPLE
ORCHARDS IN HIMACHAL PRADESH
UNDER THE TECHNOLOGY MISSION
Sushma Panigrahy1, Bhatt NB1, Oza SR1, Alka Sharma2, Parihar JS1 and Singh HP3
Space Applications Centre, ISRO, Ahmedabad-380015
2
HP Remote Sensing Cell, Shimla 3 Indian Council of Agricultural Research, New Delhi
1
Introduction
Horticulture crops comprising fruits, vegetables, flowers, spices, beverages, etc., play a
significant role in the food and nutritional security of the country (Negi, 2000). They occupy
about 8.5% of the total cultivated area of the country and accounts for 30% of India’s agricultural
GDP (Matto et al., 2007). Thus, during the past few years, horticulture development has emerged
as one of the major thrust areas under food security and rural development. The importance of
these crops compounds in hilly and undulating terrains, like the North East region of India,
Himachal Pradesh, J&K and Uttarakhand, where traditional agricultural activities catering to
field crops is not economically remunerative and sustainable, while the climate in general is
suitable for growing a variety of temperate and subtropical fruits. These regions have a vast
potential for horticultural crops that is yet to be realized. In this context, the Technology
Mission for Integrated Development of Horticulture in the North Eastern States including
Sikkim, Himachal Pradesh (HP), Jammu & Kashmir (J&K), Uttarakhand (TMNE) launched by
Government of India was very significant (Anon, 2000). The objective of the Mission was to
strengthen the horticulture based farming system that is economically viable and ecologically
sustainable, and is based on the “end-to-end approach” from planting to post harvest.
Himachal Pradesh has already carved a niche as a horticulture state. The state has
been recognized as the “Apple state of India” for being the first to introduce delicious
apples and for producing good quality fruits. Under the Technology Mission, the state
prioritized to accentuate the benefits of apple orchards farming. However, it is well known
that success of orchard based economy is strongly linked to marketing of the produce.
Cluster approach to create/intensify the apple belt was planned, instead of isolated
development, as this will enhance the capability of farmers to organize at association level to
address various requirements like supply of sapling, pesticide, fertilizer, development of
storing/packaging, processing units, etc. The prerequisite to achieve such goal is to have a
decision support system, providing information on the location, extent, condition of the
existing apple orchards, availability suitable area that will form a cluster for further expansion,
and prioritizing them for phased development. Geospatial technique is the best suited one
to achieve this task with desired accuracy and timeliness. It is now well established that
satellite Remote Sensing (RS) data with its multi-spectral imaging, temporal monitoring, and
large area coverage has the capability of generating information on extent of orchard area,
its condition and phenological events. Geographic Information System (GIS) techniques
help in integration of a variety of ancillary data: spatial, non-spatial and efficient to capture,
store, retrieve, analyse and display such data. Thus, RS and GIS together are best suited
2 0 1 2
approach to develop decision support system for planning and managing horticultural sector
in India. This potential of RS technology had already been harnessed for site suitability of the
J U N E
jhum areas for specific horticulture crops like passion fruit, pineapple, mandarine, cashew nut,
etc., under the first phase of the TMNE in the North Eastern region viz., Arunachal Pradesh,
Manipur, Tripura, Meghalaya, Mizoram and Sikkim states (SAC, 2003: A-E). The technique
-
was subsequently extended to Himachal Pradesh. This paper highlights the methodology and
B U L L E T I N
results obtained.
Study State and Crop
Himachal Pradesh (HP) having a geographic area of 55673 sq. km. consists of
12 districts. The state is having hilly terrain in which Shivalik hills occupy most of the area with
dense forests. The climate in the state varies from hot and sub-humid in lower regions, where
N N R M S
the elevation is only 450m to 600m to very cold of the alpine and glacial region in the northern
parts, where elevation is from 4800m upwards. Apple was introduced into the country by the
British in the Kullu Valley of HP in 1865, while the colored ‘Delicious’ cultivars of apple were
introduced to Shimla hills in 1917. There has been phenomenal increase in apple area during
the last 50 years, from 3026 ha in 1960-61 to little more than 85,000 ha in 1998-99. However,
the productivity level is still very low (5.56 t/ha). Although there are 226 varieties of apple in HP,
delicious group constitute the major share (about 83% in HP) of apple production. The
monoculture of a few cultivars such as Royal Delicious, Red Delicious and Rich-a-red has started
showing negative impact on the apple industry. Large number of old orchards (more than 30
years old) are showing decline in terms of growth and fruit yield and require large-scale
replantation. Since fruit color development in apple in warmer and lower (below 1800 m)
elevations are generally poor, such gardens need diversification to other viable crops. Three
districts namely Shimla, Kullu and Mandi, contributing around 78 per cent of the acreage and
more than 93.0 per cent of the production of apple in the state, was taken up for this study,
covering an area of 14584 sq. km area in 23 administrative blocks (Table 1).
Table 1 : Characteristics of the study districts
District
Geographical
area (sq. km)
No. of
Blocks
% area under apple
Shimla
5131
8
39.87
Kullu
5503
5
22.40
Mandi
3950
10
15.88
Total
14584
23
Objectives
The main objective was to create a digital spatial data base to aid planning and
management of apple orchards in HP. The detailed objectives were to:
I.
map the apple orchards at block level
II.
categorise the orchards based on their density/vigour
III.
generate spatial data base of terrain parameters ( elevation, slope, aspect) and
IV.
identify and prioritise additional areas for apple orchard expansion using suitability index
51
Data Used and Methodology
Indian Remote Sensing Satellite (IRS) Resourcesat-1 LISS III data of May 2004 and 2005 has been used
to map the apple orchards at block level. LISS III data has 23 m spatial resolution and optimum for generating
1:50,000 scale maps. The sensor provides data in four spectral bands viz., green, red, near infrared (NIR) and
short wave infrared (SWIR). All the four bands were used to map the orchards using suitable classifier. Red and
NIR bands were used to generate Normalised Difference Vegetation Index (NDVI) that reflects the vegetation
density/vigour, which was used to categorise the orchard area into three classes viz (i) dense, (ii) moderately
dense and (iii) sparse. Resourcesat-1 LISS IV data with around 6m spatial resolution was used at sample basis to
collect ground/field information and evaluate classification accuracy of the orchards obtained using LISS III data.
Apple orchards have distinct chilling requirement for various physiological processes. Thus, elevation,
slope and aspect play an important role for growth and fruit quality. The Shuttle Radar Topographic Mission
(SRTM) data of NASA that provides Digital Elevation Model (DEM) with a spatial resolution of 90m and vertical
error of less than 15m was used to generate terrain parameters viz., elevation, slope, aspect and drainage. The
apple orchard map was wrapped over DEM to obtain the orchard site characteristics. The location of large contiguous
dense orchards found in different districts was used to develop the site suitability index, in terms of elevation,
slope, and aspect. Major village locations were identified in the LISS III data and compiled from other sources.
Map showing culturable wasteland areas were taken from available source. This was further fine tuned
using LISS III data acquired in 2004. April month data was selected to avoid inclusion of agriculture fallow lands.
This site suitability index was used through a logical modeling to identify potential sites (within the culturable
wasteland area) for apple orchard expansion. The sites were fine tuned using soil information and then prioritized
based on their nearness to the existing orchards. All these data were brought to a standard map projection and
stored in GIS environment for easy retrieval and to aid decision making by planner and managers. This will also
help monitoring of the success of such national/state schemes.
Results
Status of Current Orchards
May month data was found to be better suited to discriminate the apple orchards from other land cover
classes like forest, agriculture and scrubland as seen in the False Color Composite (FCC) of LISS IV data acquired
(Figure 1). The average classification accuracy of apple orchards using LISS III data was around 90 per cent. The
accuracy is high (around 95 per cent) for Kullu and Shimla districts due to the high concentration, contiguous
cultivation practice and good crop vigour.
The accuracy was around 88 per cent in
Forest
Mandi district as the orchards are more
fragmented, sparse and adjoining scrub
land cover.
Apple
Total orchard area mapped in the
study blocks in the three districts
Forest
amounted to 73282 ha with 21132 ha,
37630 ha and 14520 ha area in Kullu,
Shimla and Mandi districts respectively.
Nagar block in Kullu district with
8411.0 ha was observed to be the highest
Fig. 1: IRS LISS IV image of May 2004 over Kullu valley showing the spectral signature of
apple orchards and other land cover/use classes (the field photograph shows the orchard
status during end of May at lower elevation)
orchard-holding block, followed by
Jubbal-Kotkhai (7942 ha) and Rohuru
2 0 1 2
(6142 ha) blocks of Shimla district, while Nirmand block of Kullu district had the lowest orchard
J U N E
area (1453 ha).
The orchards were in general of good condition. Around 18.6 and 27 per cent orchards
in Shimla and Kullu districts respectively were under dense category. Nagar block of Kullu
-
district had the highest concentration of dense orchards (3293 ha), followed by Narkanda
block of Shimla district (2273 ha) (Table 2). Mandi district had around 29 per cent area under
B U L L E T I N
sparse category.
Terrain analysis showed that around 92, 82 and 75 per cent of the orchards in the
Shimla, Kullu and Mandi districts respectively lie at the elevation range of 1500-3000 m
(Table 3). Slope of 20-40 degree and aspect of the North-East and South-East supported most
of the orchard. Very good orchards were observed at the elevation range of 2000 – 2500 m.
N N R M S
Most of the poor orchards were observed below 1500m elevation mainly in Mandi district.
Table 2 Apple orchard area and per cent area under different density category
District
Orchard area (ha)
Density category (ha)
Sparse
Moderate
Dense
Kullu
21132
648
14814 (70.10%) 5670 (26.83%)
Shimla
37623
9659
20974 (55.75%) 6990 (18.58%)
Mandi (4 blocks) 14511
4229 (29%)
9452 (65.14%)
830 (5.72%)
Total
14536
45240
13560
(19.84%)
(61.75%)
(18.41%)
73266
Teble 3 Elevation range of apple orchards in three districts of Himachal Pradsh.
District
Kullu
Shimla
Mandi
Total
Orchard area (ha) in relation to elevation (m)
<1500m
1500-2500m
2500-3000m
>3000m
520
11830
5437
3311
(2.50%)
(56.0)
(26.0%)
(15.5%)
1757
29717
4997
1089
(4.7%)
(79.0%)
(13.3%)
(3.0%)
3523
9167
1696
103
(24.3%)
(63.0)
(11.7 %)
(1.0%)
5800
50714
12130
4503
(7.9%)
(69.2%)
(16.6%)
(6.3%)
The work was carried out for block level. Maps were generated for each of the blocks
showing the following:
i.
Distribution pattern of total apple orchards overlaid in satellite image
ii.
Distribution pattern of orchard categories: dense, moderate and sparse
iii.
Orchard distribution in relation to elevation range (in metre)
53
iv.
Orchard distribution in relation to slope (in degrees)
v.
Orchard distribution in relation to aspect
As an example, a block level map output is shown in Figure 2, representing Nagar block in Kullu district
which had highest concentration of dense orchards. For details of all block results and maps kindly refer the
project report ( SAC, 2006).
Suitable Sites for Orchard Expansion
The terrain parameters indicated that majority of the dense orchards lie in the elevation range of
1800-2500m and the slope of 25-35 degree. Using the above suitability index, around 50000 ha area has been
identified as potential area for apple orchard in these three districts and belong to the agroclimate zone of
“High hill temperate wet zone”. District-wise, the area distribution is 15,000 ha in Kullu district, 16000 ha in
Shimla district and 12000 ha in Mandi district. Around 40 per cent of these areas lie adjacent to the
existing orchards, thus most conducive for intensifying the current apple belt through cluster approach.
The distribution pattern of the suitable areas in these three districts overlaid on the elevation range is
shown in Figure 3.
Conclusion
Horticulture development has
emerged as one of the major thrust areas
in agriculture sector in view of the
domestic requirements as well as export
potential. Since post harvest management
and marketing is critical for economic
viability of horticulture based farming, a
strategic development plan is essential for
this. Thus, a cluster approach (selection of
land units of adjoining villages/blocks
suitable for a particular crop that will
sustain commercial marketing) is generally
preferred when horizontal expansion is
planned. An accurate and updated
database of the exiting orchard and a
decision support system to select most
suitable areas for further expansion is the
prime requirement for such planning.
