virany sengtianthr land cover and land use

VIETNAM NATIONAL UNIVERSITY, HANOI
VNU UNIVERSITY OF SCIENCE
_______________________
VIRANY SENGTIANTHR
LAND COVER AND LAND USE CHANGE STUDY
USING REMOTE SENSING AND GIS
FOR SUSTAINABLE DEVELOPMENT
IN SAVANNAKHET PROVINCE, LAO PDR
Branch: Cartography, Remote Sensing and GIS
Code: 62440214
THE EXECUTIVE SUMMARY DRAFT OF DISSERTATION
FOR THE DEGREE OF DOCTOR OF CARTOGRAPHY,
REMOTE SENSING AND GIS
Hà Nội - 2015
The dissertation was performed at : VNU University of Science
Supervisors:
-
Assoc. Prof. Dr. Nguyen Ngoc Thach, Faculty of Geography,
-
Hanoi University of Science, VNU
Assoc. Prof. Dr. Nguyen Dinh Minh, Faculty of Geography,
Hanoi University of Science, VNU
Reviewer 1 : Assoc. Prof. Dr. Pham Van Cu, Faculty of Geography,
Hanoi University of Science, VNU
Reviewer 2 : Assoc. Prof. Dr. Nguyen Cam Van, Institute of Geography,
Vietnam Academy of Science and Technology
Reviewer 3 : Dr. Dinh Thi Bao Hoa, Faculty of Geography,
Hanoi University of Science, VNU
This dissertation will be evaluated by the state evaluation council of
PhD dissertation at : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
at. . . . . .O‟clock . . . . .day . . . . month. . . . . .year . . . . .
This dissertation can be found at:
- Vietnam Natinal Library
- Center of Information-Library, Vietnam National University
- National University of Laos
Introduction
Land cover and climate change are inherently linked and It had become increasingly
important as the national level in Lao PDR. Land cover is devoted to the vegetational and artificial
construction covering the land surface. The study of land cover and land use change is very
importance to have proper planning and utilization of natural resources and management.
Savannakhet is located in the centre of the southern part of Lao PDR and has large forest resources.
There are three types of forest resources namely: national biodiversity conservation area (NBCA),
national protection forest area (NPFA) and production forest area (PFA) including provincial and
district level. The Government of Lao PDR has recognized the incidence of deforestation in the
country. This introduction focus on the situation forest of the Savannakhet Province, Lao PDR. At the
same time the research outline will be presented wich leads to the problems statements and specific
research objective and the research resuilt. The problem statement of this study research can represent
land cover and land use change in Savannakhet province of Lao PDR in particular for the four keys
factors: (1) Currently, the Socio-economic development is rapid development (Department for
Planning and Investment of Savannakhet Province, 2010). Forest degradation is happening in the
province due to urban development such as building construction and logging increasing. (2)
Infrastructure development (e.g. road network, electricity and irrigation project) has further enhanced
the land use change process in the area. (3) Industrial agriculture is relatively new to Savannakhet
Province, as it was only recently introduced by foreign companies, but is expanding rapidly.
(International Union for Conservation of Nature-IUCN, Lao PDR and National Economic Research
Institute-NERI, Ministry of Planning and Investment of Lao PDR, 2011) and (4) Local vegetation
cover change due to investment planning, specifically land cover type change, has significant to
national and global level changes. Therefore, it is important to study the reason change of land use and
land cover to understand the change process. In order to understand the reason of change, it will be
necessary to conduct studies that explicitly reveal the variations in change characteristics. The
identifying relevant geographical and reason of land cover and land use change for sustainable
development in Savannakhet province focused on the relationship between land cover-land use change
and geographical characteristics with minimized the key factor priorities such as: urban, land use
planning, population, ethnic group distribution, road network, river, digital elevation model and soil.
The figure1 is to explain the relationship between land cover and land use change and indicators to
find a reason to change.
1
Figure 1: Indicator and their relationship to find the reason of land cover and land use change
This research will address relevant issues on Land Use and Land Cover changes in relation to
the socio-economy and provide recommendations which may contribute to the sustainability of natural
forests in the study area. The study thus objectives: (1) to classify Land cover classes by using
LANDSAT 2000 and LANDSAT 2013 of Savannakhet province, Lao PDR; (2) to detecting change of
land cover and land use in Savannakhet Province during the period from 2000 and 2013; (3) to
classify dipterocarp forest in Savannakhet province using NDVI index; (4) To establish change map
and analyze relationship between the changes, natural and socio-economic factors during the period
from 2000 and 2013. (5) To predict probability future deforestation to the year 2020, and to forecast
probability reforestation and sustainable development in Savannakhet Province to the year 2020.
The research result will contribute to the Environment Action Plan of Savannakhet Province,
Socio-Economic Development Plan (SEDP) of Savannakhet Province and to enable informed
decision-making, context related policies and visions of concerned stakeholder levels concerning
sustainable development of the Savannakhet Province and also understand the actual situation on the
land cover and land use and related to natural resources in the Savannakhet Province and to provide
technical information sharing such as data network at the national level. To contribute support in the
long term goal of the SEDP and the forest management plan of the Savannakhet province as well as
contributes to the goals of the institutional partner and to enable informed decision-making and
context related policies, contribute to Lao Environment Monitoring Report and next 5 year National
Environmental Action Plan (2015-2020), Natural Resources Management, the Socio-Economic
Development Plan (2015-2020) and also Programes required for development planning.
The dissertation has been prepared in three main chapters.
2
CHAPTER 1: OVERVIEW OF LAND COVER, LAND USE CHANGE AND SUSTAINABLE
DEVELOPMENT
Chapter1: Overview of Land Cover, Land Use Change and Sustainable development
In chapter 1, this chapter has been introduce included the concept of land cover, land use and
sustainable development.
1.1. Land Cover, Land Use and Change
1.1.1 Concepts of Land Cover and Land use
Land cover is the observed (bio) physical cover on the earth's surface. When considering land
cover in a very pure and strict sense it should be confined to describe vegetation and man-made
features. Consequently, areas where the surface consists of bare rock or bare soil are describing land
itself rather than land cover. Also, it is disputable whether water surfaces are real land cover.
However, in practice, the scientific community usually describes those aspects under the term land
cover. (FAO) Land cover is defined as the physical material at the surface of the earth. Land covers
include grass, asphalt, trees, bare ground, water, etc. (ESA). Land use refers to the human activities
that are directly related to the land and Land cover refers to the physical and biological cover over the
surface of land, including water, vegetation, bare soil, and/or artificial structures. (NASA,
http://lcluc.hq.nasa.gov)
1.1.2. Classification of Land Use and Forest Types in Lao PDR, Vietnam and International
Organization
According to the Ministry of Agriculture and Forest, Lao PDR noted that forest and land use
classification applied, was the same classification used in 1982 and 1992 that current forest has
cannopy density from 20 percent and above and if forests have cannopy density below this threshold
they will be classified as other forest types. Land cover classification systems for agriculture are the
most widely developed by international organization (e.g FAO) and The Global Forest Observations
Initiative (GFOI) Methods and Guidance Documentation is intended to provide options and support
to countries in the use of ground observations and remotely sensed data and methodologies for the
establishment of their national Forest Monitoring and Carbon Tracking systems, focused on
addressing Reducing Emissions from Deforestation and Forest Degradation (REDD).There are two
official land use classification systems are operating in Vietnam that the General Department of Land
Administration (GDLA) under MONRE, which focuses primarily on land use management and
planning, and that of Forest Inventory Planning Institute (FIPI) under Ministry of Agriculture and
Rural Development (MARD), which focuses on forest management. This situation creates
inconsistencies between existing land use „categories‟ and inconsistencies in the available forest data
(Hoang, 2010).