Geospatial technique comprising of
satellite remote sensing and Geographic
Information System (GIS) is the only way
to achieve this in spatial domain. This
technology was used for developing a
decision support system for apple orchard
management and expansion planning in
Himachal Pradesh under the Horticulture
Technology Mission. The apple orchards
Fig. 2: Map output for Nagar block in Kullu district showing (a) apple orchards overlaid on
LISS III FCC,(b) density classes of apple orchards, (c) distribution pattern of orchards in
relation to elevation, (d) in relation to aspect.
in the major growing blocks of Kullu,
Shimla and Mandi districts in Himachal
could
be
mapped
2 0 1 2
Pradesh
with
90% classification accuracy using
J U N E
Resourcesat-1 LISS III data. It was also
feasible to categorise the orchards into
three density classes. Elevation, slope and
-
aspect of the orchards generated using
satellite based DEM showed dense
B U L L E T I N
orchards in the elevation range of 18002500 m. This is for the first time that maps
showing apple orchard overlaid on
elevation, slope and aspect have been
generated for the state at block level. This
N N R M S
data base along with culturable wasteland
map derived using RS data was used to
identify suitable areas for further
expansion of the apple orchards that will
strengthen the current apple belt. Around
50,000 ha area was identified as suitable
in these three districts. The geospatial
Fig. 3: Distribution pattern of current apple orchards (green color) and the
potential suitable areas (blue color) for future expansion of apple orchard
overlaid on elevation gradient (dark is lower and white is higher) in three
districts of Himachal Pradesh.
database in digital format will facilitate
to
develop
and
execute
further
development plan on scientific basis.
Acknowledgements
This work was carried out as a part of the “Technology Mission for Integrated
Development of Horticulture in North East Region, including Sikkim” Department of Agriculture
& Cooperation, Govt. of India. We acknowledge the field work carried out by a large number
of Horticulture Development Officers and Extension Officers of the respective districts in HP,
which had made it possible to execute this project within the desired time and accuracy.
References
Anon, (2000). Scheme “Technology Mission on the Integrated Development of Horticulture in
NE States including Sikkim” Department of Agricultural and Cooperation, Ministry of
Agriculture, Govt. of India.
Negi, J.P., Singh, B. and Dagar, K.S. (2000). Indian Horticulture Database Millenium, 2000.
ational Horticultural Board, Ministry of Agriculture, Govt. of India.
Mattoo, A., Mishra, D. and Narain, A. (2007). From Competing at Home to Competing Abroad:
A Case Study of India’s Horticulture,” Oxford University Press and World Bank.
SAC, (2003). A. Identification and prioritisation of suitable sites for passion fruit using remote
sensing and GIS in Aizawl district, Mizoram, Project Report, Space Applications Centre, ISRO,
Ahmedabad, RSAM/SAC/RESA/NEHORT/PR/01/03.
SAC, (2003 B). Mapping of suitable sites for mandarin in East District, Sikkim using Remote
Sensing and GIS. Project Report, Space Applications Centre, ISRO, Ahmedabad, RSAM/SAC/
RESIPA/NEHORT/PR/03/03.
55
SAC, (2003 C). Mapping of current jhum areas suitable for cashew nut in East Garo Hill District, Meghalay using
Remote Sensing and GIS. Project Report, Space Applications Centre, ISRO, Ahmedabad, RSAM/SAC/RESIPA/
NEHORT/PR/04/03
SAC, (2003 D). Mapping of suitable sites for pineapple in West District, Tripura using Remote Sensing and GIS.
Project Report, Space Applications Centre, ISRO, Ahmedabad, RSAM/SAC/RESIPA/NEHORT/PR/05/03.
SAC, (2003 E). Mapping of suitable sites for pineapple in Bishnupur and Thoubal Districts, Manipur using
Remote Sensing and GIS. Project Report, Space Applications Centre, ISRO, Ahmedabad, RSAM/SAC/RESIPA/
NEHORT/PR/06/03.
SAC, (2006). Apple orchard characterization using remote sensing and GIS for Shimla, Kullu and Mandi districts
in Himachal Pradesh, Project Report, Space Applications Centre, ISRO, Ahmedabad, SAC/RESIPA/ARG/AMD/
HTM/PR/01/06, pp. 163.
Introduction
Concern over the role of human activity on our environment has increased
the demand for integrated, spatially distributed, environmental models that address
the interactions of human activity, the terrestrial biosphere, climate and forests.
Furthermore, the widespread availability of Geographic Information Systems (GIS)
supports spatial data processing, manipulation and analysis to increase the accessibility
of spatial models. As a result, there has never been a greater need for decision support
tools to help in evaluating the applicability of complex environmental models. One of
the many possible requirements for such models is that their underlying assumptions
should be consistent with a potential application of the model. The more deep rooted
assumptions, such as those based on spatial aggregation, are often poorly documented
and thus less well understood to model end – users. If explicit knowledge of the
assumptions underlying a process-based model is unknown, then conflicting
assumptions resulting from a combination of such models will undoubtedly produce
an unexpected and often difficult to evaluate output. Conflicting assumptions in
geographical information systems have previously been described as semantic errors
(Worboys and Deen, 1991; Robinson and Frank, 1985), since they are usually
attributable to differences in interpretation of a modeled reality and usually contribute
to the overall error. Semantic errors in the context of integrated models also contribute
to error in the form of over-prediction or under-prediction of model output.
Information technologies, including GIS and the Internet, have provided opportunities
to overcome many of the limitations of computer-based models in terms of data
preparation and visualization, and provide the possibility to create integrated Spatial
Decision Support System (SDSS).
Spatial Decision Support System
Online Transaction Processing (OLTP) and decision support (including Online
Analytical Processing (OLAP)) are two classes of applications handled by database
systems. These two classes of tasks have different requirements. High concurrency
and clever techniques to expedite commit processing are required to support a high
rate of update transactions. On the other hand, good query evaluation algorithms
and query optimization are required for decision support (Silberschatz et al., 2002).
The concept of Spatial Decision Support Systems (SDSSs) has evolved in parallel
with Decision Support Systems (DSSs) (Densham and Rushton 1987; Densham and
2 0 1 2
J U N E
B U L L E T I N
Sameer Saran1 and Harish Karnatak2
Indian Institute of Remote Sensing, ISRO, Dehradun-248001
2
National Remote Sensing Centre, NRSC, ISRO, Hyderabad-500625
1
N N R M S
SPATIAL DECISION SUPPORT SYSTEM
FOR BIODIVERSITY CONSERVATION
PRIORITISATION USING GEOSPATIAL
MODELING APPROACH
Goodchild 1989; Densham 1991; Crossland et al., 1995). Decision Support Systems are computer programs
designed to bring the whole of the knowledge base to bear a problem. This is achieved by providing a flexible
and adaptive solution system that makes explicit use of both the analyst’s models and the decision maker’s
expert knowledge. Here, the objective is to support (rather than replace) judgment in such a manner that
maximizes the strengths of both man and machine processes. Such systems create supportive tools under the
control of users without automating the total decision process, predefining objectives or imposing solutions. A
DSS is designed to support semi-structured decision making tasks, a class of problems for which no automatic
algorithms exists; rather the solution procedure consists of evaluating alternatives to find a good solution as
opposed to the optimum solution.
“The generic definition of DSSs/SDSSs (Keen and Scott-Morton 1978), can be defined as an interactive,
computer-based system designed to support a user or group of users in achieving a higher effectiveness of
decision making while solving a semi-structured spatial decision problem”.
The DSS provides three subsystems: a Knowledge System containing data and data manipulation
procedures; a Language System, the user interface; and a Problem Processing System to interface models and
data. The user employs these subsystems to develop a solution procedure that meets the particular needs.
In another perspective, a DSS can be said to consist of the following management systems (Sharifi.
1996; Saran, 2002)
a.
Data Management: The data management includes the database(s), which contains relevant data for the
situation and is managed by software called Database Management System (DBMS). This will include both
spatial and non-spatial data.
b.
Model Management: A software package includes statistical, management science or other quantitative
models that provide the system’s analytical capabilities and an appropriate software management. This will
include the existing models and provision to built new models depending upon the need.
c.
Communication subsystem (dialog subsystem): The user can communicate with and command the DSS
through this subsystem. It provides the user interface, which is iterative and interactive.
d.
Knowledge Management: This optional subsystem can support any of the other subsystems or act as an
independent component. The concept of DSS generator may be evolved to develop specific application
oriented DSS.
The objective of any DSS can be put as to
•
Assist managers in their decision processes in semi-structured tasks
•
Support, rather than replace, managerial judgment
•
Improve the effectiveness of decision making rather than its efficiency.
Multi-Criteria Spatial Decision Analysis
Spatial decision analysis is a set of systematic procedures for analyzing complex spatial decision problems.
The implementation of decision analysis is to divide the original problem into smaller parts, analyze each part
and integrate them logically to produce a meaningful solution (Malczewski, 1999). The decision making process
itself is a broadly defined term with importance in many fields such as social, economic, natural resource
management and disaster management including GIS domain. Spatial decision analysis is a specific subclass of
the decision analysis process where the decision maker has to choose the best alternative from sets of
geographically defined alternatives (events), on the basis of multiple, conflicting and incommensurate evaluation
2 0 1 2
criteria. In the geographically defined alternatives, the final decision also depends upon the
spatial arrangement of alternatives (spatial variability). Most of the real world spatial problems
J U N E
give rise to Geographic Information System (GIS) based Multi-criteria Decision-Making (MCDM)
or Multi-criteria Decision Analysis (MCDA). The evaluation and ranking of alternatives by MCDM
techniques is based on associated criteria values, objectives and preferences of the different
-
decision makers. Spatial multi-criteria analysis is vastly different from conventional MCDM
techniques because of its additional explicit geographic component. In comparison with
B U L L E T I N
conventional MCDM analysis, spatial multi-criteria analysis require information on criterion
values and the geographical distribution of alternatives in addition to the decision maker’s
preferences in a set of evaluation criteria. In the spatial multi-criteria decision analysis, two
concerns, are of vital importance: (1) the GIS component (e.g. data acquisition, storage, retrieval,
manipulation and analysis capability); and (2) the MCDM analysis component (e.g. aggregation
N N R M S
of spatial data and decision maker’s preferences into discrete alternatives Carver, 1991;
Jankowski, 1995).
Simon (1960) presents one
decision flow chart for multi-criteria
decision making (Figure 1), showing three
phases of decision making i.e. intelligence
phase, design phase and choice phase.
Data
acquisition,
processing
and
examining are done in the intelligence
phase; formal modeling/GIS interaction is
the design phase to develop a solution
set of spatial decision alternatives. The
integration
of
decision
analytical
techniques and GIS functions supports the
design phase significantly. The choice
Fig. 1: Decision flow for multi-criteria spatial decision analysis
phase involved selection of the particular
alternative from available set. In this phase, specific decision rules are used to evaluate and
rank the alternatives. The three stages of decision making do not necessarily follow a linear
path from intelligence to design, then to choice (Malczewski, 1999).
AHP for Multi-Criteria Spatial Decision Analysis
Saaty (1977, 1980) has developed the Analytic Hierarchy Process (AHP) based
on three principles i.e. decomposition, comparative judgment and synthesis of priorities.
AHP is a mathematical method of analyzing complex decision problem with multi-criteria.
The decomposition principle of AHP requires the decision problem to be decomposed
into hierarchy that captures the essential element of the decision problem. The comparative
judgment principle of AHP requires pair-wise comparison of the decomposed elements
within a given level of hierarchal structure with respect to the next higher level. The synthesis
principle of AHP takes each of the derived ratio scale local priorities in various levels of hierarchy
and constructs a composite set of priorities for the elements at the lowest level of the hierarchy.
The standard method used to calculate the values for the weights from the Analytic
Hierarchy Process (AHP) matrix is to take the eigen vector corresponding to the largest eigen
values of the matrix, and then to normalize the sum of the components equal to one
(Saaty, 1977, 1980).
59
Biodiversity Conservation Prioritization using Spatial Decision Support
System
The increasing exploitation of forest and natural resources are of extreme concern to ecologists in this
country. Geospatial techniques are important tools to cater to the need of decision makers in the area of ecodevelopment and forest management. These tools can also help to study and to characterize the biodiversity of
particular regions. Further, these tools are often designed to assist decision-makers in spatial and temporal planning
and management. Although there are many land evaluation techniques and GIS software available, many systems
are not effective because they often lack the analytical and statistical facilities required by many decision makers
(Armstrong and Densham, 1990; Eastman et al., 1993; Pereira et al., 1993). The goal of the DSS is to help decisionmakers in the process of solving semi-structured problems, hence providing functions for storing, managing,
analyzing and displaying spatial data.
Problem Definition
The SDSS implementation model (Saran et al., 2003) is based on a landscape ecological principles
derived model developed by Roy and Tomar (2000) for biodiversity characterization. The data used are the
outcomes of the DOS-DBT Phase I project entitled “Biodiversity Characterization at Landscape Level in North East
India using Remote Sensing and GIS” under joint collaborated project by the Department of Space and Department
of Biotechnology, Government of India.
Conceptual Framework of SDSS Model
Fragmentation of ecological units have been well documented at landscape level using patch number,
size, shape, abundance and forest matrix characteristics (Forman and Godron, 1986; Lehmkhul and Ruggiero,
1991; Ripple et al., 1991; Skole and Tucker, 1993; Ravan and Roy, 1995; Roy and Tomar, 2000). Ecosystem
degradation and patch characteristics are found to be associated with a degree of spatial fragmentation (Ludeke
and Maggio, 1990; Mertens and Lambin, 1997; Roy et al., 1997). Characterization of habitats, their configuration
and degree of fragmentation, on the other hand, provide reliable information on biodiversity distribution
pattern. The site-specific studies use theoretical principles in conservation biology, which are often inadequately
tested in the field. These principles are being used as a framework to guide management plans, with obvious
limitations of being applicable only in the spatial context (Abott, 1980). An application of ecological principles
for prioritizing the biodiversity rich sites has the advantage of integrating the spatial information, non spatial
information and horizontal relationship in space and time. The landscape approach for biodiversity
characterization also addresses some of the limitations of ground based point inventory.
The spatial data on forest/vegetation and land use are generated using satellite remote sensing data (IRS
1D LISS III, March 2000) through digital classification. Database is generated in GIS domain for further analysis.
The spatial and non spatial data from other ancillary data sources are combined to generate habitat maps. Landscape
analysis for determining the parameters like fragmentation, porosity, proximity and other patch characteristics
have been used to derive disturbance index using proximity from settlements and roads. The knowledge base (as
available in the literature) with respect to ecosystem uniqueness, species richness, bio-diversity value is used to
create attribute information of the composite strata of vegetation type and disturbance regimes. The terrain
complexity and disturbance index were spatially combined with the above knowledge base to model the biological
conservation prioritization areas.
There are certain parameters which determine the spatial biodiversity distribution. It will be
essential therefore, for decision makers to vary these parameters as per the local knowledge. These input
parameters are criteria’s for biodiversity conservation prioritization and their respective dependency are described
in Figure 2.
2 0 1 2
The study area selected for the
research work is Nokrek Biosphere reserve
J U N E
forest which lies in Meghalya and is one
of the exceptionally bio-rich areas in North
-
East India.
Criteria’s
for
Biodiversity
B U L L E T I N
Conservation Prioritization
Overall five criteria’s have been
used for biodiversity conservation
prioritization. These criteria’s are derived
Fig. 2: Framework for biodiversity assessment (Roy and Tomar, 2000)
using remote sensing, field based data
N N R M S
and literature. The details of the individual criteria’s are explained below:
Ecosystem Uniqueness
The uniqueness of the ecosystem was determined on the basis of field data, species
composition, extent of the area, contiguity, importance in landscape and critical habitat value
of the patch. The interpreter in consultation with local research institute, field scientist and
NGO expert, assigned the weights
Terrain Complexity
This parameter was computed through DEM. The DEM, which is a spatial layer was used in
the raster mode for computing grid cell based variance for the entire spatial coverage. A grid of
specific size is convolved to compute the interspersion at each cell in an iterative manner. The input
criterion for this is the derived variance data.
Species Richness
Species richness can be described as the number of the species in a sample or habitat
per unit area. Indices can be generated to bring them to similar scale. The simplest species
richness index is based on the total number of species and the total number of individuals in
the sample or habitat. The higher the value, greater is the species richness. The species richness
is usually calculated by using Shannon Weiner Information Theory based Index.
Disturbance Index
Major disturbance to biodiversity is caused by human activities. A relationship exists
between the biodiversity and the disturbance in a particular area. Human activities like
agriculture, shifting cultivation, rail and road development activities cause depletion of
biodiversity. These disturbance causes splitting of forest area into patches. These patches
depending upon their size, shape, number, etc., exhibit different biological diversity. The
Disturbance Index (DI) is computed by adopting a linear combination of the defined parameters
on the basis of probabilistic weights. These parameters are fragmentation, porosity,
interspersion, proximity from disturbance sources and juxtaposition (Roy and Tomar, 2000)
Biodiversity Value
The valuation of diversity is based on auxiliary information like Total Importance Value
(TIV), economic value, scientific known value and ground knowledge. The TIV, in percent, was
computed following Belal and Springuel (1996). Available data with Botanical Survey of India
and available literature have been used to develop this index.
61
Prioritization efforts for biodiversity conservation would not be complete without considering the flora
and fauna richness of the region/area. However, the study only focuses on floral endemism. Depending upon the
distribution pattern inferred from species database, appropriate weights are given. An attribute value to each
forest type or floristic type is attached wherever such habitats occur (Ramesh et al., 1997). The attributes a collected
on the ground were transferred into GIS domain as composite stratum of habitat and disturbance regimes.
System Architecture and Implementation
Biocon SDSS is based on the state-of-the-art client/server computing technology (Saran et. al., 2003;
Karnatak et al., 2009). The overall flow diagram of the computing environment is shown in Figure 3.
The system is based on multi-tiered architecture, which can be divided into two broad categories, i.e. client end
process and server end process. The server end processes can be again divided into two parts i.e. application
server and data server.
Biocon SDSS is an Internet GIS based application where most of the GIS functions are available on the
web browser. The GUI of the application
provides the facility of selecting models
and area of interest and the system lacks
the decision maker’s input for decision
analysis. After pair-wise comparison of
decision alternatives and criteria, the
system calculates the overall priorities of
the alternatives and this priority goes as
an input in the GIS environment. The
application server processes the input with
available data sets in data server and sends
it to map server, map server launches this
output for the web browser by using the
available map service in the service registry
of the map server. The JAVA servlet engine
is used for connectivity between a map
server and a web server. In an ASP based
application most of the programming is
Fig. 3: System architecture for Biocon SDSS
done using the server side programming
language. Multi-criteria decision models like AHP are implemented by using VB script and JAVA for vector and
raster data sets respectively. The geospatial data is organized as Geodatabase in the RDBMS environment, which
is one of the best solutions for distributed GIS applications.
In the decision making process, the SDSS shell prepares one SDSS layer at the back by overlay operation
for vector data; in each operation it generates a large number of records in Geodatabase. The complex geospatial
analysis such as MCDA, for this type of data sets takes more time in geo-processing; therefore the performance
and tuning of the GIS data can be improved by using the RDBMS function. The Biocon SDSS gives its output in
two ways i.e. one in non-GIS environment and another in GIS environment. The statistical report and non-GIS
output are in simple HTML file format, GIS environment of Biocon SDSS provides GIS tools for geospatial
analysis and querying such as panning, zooming, query builder, layer addition and identify tool (Saran et. al.,
2003). The database server of Biocon SDSS contains all the spatial criteria maps and species tables organized
into RDBMS. The other information of the study region, the implementation of Online Analytical Processing
(OLAP) and related concepts of RDBMS were used for better performance and security of data.
2 0 1 2
Biocon SDSS ver. 1
The procedure of decision making
J U N E
in the area of biodiversity conservation
prioritization involves the organization of
spatial and non spatial databases. The web
-
application of SDSS is developed and
operational through BIS (Biodiversity
B U L L E T I N
Information System) Portal (Figure 4a)
(Saran et. al., 2003) (www.bisindia.org/sdss)
(Figure 4b). It involves the following steps:
Step 1: The user can select the particular
forest type where conservation is needed
N N R M S
by using a web interface of SDSS form.
This is applied as a constraint within the
criteria layers (Figure 5a).
Fig. 4a: Biodiversity Information System (www.bisindia.org)
Step 2: Assignment of weights to each
of the layers based on the users choice.
The user’s choice was obtained from a web
interface. The qualitative choice varies from
unimportant to most important for each
of the individual criteria layers.
Step 3: Spatial overlaying of all the criteria
layers were multiplied with their respective
weights using weighted summation
model approach.
Step 4: The resultant layer is generated
on the fly showing different biodiversity
conservation priorities sites on the
web browser using ArcIMS Map server
(Figure 5b).
Biocon SDSS ver. 2.0
Fig. 4b: Spatial Decision Support System (www.bisindia.org/sdss)
The Bioncon SDSS was further
enhanced by integrating the landscape
model with AHP for deriving biologically rich sites in group decision making environment. The
decision maker can interact with the spatial criteria layers and after value addition, the landscape
model generates new priorities area (map based) on decision maker’s choice and preferences
(Karnatak et al., 2007). The decision making process of developed system is explained in the
following steps:
Step 1: When the decision maker runs this system, it asks for selection of decision factors and
primary weight to decision criteria (Figure 6a).
Step 2: In the next step, the AHP technique is implemented where the decision maker has to
do the pair wise comparison of decision factors and criteria based on which the priorities of the
alternatives and criteria are calculated. These priorities, entered as an input into GIS shell,
63
where the various GIS operation will take
place and the system will generate a SDSS
layer with priorities of the areas.
Step 3: The output can be visualized in
thin as well as thick client mode in web
browser. Various GIS tools (like zooming,
panning,
identify,
query,
distance
measurement, buffer analysis, map output
generation, etc) are available for further
analysis and decision making. The system
also provides a facility to validate the
results in the same map viewer window
(Figure 6b).
Step 4: The ground truth sample plot can
Fig. 5a: Forest Type & Decision Criteria’s
be overlaid with output SDSS layer and
can be further queried for phytosociological analysis and status of the
plant species at that particular point of
location. The actual ground situation is
enhanced with available literature for each
plant species recorded at that particular
point during field sampling. For each
sample plot the SDSS shell calculate the
“total number of species recorded”,“total
number of medicinal, economical and
endemic plants and their importance”.
It also calculates the Total Importance
Value (TIV) index for each plant which is
based on available literature and
Fig. 5b: Biodiversity Conservation Priority Sites
supporting studies.
Step 5: The other important GIS
capabilities like closer view of the map
output with buffer analysis, satellite
image overlay, etc., can also be performed
in simple web browser at user end
without having any specific GIS software
installed at client end. The output is also
available in thick client mode where
various complex GIS operations like
editing of the map, add layer at user end,
submission of the comments, etc., are
available.
These analyses are very
important for quick decision making for
any GIS based decision analysis system
Fig. 6a: Forest types & decision criteria’s
(Figures 7a & 7b).
2 0 1 2
Step 6: If the decision maker is not
satisfied with the results, he/she can
J U N E
re-enter the values for new output.
Otherwise the each output window can
be saved at user end, in standard web
-
compatible data formats.
B U L L E T I N
Conclusion
The process of effective spatial
decision-making involves the organization
of large spatial and non spatial normalized
databases. It would thus be a cumbersome
N N R M S
task to evaluate every condition at each and
every point. This gives rise to uncertainty
Fig. 6b: Priority sites on thin client for conservation
in the process of decision making. In order
to compensate for this uncertainty, certain
assumptions have to be made. In such
cases, integration of the database with
community
knowledge
and
the
knowledge of the local people is essential.