1.1.3. Overview of dipterocarp forest in Savannakhet province
Dipterocarp forest (Dipterocarp) is a type of forest with plant species under Dipterocarpaceae
family prevailing. This is a type of thin and clear forest, it is offen located in the region with climate
distinguished into two clear seasons: raining season and dry season. Savannakhet has a large area of
natural forest, especially dipterocarp forest. However, not many studies research about land cover in
this province. Thus, Remote Sensing and GIS method on the Application of Remote Sensing and GIS
in researching dipterocarp forest in Savannakhet Province, Lao PDR was selected for researching
dipterocarp forest to identifying the object of dipterocarp forest by using Remote Sensing, to support
overcome difficulties in the interpretation of the image. The result found that the most Dipterocarp is
3
larges area in five districts namely: Thapangthong district, Songkhone district, Xonbuli district,
Champhone district and Atsaphangthong district.
1.2. Sustainable Development
The term of sustainable development was introduced in 1987 by the Brundtland Commission, a
high-level global entity intended to rally countries to work to pursue this goal together.1 It means of
sustainable development that “meets the needs of the present without compromising the ability of
future generations to meet their own needs.” The United Nations Conference on Environment and
Development (UNCED), or “Earth Summit,” was held in Rio de Janeiro in 1992. Rio declaration on
the environment and sustainable development and adopted the Agenda 21.
1.2.1. Lao PDR
Based on global definitions and principles of sustainable development, in Lao PDR the
Government‟s vision for sustainable development was promulgated in the National Sustainable
Development Strategy (NSDS) 2008, namely, “Achieving the sustainable well-being of all people of
the Lao PDR through the simultaneous and integrated pursuit of a prosperous economy, equitable
society and healthy environment.” Complementing the overarching national goal of graduation from
LDC status by 2020, the NSDS focuses on four areas of sustainability: 1) sustainable economic
development; 2) sustainable social well-being and development; 3) sustainable environmental and
natural resource management; and 4) good governance (MPI and WREA. 2008). The Government of
Lao PDR is committed to rural development and poverty reduction. This includes decentralization and
greater involvement of villagers in bottom-up participatory planning and rural development aiming at
improving the general socio-economy, reducing deforestation and promoting more productive
livelihood systems.
Sustainable Forest Management (SFM) - minimizing degradation of forest area in the progress
of development-is essential for sustainable development of the country. SFM also focus on the
preservation of forest ecosystem, forest protection, productively enhancement and public needs. Lao
PDR needs to establish sustainable forest management frameworks and policies in order to prevent
and stop the forest degradation and sustainable manage forest resources.(MPI and WREA. 2008).
1.2.2. Savannakhet Province
Sustainable development in the context of Savannakhet Province is based on government of
Lao PDR vision for sustainable development in the National Sustainable Development Strategy, 2008.
Provincial Natural Resources and forest management Management aimed at assisting the
Government of Laos in developing sustainable use of natural resources to the benefit of rural and
urban people. To achieve sustainable development, Savannakhet province has implemented of
national social-economic development plan as well as provincial social-economic development plan.
Chapter 2: Methodology of Remote Sensing and GIS for Detecting Land Cover and Land Use
Change
Chapter 2 of this section focus on the remote sensing and GIS, advance RS-GIS method, modeling in
geography related to identify land use-land cover change and spatial statistic analysis method were
used as the main applicable method for this research study.
1
Ministry of Natural Resources and Environment, National Rio+20 Report for Lao PDR.
Vientiane, Lao PDR, 2012.
4
2.1 Overview of Remote Sensing
2.1.1. Remote Sensing
Remote Sensing is the science and art obtaining information about an object, area or phenomenon
though the analysis of data acquired by device that is not in contact with the object, area, or
phenomenon under investigation. (Agarwal, Garg, 2000)
2.1.2. Normalized Difference Vegetation Index-NDVI
NDVI is a numerical indicator that uses the visible and near-infrared bands of the
electromagnetic spectrum. The NDVI is calculated as follows (McFeeters (1996) :
(1)
Where: IR: Infrared (B5) and R: Red (B4)
NDVI values are represented as a ratio ranging in value from -1 to 1 but in practice extreme negative
values represent water, values around zero represent bare soil and values over 6 represent dense green
vegetation.
2.2. GIS
According to (Burrough, 1986) definition GIS is a powerful set of tools for storing and
retrieving at will, transforming and displaying spatial data from the real world for a particular set of
purposes. A Geographic Information System is a computer system for assembling, storing,
manipulating, and displaying data which contains physical locations (geographic coordinates) of
features and information about those features (attribute data).
2.3. Introduction and the Concept of Modeling
This research study conducts in-depth investigation on relation between the forest area change
and residential change and socio-economic factors and develops the variable probability map on the
basis of developing the land cover change map. Spatial statistic analysis method shall be the main
applicable method. Modeling in Geography is the science simulate the phenomenon, the problem of
geography and the forecast changes over space and time.
2.3.1. Cartographic Model
The term cartographic modeling was coined by C. Dana Tomlin and Joseph K. Berry (1979)
to designate the process of using combinations of commands to answer questions about spatial
phenomena. (Michael N., 2009). Modeling in Geography is the science simulate the phenomenon, the
problem of Geography and the forecast changes over space and time.
2.3.2. Structure of Model
In general, there are three types of model elements in the following figure:
5
Figure 2. The structure of the model
1). Input data: Including database and data format. Each element should have a specific, unique theme
that is representative of a single factor or group of factors in the model.
2). Processing: Including the process of receiving input, computation, analysis, evaluation, data export
and storage.
3). Output: expressed in the graphs, tables, end product, and new information.
2.4. Remote Sensing and GIS for Land Cover and Land use Change Study
2.4.1. Basic land cover classification systems
In the Lao PDR forest is defined as a land with woody vegetation with a 20% canopy cover
Vegetation height is >5 in situ or has the ability to attain this height and minimum mapping unit of
0.5 ha. According to the FAO definition, the assessment of the vegetation cover, the upper most
canopy layer was decisive, unless it covered less than 25 % of the area. Trees and bushed were
distinguished by their height. Land cover classification system (LCCS): Woody plants higher than 5 m
were classified as trees and those below as bushed, unless they had a clear physiognomic aspect of
trees. In this case, also woody plants between 3 to 5 m were considered as trees.
2.4.2. Image pre-processing
The data pre-processing is an important step in the land cover and land use process. Before
digital images can be analyzed and usually require some degree of preprocessing. The term
preprocessing is referred to as the correction of geometric and radiometric deficiencies and the
removal of data errors (MATHER 1999). The choice of methods should always be purpose dependent.