The multi-criteria decision making process
further
improves
the
model
by
incorporating various normalization
methods, like Saaty’s scale, to do
pairwise comparison among various
criteria’s and use AHP method to
generate priority vectors. A web-based
approach is thus applied to interact with
distributed databases and cater to
effective decision making.
Fig. 7a: Priority sites on thick client for conservation
This is perhaps the first time that
such a Spatial Decision Support System
for biodiversity conservation prioritization
has been attempted, demonstrated and
operationalized. It has been observed that
the existing SDSS thus developed has
been used for planning and management
of biodiversity by various premier
institutions of the Government of India,
Non-Government Organizations and
above all, by policy and decision makers.
Fig. 7b: Geospatial analysis on priority sites
References
Abott, D.T., (1980). Woody litter
decomposition of Coweeta (Trombiculidae, Acarina), Hydrological Laboratory, North Carolina,
Ph.D dissertation, 136p, Athens, GA.
65
Armstrong, M.P. and Densham, P.J., (1990). Database organization strategies for spatial decision support systems.
International Journal of Geographic Information Science, 4(1), pp 3-20.
Belal A., and Springuel I., (1996). Economic value of plant diversity in arid environment, Nature and Resources,
31(1), 33-39.
Carver, S.J., (1991). Integrating multi-criteria evaluation with geographical information systems, International
Journal of Geographical Information Systems, Vol. 5:321-339.
Crossland, M.D., Perkins, W.C. and Wynne, B.E. (1995). Spatial decision support systems: an overview of technology
and a test efficiency. Decision Support Systems 14(3); 219-235.
Densham, P., and Rushton, G. (1987). Decision Support Systems for locational planning. In:R.G.Golledge and
H.Timmermans (eds.), Behaviour modeling in geography and planning. New York: Croom Helm, pp. 56-90.
Densham, P., and Goodchild, M.F., (1989). Spatial Decision Support Systems: a research agenda, Proceedings of
GIS/LIS’89, Orlando, FL, pp.707-716.
Densham, P.J., (1991). Spatial Decision Support Systems. In: D.J. Maguire, M.S. Goodchild, and Rhind D.W. (eds.),
Geographical information systems: principles and applications. Harlow, Essex, England: Longman, pp. 403-412.
Eastman, J.R., Kyem, P.A.K., Toledano, J. and Weigen, J., (1993). GIS and Decision Making. Explorations in
Geographic Information Systems Technology, Vol.4, UN Inst. For Training and Research, UNITAR European Office,
Switzerland, 112 pp.
Forman, T.T.R. and Godron, M., (1986). Landscape Ecology, (New York: Wiley and Sons).
Jankowski, P., (1995). Integrating geographical information systems and multiple criteria decision-making
methods, International Journal of Geographical Information Systems, Vol. 9(3):pp251-273.
Karnatak, H.C., Sameer Saran, Bhatia K. and Roy, P.S., (2007). Multicriteria Spatial Decision Analysis in Web GIS
environment. Geoinformatica, 11:407-429. (DOI 10.1007/s10707-006-0014-8).
Karnatak, H.C., Sameer Saran, Bhatia K. and Roy, P.S., (2009). Geospatial database organization and spatial decision
analysis for biodiversity databases in Web GIS environment, Geocarto International, 1-21. (DOI: 10.1080
10106040802677045).
Keen, P.G.W., and Scott-Morton, M.S., (1978). Decision support systems: an organizational perspective. Reading,
MA: Addison-Wesley.
Lehmkhul, J.F. and Ruggiero, L.F., (1991). Forest fragmentation in the pacific northwest and its potential effects
on wildlife. pp35-46.
Ludeke, A.K. and Maggio, (1990). An analysis of anthropogenic deforestation using logistic regression and GIS. Journal of
Environmental Management, 31, pp247-259.
Malczewski,
J.,
(1999).
GIS
and
Multi-criteria
Decision
Analysis.
John
Wiley
&
Sons:
New York, 392.
Mertens, B. and Lambin, E.F., (1997). Spatial Modeling of deforestation in Southern Cameroon. Applied
Geography, 17(2), pp143-162.
Pereira, J.M.C. and Duckstein, L., (1993). A multiple criteria decision making approach to
GIS
based
land
suitability
evaluation.
International
Journal
of
Geographic
Information
science, 7(5), pp407-424.
Ramesh, B.R, Menon, S. and Bawa, K.S., (1997). A vegetation based approach to biodiversity
gap analysis in the Agastyamalai region, Western Ghats, India. Royal Swedish Academy of Sciences, pp529-536.
2 0 1 2
Ravan, S.A. and Roy, P.S., (1995). Landscape ecological analysis of a disturbance gradient using
geographic information systems in the Madhav National Park, Madhya Pradesh. Current Science,
J U N E
68(3), pp309-315.
Ripple, W.J., Bradshaw, G.A. and Spies, T.A., (1991). Measuring forest landscape patterns in the
-
Cascade Range of Oregon, USA. Biological Conservation, 57, pp73-88.
Robinson, V.B. and Frank, A.U., (1985). About different kinds of uncertainty in collections of
B U L L E T I N
spatial data. Proc. Auto-Carto 7, American Society for Photogrammetry and Remote Sensing
and American Congress on Surveying and Mapping, Falls Church,VA, pp440-449.
Roy, P.S., Tomar, S. and Jegannathan C., (1997). Biodiversity Characterisation at landscape
Level using geospatial modeling technique. Biological Conservation, 95(1), pp95-109.
Roy, P.S. and Tomar, S., (2000). Biodiversity characterization at landscape level using geospatial-
N N R M S
modeling technique. Biological Conservation, 95(1), PP95-109.
Saran, S., (2002). Satellite multispectral image restoration and application in identifying
wastelands and also to develop computer based approach for their management- A study in
Bandwan Block, Purulia District(W.B.). H.N.B. Garhwal University, Srinagar, Garhwal, Phd thesis
(unpublished).
Saran, S. and Raju, P.L.N., (2002). Spatial Decision Support System for Wasteland Management,
IIT Roorkee.
Saran, S., Ghosh, S., Srivastava, G., Roy, P.S., Talukdar G. and Prasad. N., (2003). Spatial decision
support system for biodiversity conservation prioritization: A web based approach, Asian Journal
of Geoinformatics, Vol. 4, No.1, pp.21-30.
Saaty, T.L., (1977). “A scaling method for priorities in hierarchical structures,” journal of
Mathematical Psychology, Vol. pp15:234-281.
Saaty, T.L., (1980). The Analytic Hierarchy process. McGraw-Hill: New York.
Sharifi, M.A., (1996). Introduction to Decision Support Systems For Natural Resource
Management, (The Netherlands:ITC).
Silberschatz, A., Korth, H.F. and Sudarshan, S., (2002). Database System Concepts, 4th ed. (New
York: McGraw Hill Higher Education).
Simon, H.A., (1960). The New Science of Management Decision. Harper and Row: New York, 273.
Skole, D. and Tucker, C. (1993). Tropical deforestation and habitat fragmentation in the Amazon:
satellite data from 1978 to 1988, Science, 260, pp1905-1910.
Worboys, M.F. and Deen, S.M., (1991). Semantic heterogeneity in distributed geographic
databases, ACM SIGMOD Record,20(4),pp30-34.
67
ONERS: WEB BASED INDIGENOUS
DECISION SUPPORT SYSTEM
Raja Shekahr SS1, Shantanu Bhatawdekar2, Krishna Murthy YVN1
Srinivas CV3 and Venkatesan3
1
Regional Centres, NRSC, Hyderabad-500625
2
ISRO HQ, Benguluru
3
Radiological Safety Division, SE Group, IGCAR, Kalpakkam
E-mail: [email protected], [email protected]
Introduction
Bhabha Atomic Research Centre (BARC), Mumbai has initiated the development of
Online Nuclear Emergency Response System (ONERS) for nuclear power stations namely,
Kalpakkam (Tamilnadu), Kaiga (Karnataka) and Narora (UP). The system envisages the
assessment of dose, short & long term forecast, counter measure support, impact assessment
and emergency preparedness during radiological emergencies in order to minimize potential
threat to man and environment. On a pilot basis, the development of system has been taken
up for the Nuclear Power Station at Kalpakkam.
Methdology & Framework
The broad objectives of the system are:
•
Predictive calculation of meteorological parameters.
•
Calculation of concentration profiles using predicted weather data.
•
Calculation of source term and radiological doses.
•
Creation of spatial and non-spatial database for ingestion into dispersion model.
•
Development of GIS based Decision Support System and Integration of GIS database
The system calls for generation of spatial database viz., vegetation fraction,
soil texture, soil depth, slope, landuse, water mask, etc., for meso-scale atmospheric/
dispersion model to predict radiological impact, infrastructure and village database for
emergency preparedness and development of GIS based Decision Support System for
crisis management and mitigation. The sub systems involved in the overall system are
shown in Figure 1.
The various activities involved in the development of DSS includes:
•
User requirement analysis
•
Spatial framework creation
•
Identification of spatial and non-spatial database elements, level of database and
codification needs.
•
Spatial database creation for ingestion into dispersion model
•
Development of GIS based Decision Support System and Integration of
GIS database
2 0 1 2
Users Requirement Analysis
A detailed user need analysis has
J U N E
been carried out for clear understanding
of the DSS requirements particularly with
regards to the development of Graphical
-
User Interface, Display and Query interface
and overall structure of Decision Support
B U L L E T I N
System (DSS). Software requirement
specification document has been prepared
emphasizing the requirements and queries.
Database standards have also been evolved
for generation of spatial and non-spatial
N N R M S
database. Extensive study of open source
Fig. 1: Sketch of complete ONERS system including sub-systems
implementation has been done for
realizing the operationalization of DSS.
Spatial Framework Creation
Spatial frame work has been
created with grids of varying resolutions,
Figure 2, (18km, 6km, 2km, 1km, 500m,
250m) with required projection details for
MM5 and Dispersion models pertaining
to 68000’ E, 22000’ N to 88 000’ E, 8000’ N
latitudes
covering
the
states
of
Maharashtra, Goa, Andhra Pradesh,
Karnataka, Tamil Nadu, Kerala, Southern
parts of Gujarat, Madhya Pradesh,
Chhattisgarh and Orissa.
Fig. 2: Spatial frame work showing grids of varying resolutions
Support
for
Spatial
Database Creation
The spatial database prepared under ‘G2G project’ has been provided to IGCAR,
Kalpakkam for ingesting into meso-scale atmospheric/dispersion model for coarser domains
and evaluating the models. Spatial database viz., state, district, taluk, roads, rail, landuse,
vegetation, soil depth, soil texture, soil erosion, slope, waterbody generated under G2G project
has been reprojected to reference grids for incorporating in the model.
Development of Web based Decision Support System
Web based DSS is a quick tool to identify areas of impact, counter measure supports and
assimilation of on-line field measured data for re-assessment of the situation. Spatial database,
dispersion simulation and dose forecast can be integrated with facility of querying the position
of interest on maps, analyzing severity levels under different domains and impact assessment.
The plume distribution pattern and dose forecast in terms of direction, distance range and the
levels of the plume dose form the basis for decision making in emergency response.
Thus, a well coordinated DSS for risk management and communication helps in effective mitigation
measures and control actions during emergencies (Chang et al., 2002; Chang et al., 1997;
Sullivan et al., 1993).
69
The critical inputs for DSS for
nuclear emergency are the meteorological
and radiological parameters over the
region of interest and spatial database
providing natural resources information,
demography and infrastructure. Realistic
simulation
of
environmental
dispersion
radioactive
and
dose
estimations in the off-site long-range
distances (>10km) requires the application
of prognostic meso-scale atmospheric
models for the prediction of the time and
space varying meteorological fields. A
community developed Regional Weather
Fig. 3: Flowchart of operational real-time atmospheric dispersion system
Forecast model (MM5) coupled to an
advanced
Random
Walk
Particle
dispersion model (FLEXPART) is used for
the emergency response. The flow is
shown in Figure 3.
The meteorological fields needed
by the dispersion model are predicted
using the hydrodynamic model MM5.