For instance, if a check of a certain land cover or object with a satellite image is the aim, visual
interpretation is sufficient and even geometric correction may be omitted (JENSEN, 1996).
2.4.3. Geometric correction of high resolution satellite imagery and mosaicking
Raw remotely data that are collected by sensors on satellite platforms or aircraft are not
directly referenced to known map projection. The goal is to fill a matrix that is in a standard map
projection with the appropriate values from a non-planimetric image. And Output-to-input, or inverse
mapping logic, is based on the following two equations (Jensen, 2004):
x '  a0  a1x  a2 y
y '  b0  b1x  b2 y
(2)
Where x and y are positions in the output-rectified image or map, and x and y represent corresponding
positions in the original input image. The rectified output matrix consisting of x (column) and y (row)
coordinates is filled in a systematic manner. The geometric correction is usually necessary to
preprocess remotely sensed data and remove geometric distortion so that individual picture elements
(pixels) are in their proper planmetric (x, y) map locations.
6
(a)
(b)
Original input image
Rectified output image
Figure3 : The brightness value closest to the predicted x’, y’ coordinate is assigned to the output x, y
coordinate. (Jensen, 2004)
Calibration coefficients is calibration and validation is an important aspect of any remote sensing
system. The calibration of the landsat sensor is supported by data from preflight, post launch- on
board, and ground reference
2.4.4. The Change Vector Analysis Concept and Procedure
The change vector analysis technique (Malila, 1980) is an empirical method of detecting of
radiometric changes between multi date satellite images in any number of spectral bands. The vector
describing the direction and magnitude of change from date 1 to date 2 is a spectral change vector.
Please see in the figure 4 bellow:
Figure 4 : Illustration of a Spectral Change Vector. (Malila, William A., 1980)
2.4.5. Classification method
The main of image classification is to automatically categorize all pixels in image in to land
cover class. This figure 5 show a magnitude of colors illustrating various feature of the underlying
terrain but it is quite useless unless to know what the color mean.
Figure5 : Spectral Reflectance curve of 3 land covers
2.4.6. Change detection
Change detection for geographical information systems (GIS) is a process that measures how
the attributes of a particular area have changed between two or more time periods. Change detection
7
often involves comparing aerial photographs or satellite imagery of the area taken at different times. A
fundamental assumption of digital change detection is that a difference exists in the spectral response
of a pixel on two dates if the biophysical material (i.e., land cover) have changed between dates
(Jensen, 1996)
2.4.7. Supervised Classification and unsupervise classification
There are two types of automatic classification method were used. Supervised classification is
used to identify representative samples of different surface cover types. (i.e. land cover type) to
develop a statistical characterization of the reflectance for each information class and Unsupervised
classification is a method which examines a large number of unknown pixels and divides into a
number of classed based on natural groupings preset in the image values. unlike supervised
classification, unsupervised classification does not require analyst-specified training data. The basic
premise id that values within a given cover type should be close together in the measurement space
(i.e. have very different gray levels) (PCL, 1997, Lillesand and Kiefer, 1994; Eastman, 1995).
2.4.8. Change Detection and Accuracy Assessment
Following the classification of imagery from the difference year, a multi-post-classification
comparison change detection algorithm was used to determine changes in land cover between 2000
and 2013. Quantitatively assessing classification accuracy requires the collection of some in situ data
or a priori knowledge about some parts of the terrain which can then be compared with the remote
sensing derived classification map. Thus to assess classification accuracy it is necessary to compare
two classification maps 1) the remote sensing derived map, and 2) assumed true map (in fact it may
contain some error).
2.4.9. Integrated of RS and GIS for Land Cover Change Study
In this study, remote sensing and geographic information system integration were used in
order to analyse land cover and land use change of Savannakhet province by using Landsat imagery
and GIS data. The relation between the remotely sensed data and GIS data has been in one direction.
For example Landsat, with 30m spatial resolution multispectral has been used to study for land cover
and land use classification and GIS is an information system for spatial data that are referenced by
geographic coordinates. Advance GIS and landsat information can combine and also have been use to
analyze and map result.
2.5. GIS Method for Land Cover and Land Use Change
2.5.1. GIS for Land Cover map
GIS is a method of overlapping layers of information on the geographical map of the region is
set to draw digital information layer can synthesize quantitative.
2.5.2. Post Classification Method
Post-classification methods focus on the analysis of differences of land use/land cover classes
of two independently classified images (Lunetta, 1999).
2.5.3. Accuracy Assessment and Kappa coefficient
Accuracy assessment requires determination of classes based on reference data which have
been gathered by collecting ground truth derived from field work or the analysis of large scale maps or
the visual interpretation of imagery. The reference classes are compared with the result of
classification and the ratios of correctly versus wrongly classified pixels are calculated for each class.
8
Kappa analysis is a discrete multivariate technique for accuracy assessment. The kappa coefficient of
agreement, khat, is calculated as (Cohen 1960):

r
r
i 1
11
N *  Xii   ( Xi. * X .i )
(3)
r
( N 2   ( Xi. * X .i ))
11
Where:
r
=
the number of rows in the error matrix
Xii
=
the number of observations in row I and column i
Xi.
=
the marginal totals of row i
X.i
=
the marginal totals of column i
N
=
the total number of observations
Bellow here is one possible interpretation of Kappa
Table 1: Possible interpretation of Kappa
K
Interpretation
<0
No agreement
0.0-0.2
Slight agreement
0.21-0.40
Fair agreement
0.41-0.60
Moderate agreement
0.61-0.80
Substantial agreement
0.81-1.00
Almost agreement
2.5.4. Advanced Raster Spatial Analysis using ArcGIS
ArcGIS Spatial Analyst, an optional extension to ArcGIS Desktop (ArcInfo, ArcEditor, and
ArcView), provides powerful tools for comprehensive, raster-based spatial analysis. With ArcGIS
Spatial Analyst, users can employ a wide range of data formats to combine datasets, interpret new
data, and perform complex raster operations such as terrain analysis, surface modeling, surface
interpolation, hydrologic analysis, statistical analysis, and much more. (ESRI)
2.5.5. Overlay Methods
An intersect overlay defines the area where both inputs overlay and retains a set of attribute
fields for each. A symmetric difference overlay defines an output area that includes the total area of
both inputs except for the overlapping area. In general, there are two methods for performing overlay
analysis-feature overlay (overlaying points, lines, or polygons) and raster overlay. Some types of
overlay analysis lend themselves to one or the other of these methods.
9
2.5.6. Feature Overlay and Raster Overlay
The key elements in feature overlay are the input layer, the overlay layer, and the output layer.
The overlay function splits features in the input layer where they are overlapped by features in the
overlay layer. New areas are created where polygons intersect. If the input layer contains lines, the
lines are split where polygons cross them. The attributes of features in the overlay layer are assigned to
the appropriate new features in the output layer, along with the original attributes from the input layer.
In raster overlay, each cell of each layer references the same geographic location. That makes it well
suited to combining characteristics for numerous layers into a single layer. Usually, numeric values are
assigned to each characteristic and mathematically combine the layers and also assign a new value to
each cell in the output layer.
2.6. General Principle on determining the relation
Based on GIS method in previous work, this section will go deep in understanding on relation
between the deforestation and urban expansion and socio-economic factors of Savannakhet province
and develops the variable probability map on the basis of developing the land cover change map.
Spatial statistic analysis method shall be the main applicable method. In order to consider the relation
among the above factors, the research applies the multi-grade logic regression analysis model. This is
one of the statistical analysis methods to determine how the independent variables (explanatory
variables) defined the dependant variables (explanatory variables). The spatial statistic analysis will
determine the probability of occurrence ranged from 0-1 in each point in the study area. This study is
concerned with the relationship between the reduction in forest areas with natural elements increasing social and urban area with natural elements - social. 10,000 randomized sample points are
determined in Savannakhet province. This province are taken randomly on ArcGIS and evenly
distributed over an area of Savannakhet. The independent explanatory variables such as urban (m),
land use planning (m), population, ethnic group distribution, distance to main roads (m), distance to
small traffic roads (m), distance to river (m), road density, distance to urban point (m), DEM (m),
slope (degrees) and soil . The dependent variables: 0 is equal to no change area and 1 is change area
present. All data of the variables are included in the XLSTAT. As a result, the variables are meaning
when value (Pr > χ2) < 0.05, other values on the principles of statistics are meaningless and eliminated.
10
CHAPTER 3: APPLICATION OF REMOTE SENSING AND GIS FOR DETECTING LAND
COVER CHANGE IN SAVANNAKHET PROVINCE, LAO PDR
Chapter 3: Open with a result and discussion on the application of remote sensing and GIS for
detecting land cover and land use change, using the NDVI differencing for mapping of dipterocarp
forest and identifying relevant geographical and reason of land cover and land use change for
sustainable development in Savannakhet province, Lao PDR.
3.1 Geography, Socio-Economic and Natural Resources Condition in Savannakhet Province
The study area is located between N: 17º08´, S: 15º53´ and E: 106º57´, W: 104º44´.
Savannakhet province consists of 15 districts namely Kaysone phomvihan, Outhoomphone,
Atsaphangthong, Phine, Sepone, Nong, Thapangthong, Songkhone, Champhone, Xonbuly, Xaybuly,
Atsaphone, Xayphoothong and Phalanxay which borders Khammouan Province to the north, Quang
Tri and Thua Thien-Hue province of Vietnam to the east, Salavan province to the south, and Nakhon
Phanom and Mukdahan province of Thailand to the west and is located alongside the Mekong River
and has a total area of approximately 21,774 square kilometers. Total population is 953,511 and its
population density is about 44 person/sq km. (Lao Statistics Bureau, 2013) please refer to figure 6.
Figure 6: Location of the study area
The study area has an upland area at the altitude of 150-794 metres above sea level, mostly
found in Sepone and Nong districts, covering 5,183 sq km. It covers the Phou Xang He and the Phou
Hino-Katong mountains, which are the national biodiversity conservation areas. North and northeast
part of the mountain range is dominated by steep sandstone (escarpment) from the north to the south.
The west side is plateau, covering an area of 6,771.6 sq km in Phine, Vilabouly, and Thapangthong
district. It is suitable for commercial tree plantation.
Phoukhaokhuai mountains are high altitude in the central region, 1,026 metres. (Lao Statistics
Bureau, 2014). The mountains has generally moderate slops with exhibited elevation from between
300-700 meters above sea level. They are also suitable for agricultural production and have potential
for commercial tree plantation, while mountain ranges at the border between Lao PDR and Viet Nam
are conserved natural forest, which has potential for ecotourism development.
The study area has 9,819.4 sq km as lowland area along the Mekong River, covering
Outhoumphone, Xonbouly, Phalanxay, Atsaphone, Champhone, Kaisone Phomvihan, Xaibouly,
Songkhone, Xayphouthong and Atsaphangthong district. The area is a tributary of many rivers namely
the Mekong river, Xe banghiang, Xe bangfai, Xe noy, Xe Champhone, Xe sangsoy, Xe Lanong, Xe
pone and Xe thamouak rivers at the lower Mekong area, which is very productive for rice and
agricultural production. The area covers a total drainage area of 1,332.85 sq km. Savannakhet is rich
of many different mineral resources and some of those mineral resources have been mined and
processing for internal use and external export such as: Gold, Silver, Gypsum, Copper, Gravel, etc.
and also wildlife conservation area namely Ongmang in Saybuly district.
11
Recently there was an opening of the second Lao-Thai Friendship Bridge connecting
Savannakhet to Mukdahan province of Thailand, the town likely becomes triangle trade among Lao
PDR, Thailand and Viet Nam. The climate of Savannakhet Province, Lao PDR is seasonally tropical,
with a pronounced wet and dry season. The average number of hour of sunlight per year is 2,301
(Statistical year book, 2013). The average rainfall is 1,603.61 per 22 years period. The temperature
changes from northern region, central region and southern region. The annual average maximum
temperature is 32C - 35C in March to April, and the annual average Minimum temperature is 18C
to 24C in December to January. The history of Savannakhet Province was under influence of
the Champa Kingdom from 7th to 10th century, followed by the Khmer Kingdom until 13th century.
The name of Savannakhet Province derives from old bali language, meaning “a land of gold”. The
name Savannakhet from the words Souvan Nakhone, which means “City of Paradise”. “La ville de la
porte du ciel” is the French transcript for the town name since 1895, which is pronounced in Lao as
“Savannakhet”. (Governor of Savannakhet) Savannakhet province is affected by changing of the
usual climate, resulting serious floods and droughts, especially in the paddy field areas. At present,
both urban and rural areas in Savannakhet are not much affected due to industrialization and
infrastructure. Socio-economic development during 2000-2013 was well recognized as rapid
development. GDP was increased up to 12.5% from 2000-2009. Record from the statistical year book
2009 indicated the increase in mining production such as goold, gypsum and copper in 2008-2009 as
the major contributor to the rapid development. GDP in agricultural and forest sector was 43.92%,
industry is 29.23%. An average of GDP was USD 1,628 /person/year. (Department of Economic
Statistics, Lao Statistics Bureau, Ministry of Planning and Investment, 2013) Lao PDR is mainly
affected by unexploded ordnance (UXO) dating back to the Indochina war, especially the period from
1964 to 1973. Savannakhet ranks 1st out of the nine provinces severly impacted by UXO. (UXO Lao,
2012) The three eastern districts of Savannakhet were extensively bombed due to the presence of the
Ho Chi Minh Trail. Savannakhet Province has relatively comprehensive and well-developed
infrastructure systems, especially, road infrastructure and communication facilities. The current
activities along the corridor focus on servicing transit trade and services of tourism to other parts of
the corridor. Importantly, the province shares its international border gates to Thailand and Vietnam
which are good & services trade and investment partners with Lao PDR in general.
3.2. Remote Sensing and GIS data Used
Landsat 7+ and Landsat 8 Imagery were used. In order to analyse the whole area of
Savannakhet province, mainly eight Landsat7+ and Landsat 8 scenes were analysed. A single scene,
path/row 127/047, 127/048, 127/049, 126/047, 126/048, 126/049. 125/048 and 125/049 taken in 2000
by ETM sensor was used. The same path/row image taken in 2013 was used as recent data. Please
refer to figure 6.