These are the wind field (u, v, w) and the
boundary layer parameters (roughness
length, friction velocity, convective scaling
velocity, monin obukhov length, etc.). The
mesoscale model is validated for realistic
simulation of weather in the larger region,
Fig. 4: Front end Interface of ONERS
land–sea breeze circulation in the
Kalpakkam region and the associated
thermal internal boundary layer formation
(Srinivas et al., 2004; Rajendran TR 2003
and Venkatesan et al., 1997).
Development & Integration
ONERS
(Online
Nuclear
Emergency Response System) is an
indigenous web enabled Decision Support
System conceptualized, designed and
developed as client-server application as
shown in Figure 4. Open source
methodology and total indigenous
development efforts are employed in the
development of DSS. Linux operating
system has been a preferred choice for
Fig. 5: Integration of spatial data
open source and a combination of PHP
2 0 1 2
and java have been used for developing
interface
and
analysis
modules.
GIS
functionalities
required
J U N E
All the software components and
for
display, query and analysis have been
-
designed and developed indigenously
without any dependency on third party
B U L L E T I N
commercial libraries. The DSS primarily
designed for risk management and
mitigation, entails the assessment
of dose, short & long term forecast;
counter
measure
support,
impact
Fig. 6: Interface for raster & vector display, overlay & attribute identification
N N R M S
assessment and emergency preparedness
during radiological emergencies.
The DSS enables monitoring of
radioactive plume dispersion due to
airborne releases in a range of 25km and
100km around the site. The system is
enabled for real time monitoring by
integrating the numerical system that
constitutes a parallel version of a nested
grid meso-scale meteorological model
(MM5) coupled to a random walk particle
dispersion model (FLEXPART). The parallel
computing facility at the site provides
inputs for various real time and
hypothetical airborne releases in grids of
500m and 2km resolutions. Spatial
Fig. 7: Plume movement for thyroid, ground deposition and cloud
gamma dose
database
of
Infrastructure
natural
and
resources,
demography
is
integrated into the system using RS & GIS. Automatic data updating modules running on Linux
clusters integrate these real-time inputs to the model for analyzing the spatial distribution and
arriving at different domains of severity. The system helps decision makers in arriving at an
action plan for mitigating nuclear disasters and provides continuous monitoring facility to the
designated personnel at the site.
The present version of DSS facilitates following functionalities and the interfaces developed
for the same are depicted in Figures 5 to 9:
•
Display of the radiation doses, spatial & non-spatial database, wind vectors
and overlays
•
Simulation of plume movement for thyroid, ground deposition and cloud gamma dose.
•
Analysis of dose values for generating the severity domains & estimation of criticality.
•
Impact assessment, report generation and predefined mitigation measures
•
Proximity analysis for searching safe zones in the next 24 or 48 hours.
71
The core system is divided into
two branches. The first is online data
receiving and updating module that
resides on Fedora OS. Main function is to
schedule tasks for receiving and updating
radiological
and
meteorological
parameters from Debian cluster and
update the existing data on the web server
at synchronized time requirements. The
second module consists of servlets
performing various tasks as required by
the client for data display, query, analysis
& report generation. It is available for
direct access to client on any web browser
Fig. 8: Query interface for analysis of severity domains & impact assessme
connected to the server. The core database
is virtually available to the user for
modification / simulation with different
release rates, display schemes and report
formats. This entitles both decision
makers and technical / monitoring
personnel to utilize database according
to their own requirements / analysis for
the purpose of disaster mitigation /
hazard simulation & analysis.
Conclusion
For operational use, web based DSS along
with spatial database has been installed
on Linux cluster at IGCAR, Kalpakkam and
integrated with the real time output of
dispersion model. It has been rigorously
Fig. 9: Query interface for proximity analysis & safe zone search
tested with real time data simultaneously
on multiple client machines.
ONERS package integrated with the real time data of dispersion models has been demonstrated online. “Onsite Emergency Drill” has also been organized at IGCAR, Kalpakkam to appraise the district administration and
decision making authorities about the actions to be taken and formulate plans for mitigating the disaster.
ONERS DSS has successfully demonstrated the real capability of open source implementation and significantly
propelled the idea of utilizing open source solutions and providing freedom to the user from unnecessary
burden of commercial software. It is an innovative system implemented in a nuclear reactor scenario in India for
display of real time dispersion models for radioactive nuclides. Realizing the success of indigenous effort, it has
been envisaged to replicate the system for other Nuclear Power Plants in phase-II.
References
Chang Der-Quei, (2002). “Decision Support System for Emergency Response and Risk Management in Nuclear
Power Plants” , Dept of Environmental Engineering, National Cheng Kung University, Taiwan.
2 0 1 2
Chang, N.B., Wei, Y.L., Tseng, C.C. and Kao, C.Y., (1997). “The design of a GIS based decision
support system for chemical emergency preparedness and response for urban environment”,
J U N E
Computers, Environment and Urban Systems, 21(1), 67-94.
Rajendran, T.R. (2003). “Manuel on Emergency Preparedness – Kalpakkam DAE Centre”, Madras
-
Atomic Power Station, DAE, Kalpakkam.
Srinivas C.V., Venkatesan R., Bagavath Singhm and Somayaji K.M, (2004). “A real case simulation
B U L L E T I N
of the air borne effluent dispersion on a typical summer day under CDA scenario for PFBR using
an advanced meteorological and disperision model”, IGC-259, Kalkpakkam.
Sullivan, T.J., Ellis, J.S., Foster, C.S., Foster, K.T., Baskett, R.L., Nasstrom, J.S. and Schalk, W.W.,
(1993). “Atmospheric Release Advisory Capability: Real-Time Modeling of Airborne Hazardous
N N R M S
Materials”, Bulletin of the American Meteorological Society, Vol. 74, No. 12, December.
Venkatesan,R., Moelmann Coers, M. and Natarajan, A., (1997). “Modelling wind field and
pollution transport over a complex terrain using an emergency dose information code SPEEDI”,
J. Appl. Meteorology, V.36, No.9, 1138-1159.
73
A NOVEL GEOSPATIAL FRAMEWORK FOR
PROVIDING EFFECTIVE PLANNING AND
DEVELOPMENTAL INPUTS FOR DISTRICT
RESOURCES PLAN
Chutia D, Singh PS, Goswami C, Goswami J, Das R, Rocky P and Sudhakar S
North Eastern Space Applications Centre (NESAC), Department of Space,
Government of India, Umiam-793103
Introduction
The North Eastern Region (NER) has a unique amalgamation of geographical position,
cultural and socio-economic conditions, but this region of our country has not made much
progress as compared to the rest of the country due to various reasons viz., infrastructure
bottlenecks, institutional weakness, technological gaps, etc. In an age of liberalization of
the economy and globalization, agriculture in NER, with their diversity in plant species and
climate, can produce variety of commodities that have high export value in the regional and
global markets. These require more labour and knowledge of cultivation and needs support
of modern technologies, cold storage and marketing (Neog, 2006). Moreover, infrastructural
deficit is a major problem in the region, and acceleration in economic growth and the
region’s emergence as a powerhouse depend on how fast this deficit is overcome. Many a
times, the programs undertaken by Government institutions to address specific problems of
rural masses get poorly implemented due to insufficient technical inputs, lack of zeal and
transparency in implementation and ineffective monitoring methods. These are found to be
pronounced in Natural Resources Management (NRM) programs involving terrain dependent
interventions for better sustainability (Diwakar and Maya 2010).
In spite of tremendous information available in the custodies of different stake
holders and agencies in the form of resources maps, land and water resource plans at micro
watershed level, etc., the dissemination of information to the decision makers, planners and
grass root level users is not rapid enough in NER. Hence, there is a need for development of
an efficient information dissemination mechanism with the capability of Decision Support
System (DSS) in order to address these issues through effective utilization, management and
planning of natural resources in the region. Geospatial based solutions can be developed in
an integrated environment to help decision making, strategic planning, effective resource
management and operations management. For example, NER of our country is highly prone
to all the major kinds of natural disasters such as flood, landslides, earthquakes, forest fire
etc. Hence, an emergency information system can save many people’s life during disaster by
providing relevant information in real time. There are many areas where an efficient
information dissemination system can play an important role. However, while realizing the
above, the location specific needs and demands as well as gap areas to be identified first and
prioritized accordingly.
This article presents a novel geospatial framework which can provide need & demand
based, location specific, accessible and affordable inputs specific to the requirements for
2 0 1 2
preparing Detailed Project Report (DPR), master plan document etc., for planning various
J U N E
developmental activities in the district by the Government.
Geospatial Applications and Information Systems
The Information and Communication Technology (ICT), Remote Sensing (RS), and
-
Geographic Information System (GIS) are technologies that are creating new vistas for connecting
people, to obtain and disseminate information and to bring about a new revolution in many
B U L L E T I N
developmental sectors. With rapid advancements in geospatial technologies, it is now possible
to use and analyze diverse map information, which are vital to make sound decisions at the
local or regional level planning, implementation of various government developmental schemes
and action plans, infrastructure development, disaster management support and industry
development. Over the last decade, India has produced a rich repository of natural resources
N N R M S
information through systematic topographic surveys, geological surveys, soil surveys, cadastral
surveys, various natural resources inventory programmes and the use of the remote sensing
images. Moreover, fast computers and ICT technologies provide ways and means for supplying
spatial data to the users on their desktops. Recently, the development of free and open source
software has experienced a boost over the last few years. The variety of Free and Open Source
Software (FOSS) that can be found on desktop computers ranges from word processors to
scientific applications. In the GIS domain, the widespread use of FOSS is apparent as well
(Steiniger and Bocher 2009). The past decade has witnessed a steady growth of Open Source
software usage in industry and academia, leading to a complex ecosystem of projects. Web and
subsequently GIS have become prominent technologies, widely adopted in diverse domains
(Andrea et al., 2011).
The launch of Google Earth map services in 2005 has brought countless opportunities
for communities around the world to have free access to easy-to-use and browser-based Web
mapping functionalities as well as high quality geospatial data. Bhuvan, a geoportal developed
by Indian Space Research Organisation (ISRO) gives a gateway to explore and discover earth in
3D space with specific emphasis on Indian region. GIS repository on natural resources
information for the whole country is made available through the National Natural Resources
Information System (NNRMS) programme of ISRO to enable triggering of many national level
projects. India Water Resources Information System (India-WRIS) project, a joint venture of
ISRO and Central Water Commission (CWC), aims to develop an online, user friendly application
that shall bring all information related to the water resources of the nation at single window.
Similarly, the prime objective of Bhoosampda portal of ISRO is to disseminate land use and
land cover information derived from IRS data for the whole country. Indian Bioresources
Information Network (IBIN) contains information on diverse aspects of bio-resources of the
country. ISRO’s Village Resources Centre (VRC) aims to promote a single window delivery of
need-based services in the areas of education, health, nutrition, weather, environment,
agriculture and alternate livelihoods to the rural population. Similarly, Agriculture Planning
and Information Bank (APIB) portal is a single window information kiosk to access knowledge
related to agriculture and allied sectors that are useful for the farmers, extension personnel
and planners. In addition, ISRO’s Decision Support Centre (DSC) portal contains relevant
information on major natural disasters viz., flood, cyclone, agricultural drought, forest fire,
earthquake and landslide.
75
Information Systems for NER
North Eastern Space Applications Centre (NESAC) has initiated development of a few user oriented
information dissemination systems using various ICT tools under various projects taken up for the NER. Meghalaya
Natural Resources Information System (MeNRIS) developed for the state of Meghalaya using open source GIS
tools ensures the availability of district level inventory of natural resources information in spatial format as per
NNRMS standards with proper linkages to other socio-economic data and enable customized retrieval and
analysis of data for specific needs. Road Information System (RIS) developed for few priority states of NER such
as Meghalaya, Tripura, Nagaland and Manipur has the objective of creating up-to-date digital database of road
connectivity, accessibility along with various public utility services to improve efficiency in monitoring,
management, planning and subsequent development of the road network. Sericulture Information Linkages &
Knowledge Systems (SILKS) portal developed under ‘Applications of RS and GIS in sericulture development’
project, funded by Central Silk Board (CSB), Ministry of Textiles, Government of India contains necessary geospatial
inputs and recommendations for development of sericulture in the region. DSS developed for Election Management
for the state of Meghalaya is found to be a real management tool for conducting election in number of ways viz.
examining numbers of electors in polling stations, location of emergency services, preparing transportation
budget, manpower deployment etc., in the last election to Assembly and Parliamentary Constituencies. North
Eastern District Resources Plan (NEDRP) for North Eastern Region (NER) has been initiated for entire NER in order
to enable a consistent repository of spatial information at grass root level to reduce duplication of effort among
users & agencies, improve quality and reduce costs related to spatial information to make spatial data more
accessible to the users.