Figure6: Landsat data2000 and Landsat 2013
GIS data used such as the administration boundary, river – stream, road networks, forest types:
national protection forest area, national biodiversity conservation area and national production forest
12
area, land use map in Savannakhet province, population, topographic data, DEM, soil,
geomorphology, planning, meteorology and Hydrology data and socio-economic data.
3.3. Geo referencing and Mosaiking using LANDSAT 2000 and LANDSAT 2013 of Savannakhet
Province.
In order to analyse the whole area of Savannakhet Province, mainly eight LANDSAT scenes
have been analysed. A single scene, path/row 127/047, 127/048, 127/049, 126/047, 126/048, 126/049.
125/048 and 125/049 taken in 04 November 2000 and 08 October 2013 was used.
Figure7: Georeferencing and Mosaiking
3.4. Field Visit
Field visits were undertaken in the whole area of Savannakhet province to collect ground
information and interpretation keys useful for image interpretation. To investigate the vegetation and
other land cover for ground collection of data, color composite satellite images and topographical map
of Savannakhet province. Field work mainly focus on Land cover and land use types.
3.5.Methodology applied for land cover and land use change study in Savannakhet Province
The different image processing and classification methods were used i.e., combination of
supervised classification and map visualization technique. The different types of land cover were
recognized by visual interpretation and used for calibration of image classification method. Change
detection was performed by comparing satellite imageries of the study area taken at different times.
Post classification comparing technique used to evaluate land cover change during 2000 to 2013
period. Software used are ENVI 4.7 for classification, MapInfo for digitize and ArcGIS 9.3 for
change calculation with the Intersections function.
Figure8 : Process of change study
13
3.6.
3.6.1.
Result and Discussion
Land Cover maps in 2000 and 2013
Based on the definition of forest types in Lao PDR (Ministry of Agriculture and Forestry,
2005), the different types of land cover can be monitoring into 17 classes (dry evergreen, mixed
deciduous, dry dipterocarp, gallery forest, bamboo, unstocked forest, ray, savannah, scrub, rice
paddy, agricultural plantation, industry plantation, barren land and rock, grass land, swamp, urban
and build up area and water). Please refer to annex 1: figure1 and figure 2.
Field checking statistic and Accuracy Assessment for land cover map of Savannakhet Province
Due to limited time for field verification, we cannot done to the whole of the study area. To verify the
result of map classification of the dipterocarp above, the study evaluated the reliability by KAPPA
index (Cohen-1960). There are 200 of way point and overlay map that we checked careful close
between map and real land cover types.
-
Global accuracy = (3+3+2+25+4+2+1+2+23+5+21+3+5+3+29+11+6)/200*100 = 74
The formula for calculation the coefficients using Kappa (Cohen-1960):
The kappa coefficient of agreement, khat, is calculated as:

Where:
r
r
i 1
11
N *  Xii   ( Xi. * X .i )
(3.1)
r
( N 2   ( Xi. * X .i ))
11
r
=
the number of rows in the error matrix
Xii
=
the number of observations in row I and column i
Xi.
=
the marginal totals of row i
X.i
=
the marginal totals of column i
N
=
the total number of observations
Bellow here is one possible interpretation of Kappa
Table1. Possible interpretation of Kappa
-
K
Interpretation
<0
No agreement
0.0-0.2
Slight agreement
0.21-0.40
Fair agreement
0.41-0.60
Moderate agreement
0.61-0.80
Substantial agreement
0.81-1.00
Almost agreement
Calculations are based on the error matrix, Kappa coefficient =70.34%.
According to the assessment Kappa scales, it has good classification
14
3.6.2.
Using the NDVI difference to classify dipterocarp forest in Savannakhet Province
In this study due to the dipterocarp is highest classed so that we applies the based
identification dipterocarp forest vegetation index NDVI difference for the 5 districts of Savannakhet
Province namely: Atsaphangthong, Xonbouli, Songkhone, Thapangthong and Champone district.
With geographical coordinates from 15º53´ to 17º08´ North latitude and 104º44´to106º57´ East
longitude.
3.6.2.1. Scientific Concept and Method
Dipterocarp forest develops well on “ground” of monsoon tropical climate; there is no cold
winter but there is a typical dry season. Savannakhet has two seasons: the dry season and the rainy
season. Hydrological condition also affect to water regime of Dipterocarp forest. In dry season,
surface water and underground water in Dipterocarp forest is very drought. In rainy season, rain
focuses and caused flood, it forms different types of Dipterocarp forest. Because of a character of
defoliation of this short of forest, in the dry season, Dipterocarp forest is falling leaves and then the
rainy season comes, it shall become green again. This is the cause of the interruption in the remote
sensing image for this short of forest, it is mistaken for other objects. In the remote sensing image
taken in the dry season when Dipterocarp forest was falling leaves, it is mistaken for unoccupied soil
or water surface and in the rainy season, when Dipterocap forest was still green, Dipterocarp trees
grew among the evergreen forest or they grew among the rice, it is mistaken.
3.6.2.2. Research Process
Landsat8 Image was taken on 1/3/2014 (dry season) and on 8/8/2013 (rainy season) for the study.
Remote sensing method uses NDVI index as main information for discriminating Dipterocarp forest,
in addition, it uses wet index of surface to determine the wet regime of soil. The following is diagram
showing the research process.
Figure 9: Diagram of Research process
The results were calculation of NDVI index in the seasons:
NDVI = (Band 5 - Band 4)/(Band 5+Band 4)
(3.2)
Calculation of NDVI difference between two seasons:
T= NDVI2 - NDVI1
(3.3)
The steps of calculation given above are processed in ENVI software, the result is difference
of NDVI by ArcGIS software, thanks to the tools in ArcGIS, carry out dividing the NDVI threshold in
to Dipterocarp forest layer. The object comes into higher NDVI provided to which is joint, because the
15
dipterocarp has a character of defoliation by season. NDVI plant index shall be very different between
two seasons if that object is Dipterocarp forest through processing, threshold by using ArcGIS
obtained map object layer: composed of two layer of object such as Dipterocarp and other object layer
was created.
3.6.2.3. Assessment results NDVI threshold effect
The research study found threshold effects NDVI relative distribution of dipterocarp forest
which separated by two layers of objects. From this map, dipterocarp forest have found relatively
NDVI threshold effect of the distribution of dipterocarp forest in the range of values within [0.125 0.673] and other feature class around brand NDVI value within [-0.5619 – 0.125]. We can see
dipterocarp forests are concentrated in this relatively large, appear in the whole province. From the
effective threshold NDVI maps were established above, conducted two layers overlay map:
dipterocarp forest cover maps and map signal NDVI threshold.
Figure 10.: Dipterocarp forest cover map 2013 and NDVI thresholding effects map
This figure show the dipterocarp forest area separated from NDVI map thresholding effective than dry
dipterocarp forest area separated from the map overlay 2013. The result refer to table.
Table 1: Statistical table of dipterocarp distribution of Savannakhet Province, 2013.
No
Classify Name
1
Orther
2
Dipterocarp
Value
Area (ha)
Area (%)
[-0.5619 – 0.125) -
14546.2129
19.01
[0.125 – 0.673] -
485203.5332
80.9
599749.146
100
Total Dipterocarp area
3
(the actual area of the map
overlay)
Through statistical tables, with> 80% dipterocarp forest area in the range of high NDVI value
effect [0.125-0.673], using NDVI to identify dipterocarp forest on Landsat 8, it has good results and
split fairly accurately based on brand NDVI dipterocarp forest between two rainy seasons and dry
seasons.
3.6.3. Development of Land Cover Change map of Savannakhet Province
The development of change map is to overlay two classification maps of the two different
times, i.e., in the years of 2000 and 2013 of Savannakhet province. An important aspect of change
detection is to determine what is actually changing to what i.e. which land cover class is changing to
the other. This information description on change and no change. Base on the Land cover change map
in the period of 2000 – 2013 by using intersection, The map information show as dynamic change
among difference cover types in the study area during period 2000-2013. The change area from dry
evergreen into other land. The mixed deciduous change into dry dipterocarp, coniferous forest,
16
unstocked forest, ray, rice field and build-up area. Dry dipterocarp change into mixed deciduous,
unstocked forest, rice paddy, other agricultural land, build up area. The water body change into
mixed deciduous, dipterocarp, coniferous forest, unstocked forest, ray, rice paddy, others agricultural
land, swamp and build up areas. There is seem to be a little bit change ie. In 2000, dry dipterocarp
still occupies the highest class with 29.52% of the total class (or 629,995 ha) decreased to 28.36 %
(605,191 ha) in 2013. Also, unstocked forest decreased from 28.50 % of the total class (608,238 ha)
in 2000 to 27.63% (589,615 ha) in 2013. This may be due to the agriculture practice. The dry
evergreen is no change with 1.28% (27,232 ha) in 2000-2013. The industrial plantation have
increased from 0.12% (2,521 ha) in 2000 to 1.08 % (22,953 ha) today, an area larger than the
agricultural plantation. During the past 13 years, The dry evergreen is un change. The ray or shifting
cultivation area was decreased 0.03 % or no change, unstocked forest area was reduced 0.87%, dry
dipterocarp was decreased 1.16% and industry plantation area was increased 1.%. This study will also
serve as a tool in decisions making. In term of location of change between 2000 to 2013 such as
Xaybouly district, Outhoumphone district, Kayson Phomvihan district, Phin district and Sepone
district. The observation of this study area is seem to growth around the national road (road number
12 and road number 13) such as new settlement along the main road.
3.6.4.
Intersect
From the overlay cover change map in the period of 2000-2013, the thesis was to separate the
variable layers area and then "consider" the change in relationship with one dependent variable nature
and social variables (ie concretely, arranging the variable information layer with some natural and
social component maps) such as: maps of digital elevation model (DEM), distance map to the main
traffic hubs, small roads , rivers, streams, road density maps , distance to residential points , slope
map, soils map, geomorphology map and population density.
In the relation with the terrain elevation, the variable area is mainly available in areas with low
terrain (<250m), meanwhile at the elevation of 150-250m, the variable area is mostly concentrated
with > 60 thousand hectares. The higher the elevation is, the less the change is. For the high region of
over 1000m, changes may not be available. The change is mainly varied at the slope of less than 3 0.
With 6 main ethnic groups, which 3 ethnic groups of Laos occupying the area are mainly Lao Loum,
Lao Theung and Lao Sung. With the largest residence area – Lao Loum, it is characterized by the
highest variable area, accounting for nearly 50% of variable area in the period of 2000 – 2013. It is
possible to see that the nearer the residential point is, the larger the area is. It is may be due to demand
on housing, industrial park construction and industrial plantation, etc. Furthermore the area near such
residential area is significantly changed; it may change from the agricultural land to residential land or
industrial plantation land. The area has changed most is the Xaybouly district which has density of
about 52 person/sq km, followed by the Outhoumphone district has a density of about 91, the
Champhone district has a density of about 130 and the Kayson Phomvihane has a density of about 230
respectively.
3.6.5. Change in the relation with DEM
In the relation with DEM, the variable area is mainly available in areas with low terrain
(<250m), meanwhile at the elevation of 150-250m, the variable area is mostly concentrated with > 60
thousand hectares. The higher the elevation is, the less the change is. For the high region of over
1000m, changes may not be available.
3.6.6. Change in the relation with the slop
The change area is inversely proportional to the terrain slope. The higher the slope is, the less
changes are. The change is mainly varied at the slope of less than 30.
3.6.7. Change in the relation with ethnic groups
Accordingly, 6 main operation zones of China, Vietnam, Lao Loum, Lao Sung, Lao Thoung
and others, etc., are defined. Which 3 ethnic groups of Laos occupying the area are mainly Lao Loum,
17
Lao Theung and Lao Sung with the largest residence area. Lao Loum, it is characterized by the
highest variable area, accounting for nearly 50% of variable area in the period of 2000 – 2013.
3.6.8. Change in the relation with residential area
According to the ArcGIS tools, the residential buffer is successfully created. Two variable
map layers and distance map layers to the residential point are overlaid so that the change area is
demonstrated by such distance margin. The information show that the nearer residential point is larger
than far distance. It is may be due to demand on housing, construction, agricultural plantation and
industrial plantation, etc. Furthermore the area near such residential area is significantly changed; it
may change from the agricultural land to residential land or industrial plantation land.
3.6.9. Population density
The area has changed most is the Xaybouly district which has density of about 52 person/sq
km, followed by the Outhoumphone district has a density of about 91, the Champhone district has a
density of about 130 and the Kayson Phomvihane has a density of about 230 respectively.
3.6.10. Geomorphological map
The change area is mainly concentrated in the wash-eroded hillside surface.
3.6.11. Number of households
The result show area change by household quantity of each district in the whole of
Savannakhet province. The higher area change mainly is Xaybouli district, Outhoumphone district,
Champhone district, Atsaphangthong district, Kaysone Phomvihan district, Phine district and
Xayphouthong district.
3.6.12. Density road
Change is mainly allocated in the area with high traffic road density (>0.8 km/km2) and less
available in the area with low traffic road density and the area without passengers.
3.6.13. Small traffic road
The further traffic road is the lesser variety is rarely available. The changes mainly happen
surrounding the distance of traffic of 100m.
3.6.14. Analyze the Relationship between the residential area variables and natural geographical
factors-social (probability urban extension )
According to the map of probability urban extension of Savannakhet Province, during period
of 2000 -2013, this period is forecasted to be potential to occur the highest change of 97%. The
probability forecast model, the areas which have the high residential change probability are mainly
allocated along the main roads through the province. . Regarding administration, it is possible to see
that the areas with high residential change probability is mainly concentrated on the Western districts
of Savananakhet province such as Xaiphouthong, Kaysone Phomvihane, Xaibouli, Outhoumphon,
Champhone, Atsapangthong and Songkhone. Through the above statistic map, the practical population
change area is mostly concentrated in the probability area of 60-90% and least in the area of 0-10%. It
is possible to see that with the probability forecast model for the period of 2000-2013, compared to the
practical change area, the urban extension is mainly located in the highest probability forecast area
fluctuating from 60-90%.