The purpose of this article is to describe a novel geospatial framework for DSS with an effective information
dissemination mechanism developed using FOSS. It gives more emphasis on recently launched NEDRP programme
in the state of Meghalaya highlighting its objectives, approach, geoportal architecture and utilization.
North Eastern District Resources Plan (NEDRP)
NEDRP is an initiative of NESAC in collaboration with North Eastern Council (NEC), Shillong with an
objective to make use of geospatial inputs for preparation of DPR, Master plan document and planning of
various developmental activities taken up by the District Administration as well as various line departments and
to help local level planning. The prime objective of NEDRP is to provide reliable, relevant, up-to-date and
affordable information to the district administration and the concerned line departments for planning various
kinds of developmental activities in their district in a user friendly environment.
Under NEDRP programme, entire seven districts of Meghalaya has been covered in Phase-I and 25
selected districts from remaining NER states will be taken up in the Phase-II. NEDRP portal is planned to be
deployed in a standalone mode in each of the district headquarters of NER with a dedicated computer with
printer and trained manpower to handle NEDRP portal. This model helps to disseminate relevant geospatial
inputs to the hands of authentic users even in spite of lack of internet facility in some of the rural district
headquarters of NER.
Objectives
NEDRP aims to provide need & demand based location specific geospatial inputs in the form of DSS,
developed using open source GIS packages & standards. This is unique in nature which has been realized
through the following tasks:
•
To identify the needs of geospatial inputs for district administration and concerned line departments
towards preparation of DPR, master plan document etc. through proper awareness programme and
demonstration of geospatial applications.
2 0 1 2
•
To generate need based location specific geospatial inputs for land and water resources
action plans.
To develop a single window geoportal to integrate geospatial inputs for various action
J U N E
•
plans with necessary geoservices using open sources GIS software packages.
To deploy NEDRP portal in each of the district headquarters with a system, printer and
-
•
dedicated manpower to operate and act as an authentic gateway for various data
B U L L E T I N
generating agencies to share the information across various government departments,
NGOs, academies and industries.
Requirements of DPR: An Example
DPR and master plan document are the actual basis for implementation of any
N N R M S
developmental schemes or plans of Government. In general, a DPR gives information on project
overview, general description of project area, baseline survey details, institution building and
project management, management and action plans, capacity building, budget etc. Geospatial
inputs forms the core of a DPR along with socio economic data collected through Participatory
Rural Appraisal (PRA) survey. For example, DPR of integrated watershed management programme
is normally used to prepare through baseline/bench mark survey for physiography, climate,
soil, land use, vegetation, hydrology and socio economic data analysis. Considering the
requirements specific to the preparation of DPR or master plan document and understanding
the location specific needs of users, the information contents of NEDRP portal has been finalized.
However, NEDRP portal allows to integrate additional information in a user friendly environment
as and when required. The next section gives details of information modules of NEDRP portal.
Information Modules
Information content of NEDRP is
categorized into three major modules;
administrative, natural resources and
action plans module. Administrative
module
contains
information
on
administrative boundaries, headquarters,
roads, health centers, police setup,
polling areas, polling stations etc.
Similarly, natural resources module is
comprised of land-use/land cover,
Fig. 1: Work flow of NEDRP
wastelands,
generated
wetlands,
under
soils
various
etc.,
projects
coordinated by NESAC, in close collaboration with other ISRO/DOS Centers as well as Central/
State agencies in the NER. Finally, action plan module has been prepared based on the
requirements for preparation of DPR etc., which contains information related to inputs for
preparing action plan for land resources (Agriculture, horticulture, aforestation, culturable
wastelands, land capability etc.), water resources (ground water prospect, watershed
prioritization, soil erosion, identifying location for check dam, etc.) and infrastructures. Apart
from these, district and block level socio economic data are linked with each of the thematic
information. Statistical records of each of the thematic information in the form of table as well
pie chart are also incorporated. Incorporation of climate information in the form climatic zones
77
of Meghalaya helps preparation of
suitability maps for various crops.
Approach
Natural resources (NR) database
containing information on current land
use,
wastelands,
wetlands,
soil,
watershed, crop, infrastructure etc.
prepared under various national, user
funded projects etc. are made available to
Fig. 2: Flowchart generating inputs for land resources action plan
NEDRP at district (1:50K) level as per
NNRMS standard with a proper linkage to
socio-economic data (Figure 1).
District developmental planning
inputs on infrastructure development,
utilization of wastelands, cropping pattern,
soil crop suitability, forest working plan,
soil conservation, watershed prioritization,
etc., have been derived from the NR
database using various GIS analysis and
modeling tools (Figure 2) as per Integrated
Fig. 3: Land resources map
Mission for Sustainable Development
(IMSD) guidelines. The information, as and
when updated, is planned to be made
available from a recently launched Space
Based
Information
Decentralized
Support
Planning
for
(SIS-DP)
programme at 1:10,000 scale and
networked for easy access at Panchayat
level for decentralized planning in various
areas viz., watershed development plan,
aforestation
and
soil
conservation
measures etc.
Fig. 4: Inputs for agro-forestry action plan
A few examples of action plan
inputs for implementation of various
developmental activities under land
resources and agro-forestry of East Khasi
Hills district of Meghalaya are highlighted
in Figure 3 and Figure 4 respectively. Geospatial inputs are also incorporated into
NEDRP for the block level developmental
activities; an example of input for defining
horticultural
action
plan
for
the
Mawsynram block of East Khasi Hills
Fig. 5: Action plan inputs for horticulture
district is shown in Figure 5. Finally, open
2 0 1 2
source GIS software has been used for
integration of all these information in a
J U N E
web-based, single window user friendly
graphical interface, which will enable user
to view and query the NR database as per
of
NEDRP
B U L L E T I N
Architecture
Geoportal
-
their requirements.
The NEDRP geoportal employs
open source packages which have been
used to customize and develop several GIS
N N R M S
tools for web based spatial data
visualization, analysis and data retrieval.
The GIS with robust web technology
allows access to dynamic geospatial
Fig. 6: Architecture of NEDRP Geo-portal
information without burdening the users
with complicated and expensive software.
Open Source Web-based GIS systems such as the University of Minnesota MapServer are available free
of cost; however, due to the complex process of their customization, they require GIS experts with the
knowledge of digital mapping, encoding and transfer protocol (Miller 2006, Rinner et al. 2008). The
architecture adopted for development of NEDRP Geo-portal is depicted in Figure 6. The system
architecture of NEDRP incorporates the Database server (PostgreSQL), Spatial Database Engine (PostGIS),
Web server (Apache), Web Map Server (UMN Map server), and the programming environments (PHP).
The NEDRP Geo-portal has all the necessary tools for spatial data visualization, navigation, analysis,
querying and print/download in an user
friendly graphical interface.
A simple web browser is what a
client needs. A more advanced querying
with SQL-like has also been integrated into
the system so that user can query inside
any given spatial layer and get the results
display in the form of map as well as
tabular data, which later can be
downloaded as spatial data or printed out
in pdf. or jpeg format.
Fig. 7: Home page of NEDRP Geo-portal
Figure 7 is showing the home
page of NEDRP geo-portal; land resource map of East Khasi hills district overlaid with block boundary
is depicted in the home page along with geo-navigational and query tools.
Conclusion
District administration of every district ensures proper coordination and monitoring for
implementation of various developmental schemes and plans such as MGNREGS, Watershed
development, construction of rural roads programme, Rajiv Gandhi National Drinking Water Mission,
etc. They also act as a nodal agency to establish linkages with the line departments, private enterprise,
79
society and citizens for effective utilization and management of natural resources in the district. The main
objective of deployment of NEDRP portal, in each of the district headquarters, is to provide authentic geospatial
inputs to the district administration as well as concerned line department for preparing their DPR, master plan
etc., to implement above mentioned developmental schemes and plans. NEDRP can further strengthen Disaster
Risk Management Programme coordinated by every district administration through relevant geospatial
information, which can be used for preparation of optimum preparedness, mitigation and response plans to
minimize the loss of life and property during any disaster. NEDRP can also play as a real management tool in
making more practical policies while conducting the election processes viz. in examining numbers of electors in
polling stations, location of emergency services, preparing transportation budget, manpower deployment etc.
At present, NEDRP portal has been deployed in two district headquarters of Meghalaya in a standalone
mode. Since geoprocessing services of NEDRP are demand oriented and need based, many line departments
have come forward to have this portal in their office premises in addition to the portal available in the district
headquarters, due to its user friendliness and independentcy from third party commercial software apart from
rich information contents. It is also planned to incorporate various models in open source environment into the
NEDRP portal so that user can prepare their action plans as per IMSD guidelines by integrating relevant NR
layers. In addition, geospatial inputs generated at a much higher scale (1:10K) under SIS-DP programme can be
made available through NEDRP for reaching up to the grass root level users.
References
Andrea, B., Ali, T., Gavin, M. and Michela, B. (2011). A Comparison of Open Source Geospatial Technologies for
Web Mapping. Int. J. of Web Engineering and Technology, 6(4): 354-374
Diwakar, P.G. and Mayya, S.G. (2010). ICT and Geomatics process tools for community centre Watershed
Development. J. of Geomatics, 4(1):25-30
Miller, C. C. (2006). A beast in the field: The Google Maps mashup as GIS/2. Cartographica 41(3): 187-99
Neog, A.K. (2006). WTO and agriculture development in backward regions. In: Changing Agriculture Scenario in
North East India (Eds.: B.J Dev and B.Ray Dutta), Concept publishing Company, New Delhi
Rinner, C., Kessler C., and Andrulis S. (2008). The use of Web 2.0 concepts to support deliberation in spatial
decision-making. Computers, Environment and Urban Systems 32(5): 386-95.
Steiniger, S. and Bocher, E. (2009). An overview on current free and open source desktop GIS developments. Int.
J. of Geographical Information Science 23(10): 1345-1370
APIB: http://megapib.nic.in
Bhoosampda: http://applications.nrsc.gov.in
Bhuvan: http://bhuvan.nrsc.gov.in
DSC: http://www.dsc.nrsc.gov.in
IBIN: http://www.ibin.co.in
India-WRIS: http://www.india-wris.nrsc.gov.in
MapServer: http://mapserver.org
NNRMS: http://www.nnrms.gov.in
VRC: http://isro.gov.in/scripts/villageresourcecentres.aspx
Introduction
The multiplicity of forest products and their uses, and the conflicts it may
cause among the stakeholders and its long gestation period makes forestry planning
to be considerably complex. Information need in forestry basically involve characterizing
the location, area, and status of the forest resources / wildlife and the change in
spatial and time domain. These information needs are not met entirely by traditional
techniques because those were not practical or economically sound to devote more
effort to human intensive menstruation activities. Indian forests are managed through
the working / management plans, which are revised once in 10 years. Working plans
are mainly oriented towards the production forestry and implemented in forest
divisions, whereas management plans are focused on protection forestry and applicable
to protected areas like National Parks, Tiger Reserves and wildlife sanctuaries.
Working Plans and Management Plans
Working Plans are the instruments for scientific forest management. They are
useful in evaluating the present status of natural resources, assessing the impact of
past management practices and deciding management interventions to achieve the
objectives of the working plan. The conventional method of making inputs for working
plan using ground survey is tedious process and time consuming. The treatment map,
which is prepared based on ocular estimation results in rather inaccurate and inconsistent
treatment types. Each division has hundreds of coupes due for working each year hence
the above task is assigned to many persons with varying degrees of experience and
knowledge. The plan officer generally bases his calculations on rough estimations, as
he does not have very accurate and consistent slope /soil/ density/ stock maps to work
with. In addition to this, area calculations are made using the planimeter and graph
paper method. Hence, there is every likelihood of manual error. Moreover the input is
rather fixed and it is almost impossible to generate many alternate and new maps using
the features of the original map (Rao et al., 2007).