18
3.6.15. Probability deforestation mapping in Savannakhet province, Lao PDR in the period of 20002013.
Deforestation probability has been calculated using multiple regression analysis model. Based on
coefficients from regression the formula for calculating the probability of fluctuation following:
F(forest)=1/(1+ Exp(-(-1.23132774648942-6.56839834310088E – 05 *[Distance_mainroad] 2.87814155093709E–04*[Distance_residential]-3.75679366473247E– 04
*[Distance_smallroad]+1.03999818854704*[Density_road]-4.25668827495912E – 03*[Dem]5.57010586888079E-05*[Distance_rivers]-0.423379003228994 * [Soil_6]
+0.873361785391424 *[Soil_1]+1.42501086593348 *[Soil_4] +1.28368020252284
*[Soil_10]-0.88274611480505 *[Soil_8] +0.84546610498856 *[Soil_11]-0.584830451155708
* [slope_4] +0.49248203211086*[Plan6]-0.415268064708527 *[Plan7]+0.489887399432141
*[Plan14]+0.479478525111225*[Plan7]+0.948729022368104*[Plan12]1.50103940978854*[Plan8]-1.40851518716611*[Plan9]-1.11509552976985*[Plan4]1.51835022761718*[Plan11]-1.56754851154492*[Plan2]-0.641093007283192 *[Plan5]2.84196049469232*[Plan15]-1.5106643809055*[Plan16])))
According to this research, natural constraints like slop, digital elevation model, soil and
planning are very important in forest destruction. Therefore, in this research study main road and
distance to residential area, small traffic, road density, population density, geomorphological,
household, ethnic groups, slope, soil and DEM are use as base maps.
The map is forecasted to be subject to the highest forest loss probability of 83%, in which the
high probability of forest loss is mainly along the roads and the western districts of the province.
Therefore, the amount of deforestation has been decreased in far distances. With this model, it is
possible to further discover the areas with high variable areas not covered by the land cover. This can
be due to urban expansion or industrial agriculture development. The second important factor in
deforestation is slop. It should be mentioned that the most sever destruction has been occurred in the
area of less slope. The change is mainly varied at the slope of less than 3 0. This must be due to better
accessibility in such areas. The third important in depend of soil represent in less slopes, these fields
are very suitable for agriculture. Distance from residential and ethnic distribution are linked. Which 3
ethnic groups of Laos occupying the area are mainly Lao Loum, Lao Theung and Lao Sung. The
largest residence area – Lao Loum, it is characterized by the highest variable area, accounting for
nearly 50% of variable area in the period of 2000 – 2013. This might be due to built-up area or urban
expansion. DEM is the least effective factors in this resulted. The variable area is mainly available in
areas with low terrain (<250m), meanwhile at the elevation of 150-250m. This can be due to the large
area of Savannakhet province is mountainous. Through the above map, it is possible to see that the
reduced forest area is mainly concentrated on the change probability area of over 50%. In areas with
small probability such as 0-10%, 10-20%, the reduced forest area is lesser. This information will also
serve as a tool in decisions making.
3.6.16.Forecast Probability Deforestation to the year 2020
As the previous results, we have created excellent production map of forecast probability
deforestation in the period 2000 -2013. The map described as the probability of deforestation and
changes location in 13 years, for example 1 year lost 1 certain forest area, then from here we use the
multiple regression analysis model by XLSTAT software and processing to predict the probability of
deforestation by 2020, or after 20 years since 2000. The formula as following:
The formula T = 20 years x [Map forecast XS 2000-2013] / 13 South
19
The result is map forecasts deforestation to the year 2020. We can see red areas on the map
with the probability of deforestation up to 140%. It is observed those areas in the future to 2020
undoubtedly deforestation, while 1 part to 2013 forest area was change.
3.6.17.Probability forecast reforestation and sustainable development in Savannakhet province, Lao
PDR to the year 2020
The formula for calculating the probability reforestation during period of 2000-2013 following:
F (forest)= 1 / (1 + Exp (- ( -0.424595582147951+1.49850626126914E04*dem+0.360600497100621*densityroad+1.12044588719095E05*distance_residential+1.11712235111937E-05*mainroad+1.25002964952066E05*smallroad+0.113836251816706*soil-1+0.267257360594977*soil-2+0.119643943025263*soil3+0.225336654798671*soil-6-7.61850780746977E-02*nhiet-2+8.63430702908214E-02*plan1+0.710073070967384*plan-2+0.142162030376272*plan-3+0.155817951728432*plan6+0.365628914296977*plan-7+8.32749985380694E-02*plan-8+8.25690246803669E-02*plan9+9.78125863200834E-02*plan-10-0.191176975718857*plan-13+0.117926132215225*plan-147.59569867038037E-02*plan-15+2.79903645704434E-02*aspect-2)))
Formula of probability forecast reforestation 2020 following:
T = 20 years x [Map forecast XS 2000-2013] / 13 South
This section is very important to support sustainable development of effective forest management,
environmental monitoring, and for land cover and land use planning.
The forecast probabilities reforestation map to the year 2020, result show the locations of tree
plantation increasing in 2020. The value > 90-113% are considered as very high and the area having
this value is found at same area of reforestation in the period of 2000-2013. It has been observed that
in the nine districts, and Similarly, from probability forecast deforestation in the previous section, It is
observe as change in to the others types of land cover. For example. Unstocked forest to industrial
plantation or dipterocarp to agriculture plantation. The value > 80-90% are considered as high, It is
found in the eleven district. The area having this value>70-80% are considered as medium with
distribute in the whole of Savannakhet Province.
However, it should be emphasized that there are three mechanisms for sustainable development are
the following: Data Information: Data information is one of the best for the sustainable development, to
support decision making, to monitor the positive and negative activity through change detection.
Law and regulation: clear and refer to the global, national, provincial, district, village level. To guides
the planning, developing and utilization of natural resources. Awareness raising: Improving education
and knowledge is a key to improving information systems and data sharing, through which it is hoped
that improving in the field of remote sensing and GIS between government agency, national university,
provincial and district level.
20
CONCLUSIONS AND RECOMMENDATIONS
Remote sensing, GIS, spatial statistic analysis method and modeling in geography play an
important role in the areas of land cover and land use. In this study, a different image processing and
classification method combining supervised classification and map visualization technique is
proposed. Using LANDSAT 7 and LANDSAT 8 satellite data, the study has successfully classified the
image into 17 land cover classes namely: dry evergreen, mixed deciduous, dry dipterocarp, gallery
forest, bamboo, unstocked forest, ray, savannah, scrub, rice paddy, agricultural plantation, industry
plantaion, barren land and rock, grass land, swamp, urban and build up area and water. Post
classification comparing technique was used to evaluate land cover change during 2000 to 2013
period. Software include used ENVI 4.7 for classification, MapInfo for editing and ArcGIS 9.3 for
calculation with the intersections function and develop methodology to conduct in depth investigation
on relation between the forest area change and residential change and socio-economic factors and
develops the variable probability map on the basic of developing the land cover change map in the
previous using spatial statistic analysis method to consider the relationship among the above factors,
the thesis applies the multiple regression analysis model. This study also applied multiple regression
analysis model by XLSTAT software and ArcGIG advance raster analysis was used to forecast
probability deforestation in the period of 2000-2013 and forecast probability deforestation to the year
2020 and probability reforestation in the period of 2000-2013 and forecast probability reforestation to
the year 2020.
Results showed that the most important land cover and land use changes in Savannakhet
province, Lao PDR for the last 13 years are the increase of small size urban and infrastructure
development, agricultural plantation at the agricultural land and forest area. The number of areas
covered with temporally unstocked forests and dipterocarp forest have changed to young sized forests.
The dry evergreen and water body remained unchanged. The designed land cover change map in the
period of 2000-2013 is develop by intersect function in ArcGIS software shows a clear relationship
between distance from roads, digital elevation model (DEM), slop map, ethnic, distance change to
residential point, population density, soil, geomorphology, etc…The result performances that the
distribution of DEM available change area is mainly in areas with low terrain (<250), meanwhile at
the elevation of 150-250m and also the slop of less than 30. The residential buffer is successfully
created, two variable map layers and distance map to the residential point are overlaid so that the
change area is demonstrated by such distance margin. The near residential area is significantly change
from the agricultural land to residential land or industrial plantation land. The change area allocation
by population density map show that the area has changed most is the Xaybouly district which has
density of about 52 person/sq km, followed by the Outhoumphone district has a density of about 91,
the Champhone district has a density of about 130 and the Kayson Phomvihane has a density of about
230 respectively. The change area also it is possible in the wash-eroded hillside surface. Change is
mainly allocated in the area with high traffic road density or more than 0.8km/km2 and less available
in the area with low traffic road density, the area without passengers and higher mountainous location.
The change mainly happen surrounding the distance of traffic of 100 meters.
The multiple logistic regression statistics model for two dependent variables are variables and
variable populated forest, the method of statistical analysis this space helped us determine the
relationship of the dependent variable and the input variable (independent variable), consider the many
variables that affect volatility, changes effect, and others changes that are meaningful to the population
increase or decrease the study area in the period of 2000-2013.The highest forest loss probability of
83% is mainly along the roads and the western district of the Savannakhet province namely: Xaibouli
district, Outhoumphone district, Kaysone Phomvihan district, Xaiphouthong district, Champhone
district, Atsaphangthong district and Songkhone district.
21
Multiple logistic regression model helps to calculate the variables related to fluctuations
explicitly follow mathematical functions. The accuracy of the model will depend on and the level of
detail of the input variables. Variable input how much more detailed forecast models more precise.
However, using multiple logistic regression statistics model that should be consider for the importance
factors. Accordingly, we can see the variation in the population or area of the forest depends on many
factors such as distance to roads, rivers, soil types, population density, DEM, etc. and the impact of
each factor is different and also comply with the rules as one more close to traffic, the greater the
probability of deforestation, along with concentrated population.
Through the result of probability forecast deforestation in the period of 2000-2013 and forecast
probability deforestation to the year 2020. This information will serve as a tool in decisions making.
This new information will reduce the cost. However, the multiple logistic regression model is
significant to find the probability forecast reforestation in the period of 2000-2013 and probability
forecast reforestation to the year 2020. This information is very directly to the forestry strategy to the
year 2020 (FS 2020). The GoL envisages to increase forest cover to reach 70 percent of the total land
area by 2020. This information is important to support sustainable development of effective forest
management, environmental monitoring, and for land cover and land use planning. Through
observation of forecast probability reforestation map in 2020 compared to the map of forecast
probabilities reforestation in the period 2000 – 2013 was noted that such changes are the same and the
change it has expanded to area neighborhoods. So, it is possible that category of land cover in the
province of Savannakhet changes to others land cover and land use. For example in 2000-2013
Dipterocarp forest areas were turned into a area of agricultural plantation and unstock forest have
changed to the industrial plantation. If we have monitor, protect, manage, control and conservation of
forest plantation left cut and illegal logging forest plantation they will be bigger and in the near future
15-20 years it will become as a natural forest nature. In my opinion, the mechanisms for sustainable
development must have three keys elements: data information, law and regulation and awareness
raising.
In conclusion, as the socio-economic development and the infrastructure of the province has
been growing, it is reminded that dry evergreen forest area to other land uses such as infrastructure
and agricultural plantation area and build up area will continue in the future. Satellite imagery can be
used for land cover and land use monitoring for the future development of the Savannakhet province.
In our study area, LANDSAT7 and LANDSAT8 were used. Other necessary information like the
definition of forest, topographic map and GIS administration boundary would have been useful for this
study. Landsat images can provide basic information on the physical extent of land cover. However,
the structure and forest density can be challenges. The study on the number and detailed description of
the tree can be used high resolution satellite images. The research result shows land cover and land
use in Savannakhet Province in the past 13 years has small size change and through the multiple
logistic regression analysis result show that land cover and land use change quickly, especially the
probability deforestation along the road access, urban extension, industrial plantation , etc,. Thus, this
research results to enable informed decision-making on the Natural Resources and Environmental
Action Plan and Forest Management Plan of Savannakhet Province and Forest Strategy to the year
2020. This kind of study can be applied to other province in Lao PDR taking into consideration the
above mentioned requirements for sustainable development.
22
PUBLICATIONS
1. Virany SENGTIANTHR, Nguyen Ngoc Thach and Pham Xuan Canh (2013), Land Cover
Change Detection Using Remote Sensing and GIS in Savannakhet Province, Lao PDR,
proceeding of the 34th Asian Conference on Remote Sensing 2013, Bali, Indonesia.
2. Nguyen Ngoc Thach, Le Thi Khanh Hoa, Virany SENGTIANTHR, Pham Xuan Canh and
Pham Viet Hong (2014), Using the NDVI Differencing for Mapping of Dipterocarp Forest in
Svannakhet Province, Lao PDR, proceeding of the 35th Asian Conference on Remote Sensing
2014, Nay Pyi Taw, Myanmar.
3. Virany SENGTIANTHR (2015), Land Cover and Land Use Change Using Remote Sensing
and GIS in Savannakhet Province, Lao PDR, procedure journal, National Agriculture and
Forestry Research Institute , Ministry of Agriculture and Forest, Lao PDR. (Lao language)
4. Virany SENGTIANTHR, Nguyen Ngoc Thach, Le Thi Khanh Hoa and Pham Xuan Canh
(2015), Mapping and Analysis of Land Cover and Land Use Change for a Sustainable
Development in Savannakhet Province, Lao PDR. Proceeding of the 36th Asian Conference on
Remote Sensing 2015, Manila, Philippines.
23
Annex 1
Figure1. Land cover map in 2000
Figure2. Land Cover Map in 2013
24
Figure3. Probability forecast deforestation in the period 2000-2013
Figure4. Map probability forecast deforestation by 2020
25
Figure5. Map forecast probabilities reforestation in the period 2000 – 2013
Figure6. Map forecast probabilities reforestation to the year2020
26