For scientific management of protected areas (PA), Management Plans are
prepared. The basic difference between forest Working Plan and Management plans
in India lies in their objectives. While working plan is based on the principle of
sustainable harvesting of forest resources and increasing productivity of forests, a PA
Management Plan includes prescriptions for non-consumptive management of crucial
habitat units such as – food, water and cover and aims at maintaining diversity of
2 0 1 2
J U N E
N N R M S
Varghese AO, Arun Suryavamshi, Rajashekhar SS1, Joshi AK and Krishnamurthy YVN1
Regional Remote Sensing Center – Central, Nagpur
1
RC Office, NRSC, Hyderabad-500625
B U L L E T I N
-
DECISION SUPPORT SYSTEMS FOR
PRODUCTION AND PROTECTION
FORESTRY
species and habitats in order to maintain ecological processes and functions Management Interventions. Hence,
the digital database and applications will be entirely different in Forest Management Information System (FMIS)
and Protected Area Management Information System (PAMIS).
The forest management / working plans require timely and accurate geospatial information about
forest condition, wildlife status and management practices at site specific and regional scales. Geospatial
technologies, such as remote sensing, Geographical Information System (GIS), Global Positioning System (GPS)
provides vital support to collect, analyze and store all sort of geospatial information ((Rao et al., 2006). Though
commercial packages facilities to customize the above modules in their own environment, exorbitant cost of
such an approach becomes a limiting factor for providing the utilities to a large number of users.
To tackle this problem two-standalone packages, Forest Management Information System (FMIS) and
Protected Area Management Information System (PAMIS), have been developed by customizing the packages on
low cost third party libraries, which can cover all the functionality envisaged in the system. FMIS is information
and decision support system for working plan department and PAMIS is for protected areas like National parks,
wildlife sanctuaries and tiger reserves. These information systems incorporate the functionality to view, query
and analyse the data as per the requirements of users. Further, report generation utilities for providing inputs to
management plan as per the need and convenience of the user department is developed and integrated into the
system. This will enable any time anywhere availability of data, analysis and visualization system at the different
hierarchy of the forest department.
Development of Information Systems
The database for working / management plans has been organized by incorporating inputs from various
sources. The data from various sources needs to be systematically organized into the database by following
proper data structure and coding standards. A standard GIS and Digital Imaging package have been used for
generating spatial data for infrastructure, terrain and multi-thematic spatial resource data like forest type,
crown density, soil etc. from satellite data. Non-spatial data like wildlife census, regeneration status of trees, fire
history, Non-wood forest produce collection, socio-economic data from each settlement have been converted to
spatial domain.
These information systems are designed with administrative boundary as basic element (Range / round /
beat / compartment) for processing. Present system provides the inputs in a more efficient and interactive way
for working plans /management plans by integrating remote sensing and GIS based data and methods. For
extracting information from spatial and non-spatial databases the package should be robust enough to cater to
all needs. For which standalone and cost effective software was preferred to fulfill the user requirement. This
has been developed using third party component map objects and Visual basic based GUI with support of
redistributable microsoft access database and runs on windows platform. ESRI Map Objects 2.1, is third part
component tool which is cost effective and fulfill the requirements of this system for GIS purpose. Thus this
system is free of any dependency on any commercial tool or component which has some licensing issue. Entire
package is self-installable along with the necessary data that is customized for different forest divisions. Minor
variations in the functionality are also provided to different departments to personalize the usage in accordance
with routinely used methods and data. A tool for automatic LUT generation is also designed to provide effective
and fast integration of datasets. Software requires a minimum of 256 MB RAM and 100 MB hard disk space with
more disk space in accordance with the data of a particular region
Forest Management Information System (FMIS)
FMIS can be successfully used for the generation of various inputs for the preparation of forest working
plan (Figure 1). Generation of stock map; preparation of treatment types; zonation of working circles, felling
2 0 1 2
series & coupes based on treatment site
suitability analysis for coupe operations
J U N E
& site specific treatment; suitability
analysis for plantations (Teak plantation /
miscellaneous plantation), selection
tending,
water
harvesting
-
felling,
structures; generation of grazing closure
B U L L E T I N
& fire control areas etc can be achieved
with greater accuracy, timeliness and costeffectiveness with the use of FMIS than
the conventional method. Integrated
layers of administrative boundaries, slope,
quality
Fig. 1: Main interface of FMIS with the display of site suitability
and
non-spatial
data
N N R M S
drainage, forest type, density, soil, site
like
regeneration, stand volume and density
(Figure 2) would help the managers for
identifying suitable areas for site-specific
treatment and identifying the potentials
& limitations of each compartment.
Software modules incorporating the
functionality to view, query and analyse
these data as per the requirements of users
has been developed. Further report
generation utilities for providing area
statistics as per the need and convenience
of the user department is developed and
integrated into the system (Figure 3). This
will enable any time anywhere availability
of data, analysis and visualization system.
FMIS application software is designed with
Fig. 2: Graphic representation of non-spatial data for compartments in FMIS
user friendly GUI especially targeting all
levels of users ranging from highly
computer literates to novice users who can
be trained with simple click and get
information (Rajashekar et al., 2009).
Forest Management Information
System encompass data visualization
module for visualizing administrative units,
natural resources as per the coding
standards for each theme. Charting
module provides an insight into the
present condition of girth volume in
comparison to the ideal growth. Reporting
module provides administrative boundary
wise analysis of the parameters like
Fig. 3: Area statistics generation in FMIS
density, slope, treatment types, and
83
suitability for various silviculture practices
categories along with area covered by
each. Since this is available on the fly for
any area, preparing inputs for site-specific
operations
is
made
faster,
more
affordable, more accurate with RS inputs.
Protected Area Management
Information System (PAMIS)
PAMIS
incorporate
the
functionality to view, query and analyze
the data as per the requirements of users.
Further, report generation utilities for
providing inputs to management plan as
per the need and convenience of the user
department is developed and integrated
into the system. This enables any time
Fig. 4: Application main window of PAMIS with the display of territories of tiger
and leopard
anywhere availability of data, analysis and
visualization system at the different
hierarchy of the forest department (Figure 4).
PAMIS provides information like,
relative spatial abundance of wild
animals, risk factors, proximity to risk
factors and sensitivity categorization,
human influence zone, fire history, fire
frequency, fire prone areas, species-wise
NWFP collection pressure, territories of
major carnivores (Figure 5), distribution of
herbivores, primates, ungulates and
rodents, diversity & density of species,
schedule-wise distribution (Indian Wildlife
Fig. 5: Integration of database and auto LUT generation I n PAMIS
Protection Act) of species generated from
wildlife census data. This information can
be modeled with spatial data created for
infrastructure, terrain and multi-thematic
spatial resource data like forest type,
crown density, soil etc. by remote sensing
for deriving suitable area for a site specific
purpose. Analysis module is implemented
with multi layer support and having
capability of archiving generated analyzed
outputs (Figure 6). Such generated
analyzed outcomes are indispensable
during field visit (Figure 7). So printing
Fig. 6: Criteria based analysis in PAMIS
facility is an additional asset of this system.
2 0 1 2
Other advantages of the system
are the facility to integrate non-spatial
generated
by
modeling
and
J U N E
data directly, archival of the output
map
composition of the out put. Furthermore
-
PAMIS is helpful for studying the manwildlife conflict, prey base for a particular
B U L L E T I N
carnivore, prey-predator relationship,
territorial overlap of major carnivores,
prioritizing the inside settlement for
relocation, evaluation of the release /
relocation of wildlife, spices recovery
programmes etc.
N N R M S
Fig. 7: Output map window in PAMIS
This information system has been successfully used by Melghat Tiger Reserve officials
in Maharashtra forest department for identifying suitable areas for watchtowers (Prasanth
et al., 2009) protection huts, waterholes and salt licks (Ashwini et al., 2009)
Conclusion
FMIS and PAMIS are powerful planning tool for the working plan / management plan
officers and are very potent decision support and monitoring tools for the implementing
managers in the real time mode. In these packages they can make innumerable queries to find
answers to their day-to-day management questions. FMIS has been installed in East Melghat,
West Melghat and Akola Forest divisions of Maharashtra Forest Department and they are
successfully using the software in routine basis at different administrative levels for site-specific
treatments. Likewise PAMIS has been installed at Melghat Tiger Reserve and integration of the
database is going on for Great Indian Bustard Sanctuary, Maharashtra.
Acknowledgements
We place our sincere thanks to Dr. V.K. Dadhwal, Director, NRSC, Hyderabad for his
constant encouragement and valuable suggestions. We are extremely thankful to
Shri M.K. Rao, CCF, Maharashtra Forest Department for entrusting this study to RRSC, Nagpur
and providing the necessary funds. We are thankful to Shri Shantanu Bhatwadekar for his
guidance and constant encouragement during the course of the work.
References
Ashwini, S.P., Prasanth, D.K., Varghese, A.O. and Joshi, A.K., (2009). Sensitivity categorization
and management of forest protection huts using Geoinformatics. In Proc. of the ISRS Symposium
on Advances in Geo-spatial technologies with special emphasis on sustainable rainfed
Agriculture, 17-19, September 2009, Nagpur.
Prasanth, D.K., Ashwini, S.P., Varghese, A.O. and Joshi, A.K., (2009). Mapping of forest fire risk
zones and identification of suitable sites for fire watchtowers using remote sensing and GIS, In
Proc. of the ISRS Symposium on Advances in Geo-spatial technologies with special emphasis
on sustainable rainfed Agriculture, 17-19, September 2009, Nagpur.
Rajashekar, S.S., Varghese, A. O., Suryavanshi, A., Joshi A.K. and Krishna Murthy Y.V.N., (2009).
Forest Management Information System (FMIS): An integrated approach to the management of
85
forest working plan divisions. In Proc. of the ISRS Symposium on Advances in Geo-spatial technologies with
special emphasis on sustainable rainfed Agriculture, 17-19, September 2009, Nagpur.
Rao, M.K., Varghese, A.O. and Krishna Murthy Y.V.N., (2006). Use of Geospatial database in Sustainable Forest
Management, In proc. of ISPRS International Symposium on Geospatial database for sustainable development,
27 – 30, Sept. 2006, Goa, India, WG-IV-3-12.
Rao, M.K., Varghese, A.O. and Krishna Murthy Y.V.N., (2007). Remote sensing and GIS inputs for Working Plan
preparation, Indian Forester, 133 (1a): 65-76.
Introduction
Flood Hazard Zonation (FHZ) is one of the most important non-structural
measures, which facilitates appropriate regulation, and development of floodplains
thereby reducing the flood impact. The recurrent flood events at frequent intervals
demand the need for identification of flood hazard prone areas for prioritizing
appropriate flood control measures. A flood hazard map is considered as a preliminary,
yet necessary input for all regional development policies. In developing countries,
where a large proportion of the population lives in the flood prone areas, flood
hazard maps are very useful for the administrators and planners to identify areas of
flood hazard and prioritize their mitigation efforts (Sanyal and Liu, 2006). However, a
formidable challenge in carrying out realistic assessment of flood hazard zonation
and formulation of effective remedial measure is the lack of reliable and up-to-date
hydrological database (Goswami, 1998), availability of high resolution DEMs and
good network of hydrological observations (Sanyal and Liu, 2004). Conventional
method of preparing flood hazard maps requires extensive field surveys and latest
information about flood plain, flood duration, river configuration, etc., required to
be incorporated which is a time consuming, complex and expensive task. In this context,
remote sensing technology, because of its cost-effectiveness and capacity to provide
near real-time data, has emerged as an indispensable tool in the field of flood disaster
management. Remote sensing technology is being widely used especially in the
developing countries for flood inundation mapping, flood damage assessment and
flood hazard zonation studies (Rao et.al., 1998; Islam and Sado 1998; Islam and Sado
2000; Venkatachary et.al., 2001; Jain and Sinha, 2003; Sanyal and Liu, 2004; Sankhua,
et al. 2005; Jain et al., 2005; Bhatt et.al., 2010; Bhatt et.al., 2011).
Decision Support Centre (DSC) of National Remote Sensing centre (NRSC),
ISRO has prepared Flood Hazard Atlas for Assam State using more than 90 satellite
datasets acquired from Indian Remote Sensing (IRS) and Radarsat satellites during
flood season over Assam region for last 10 years (1998-2007). The Flood Hazard Atlas
has been released (Figure 1), which shows the state level flood hazard map
(http://ndem.nrsc.gov.in/hazard)
Flood Problem in Assam Region
The state of Assam having a total geographical area (TGA) of about
78,438 sq. km. and situated between 90-960 East Longitude and 24-280 North
latitude forms part of the Brahmaputra basin. The basin lies within the monsoon
2 0 1 2
J U N E
B U L L E T I N
Sharma SVSP, Srinivasa Rao G, Bhatt CM, Manjusree P & Bhanumurthy V
Disaster Management Support Division, National Remote Sensing Centre
Indian Space Research Organization (ISRO), Hyderabad-500 625
N N R M S
DEVELOPMENT OF FLOOD HAZARD
MAPS FOR ASSAM STATE, INDIA
USING HISTORICAL MULTITEMPORAL SATELLITE IMAGES
rainfall regime receiving an annual rainfall
of about 230 cm. Monsoon rainfall from
June-September account for 60-70
percent of the annual rainfall in the region.
The Brahmaputra river along with its host
tributaries causes devastating floods
almost every year with colossal loss and
damage to the infrastructure and
environment in the state. Despite many
efforts
undertaken
to
tame
the
Brahmaputra river, it continues to wreck
havoc through uncontrollable floods year
after year (Kotoky et.al., 2005). The unique
environmental setting i.e. the eastern
Himalayas, highly potential monsoon
regime, and accelerated rates of erosion,
Fig. 1: Flood hazard map of Assam.
rapid channel aggradation, deforestation,
intense land use pressure and high
population growth especially in the
floodplain belt, are some of the dominant
factors that cause recurrent floods in the
state of Assam (Goswami, 1998, Kotoky
et.al., 2003; Kotoky et.al., 2005; Kotoky
et.al., 2009; Singh 2006). IRS AWiFS
satellite imagery showing Brahmaputra
valley and the Brahmaputra River during
the pre-flood season (top image) and
during the flood season (bottom image)
are shown in Figure 2.
Methodology
Generation of Flood Hazard Layer:
The flood inundation layers generated from
the analysis of the satellite data for different
flood waves in a calendar year were
integrated in GIS environment to generate
the maximum flood inundation extent
observed in that year. The maximum flood
inundation layers corresponding to various
years (1998-2007) were integrated for
assessing the frequency of inundation and
subsequent generation of flood hazard layer.
Figure 3 shows the methodology for
generation of flood hazard maps. The
hazard layer represents the number of times
Fig. 2: IRS AWiFS satellite image showing Brahmaputra valley and the Brahmaputra river
during the pre-flood season (top image) and during the flood season (bottom image).
a given area is subjected to flooding during
the last 10 years.
2 0 1 2
The flood hazard has been classified into five categories based on frequency of
inundation (Table 1). ‘Very Low’ category indicates the areas which are inundated once or twice
J U N E
during the 10-year period. Similarly, ‘Low’ indicates three to four times, ‘Moderate’ indicates
five to six times, ‘High’ indicates seven to eight times and ‘Very High’ indicates the areas, which
are regularly subjected to inundation. Area under each category was estimated and flood
-
hazard maps at state and district level were prepared. Further, cropped area (from land use) was
also integrated with flood hazard layer to assess the impact. The statistics for cropped area
B U L L E T I N
affected under each hazard category was computed.
Table 1: Flood hazard classification and colour coding scheme adopted
Flood Hazard
Classification
Number of times / years the area
was subjected to flood
inundation during 1998-2007
Colour Coding
1
Very Low
1-2 times
Very Low
2
Low
3-4 times
Low
3
Moderate
5-6 times
Moderate
4
High
7-8 times
High
5
Very High
9-10 times (almost every year)
N N R M S
Sl.No
Very High
Database Integration: The flood hazard
layer was further integrated with the
database consisting of administrative
boundaries, land use/ land cover,
infrastructure, etc. for estimation of
district-wise area under different flood
hazard categories.
Flood Hazard Index: Further an attempt
was made to find the severity of flood
hazard in various districts, using the
following flood hazard index.
1. Weightages were given to each category
Fig. 3: Methodology for generation of flood hazard maps
of flood hazard (H) viz. very high-5, high4, moderate-3, low-2 and very low 1.
2. Weightages were also given as per the percentage of flood hazard area (A) in the district viz.,
0-10%=1, 11-20%=2, 21-30%=3, 31-40%=4, 41-50%=5, 51-60%=6, 61-70%=7, 71-80%=8,
81-90=9 and 91-100%=10.
3. Flood hazard index is derived for each district by using following formula
Flood hazard Index = Σ H x A
Map Composition: Flood hazard maps were composed at State, District and detailed levels
comprising of base information and flood hazard layer. Detailed level maps were prepared by
superimposing the village boundaries along with the submerged road and rail network. To
facilitate better visualization, a colour coding scheme was adopted for different hazard zones
89
based on their frequency of inundation
(Table 2). Figure 4 shows the district and
detailed level flood hazard zonation map
for Nowgong district.
Cropped Area under Various Flood
Hazard Zones: The cropped area
(consisting of kharif, double/triple crop
categories) was extracted from the land
use / land cover information (generated
under ISRO-NRC project using 2006-07
satellite data) and integrated with the
various flood hazard categories to
compute the cropped area affected by
flood hazard.
Validation of Maps: The flood hazard
maps prepared were provided to Assam
State Disaster Management Authority
(ASDMA). Subsequently these maps
were
distributed
to
each
district
administration through ASDMA and
feedback was obtained.
Results and Discussion
• It is observed that about 22.21 lakh
hectares of land constituting about
28.31% of total geographical area
(TGA) of Assam state is affected by
flooding (Table 2).
• Out of the total flood affected area of
22.21 lakh hectares, about 1.28 lakh
hectares of land falls under very high flood
hazard category. This area is observed to
be continuously under submergence
during the last ten years period.
• It is observed that about 2.24 lakh
hectares of land falls under high flood
hazard category, indicating that this area
Fig. 4: District and detailed level flood hazard zonation map for Nowgong district
has been subjected to flooding for about
7-8 times during last ten years.
•
Area falling under moderate flood hazard category (area subjected to inundation 5-6 times during
last ten years period) is estimated to be about 3.51 lakh hectares, constituting about 4.48% of TGA of
Assam state.
2 0 1 2
Hazard Severity
Flood
Hazard Area (ha)
1
Very High
1,28,687
2
High
3
% Flood Hazard
(with respect
to State
Geographic Area)
% Flood Hazard
(with respect to
Total Flood
Hazard Area)
Crop Area Under
Different Flood
Hazard
Categories (ha)
1.64
5.79
83488
2,24,629
2.86
10.11
168802
Moderate
3,51,667
4.48
15.83
270558
4
Low
4,91,761
6.27
22.14
351356
5
Very Low
10,24,584
13.06
46.13
621367
TOTAL
22,21,328
28.31
100.00
14,95,571
N N R M S
•
B U L L E T I N
-
Sl No
J U N E
Table 2: Flood Hazard Area under Various Categories
It is estimated that about 4.9 lakh hectares of land falls under low flood hazard severity
(area subjected to inundation 3-4 times during last ten years period) whereas 10.24 lakh
hectares of land is under very low flood hazard category (area subjected to inundation
1-2 times during last ten years period).
•
Nalbari, Marigaon, Darrang, Lakhimpur and Dhemaji are the worst flood affected districts
•
About 14.95 lakh hectares of cropped area is affected by flooding out of which 2.52 lakh
hectares falls under very high to high flood hazard category.
Conclusions
Flood hazard maps prepared for the state of Assam based on satellite remote sensing data for
last one decade (1998-2007) is the first step of its kind undertaken for better and effective
flood management in the state. The flood hazard maps prepared using the satellite images
acquired over the last one decade can be a critical scientific input in planning integrated basin
flood management programme as a long term non-structural measure against recurrent floods.
District level flood hazard maps can be utilized by the administrators for planning district
development plans by landuse regulation, suggesting alternative crop combinations,
undertaking flood proofing measures, initiating flood insurance programs and increasing
flood disaster preparedness in areas frequently inundated.
References
Bhatt C.M., Rao G.S., Manjushree P. and Bhanumurthy, V. (2011). “Potential of High Resolution
Satellite Data for Disaster Management: A Case Study of Leh, Jammu & Kashmir (India) Flash
Floods, 2010”, Journal of Geomatics Natural Hazards and Risks, 4, 365–375.
Bhatt, C.M., Rao, G.S., Manjushree, P., and Bhanumurthy, V., (2010). Space based disaster
management of 2008 Kosi floods, north Bihar, India, Journal of Indian Society of Remote
Sensing 38, pp 99-108.
Goswami, D.C., (1998). Fluvial regime and flood hydrology of the Brahmaputra River, Assam.
Memoir Geological Society of India, 41, pp.53-75.
91
Islam, M. M. and Sado, K., (1998). Application of remote sensing techniques with a geographic information
system to study flood hazard in Bangladesh. In: Proc. 27th Int. Symp. on Remote Sensing of Environment
Tromso, Norway, 745-748. Norwegian Space Centre, Tromso, Norway.
Islam, M. M. and Sado, K. (2000). Flood hazard assessment in Bangladesh using NOAA AVHRR data with
geographical information system. Hydrol. Processes, 145, pp. 606-620.
Jain, V. and Sinha, R., (2003). Geomorphological manifestations of the flood hazard: A remote sensing based
approach,Geocarto International, 18, pp. 51–60.
Jain, S.K., Singh, R.D., Jain, M.K. and Lohani, A.K., (2005). Delineation of flood-prone areas using remote
sensing techniques, Water Resources Management , 19, pp. 333-347
Kotoky, P., Bezbarauh, D., Baruah, J. and Sarma, J.N., (2003). Erosion activity on Majuli – the largest river island
of the world, Current Science, 84, pp. 929-932.
Kotoky, P., Bezbarauh, D., Baruah, J. and Sarma, J.N., (2005). Nature of bank erosion along the Brahmaputra
channel, Assam, India, Current Science, 88, pp. 634-640.
Kotoky, P., Bezbarauh,, D., Borah, G.C. and Sarma, J.N., (2009). Do node points play a role in flood proliferation?,
Current Science, 96, pp. 1457-1460.
Rao, D.P., Bhanumurthy, V., Rao, G.S. and Manjusri, P., (1998). Remote sensing and GIS in flood management in
India. Memoir Geological Society of India, 41, 195-218.
Sanyal, J and Liu, X. X., (2004). Application of remote sensing in flood management with special reference to
monsoon Asia: A review. Journal of Natural Hazards,pp. 288-301.
Sanyal, J and Liu, X. X., (2006). GIS based flood hazard mapping at different administrative scales: A case study
in Gangetic West Bengal, India. Journal of Tropical Geography, pp 207-220.
Sankhua, R.N., Sharma, N, Garg, P.K. and Pandey, A.D., (2005). Use of remote sensing and ANN in assessment of
erosion activities in Majuli, the world’s largest river island. International Journal of Remote Sensing ,
26, pp. 4445 – 4454.
Singh, S.K., (2006). Spatial variability in erosion in the Brahmaputra basin: causes and impacts, Current Science,
90, pp. 1272-1276.
Venkatachary, K.V., Bandhypadhyay, K., Bhanumurthy, V., Rao, G.S., Sudhakar, S., Pal, D.K.,
Das, R.K., Sarma, U., Manikiran, B., Rani, H.C.M. and Srivastava, S.K., (2001). Defining a space-based disaster
management system for floods: A case study for damage assessment due to 1998 Brahmaputra floods. Current
Science, 80, pp. 369-377.

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