Analysing Urban Green Open Space Planning (Case - sappk

Sekolah Arsitektur, Perencanaan dan Pengembangan Kebijakan ITB
Analysing Urban Green Open Space Planning (Case Study:
Tangerang Municipality, Indonesia)
Evan Wiraksana Maharta(1), Monika Kuffer(2), Roos Akbar(3)
(1)
(2)
(3)
Master Program in Regional and City Planning, School of Architecture, Planning and Policy Development (SAPPK), ITB.
Department of Urban and Regional Planning and Geo-Information Management, ITC - University of Twente, the Netherlands.
Urban Planning and Design Research Group, School of Architecture, Planning and Policy Development (SAPPK), ITB.
Abstract
Despite the advantages of green space for both human well-being and ecosystem, many cities
struggle in providing sufficient amount of green spaces. The intertwined, hierarchical regulation and
standards further complicate the planning and provision of green open space. This research analyse
the urban green open space planning using remote sensing and GIS. The result shows that
Tangerang Municipality has uneven distribution of green areas; and experienced green areas
fragmentation and loss. Although different source of data shows different figures, they reveal that
Tangerang Municipality has yet meets the national policy standard of 20% public green open space.
Planning analysis reveals that the green open space master plan does not fully comply with the
municipal spatial plan. Furthermore, less than 36% of planned green open space is currently green
areas. These conditions may poses difficulties in implementation of the plan.
Keywords: green open space, Landsat, NDVI, planning analysis, remote sensing
Introduction
Despite the advantages of green open space for
ecosystem (James et al., 2009; Riswan, Putra,
and Jannah, 2005; Xu et al., 2011), urban
environment (Bernatzky, 1982; Georgi and
Dimitriou, 2010), health (Fan, Das, and Chen,
2011;
Khotdee,
Singhirunnusorn,
and
Sahachaisaeree, 2012) and well-being of urban
residents (Khotdee et al., 2012; Peschardt and
Stigsdotter, 2013), many cities struggle in
providing sufficient amount of green spaces.
Urban green spaces are limited resource, and
has been reduced and become more fragmented
(Baycan-Levent and Nijkamp, 2009) due to land
use change, economic growth, population
increase, urbanization, and weakness in
planning and managing the urban development
(Uy and Nakagoshi, 2007). The problem for
maintaining and developing green open space
can be caused by: lack of coordination between
agencies
involved
(Purnomohadi,
1994),
conflicting interests, no integrated city planning,
natural resources destruction, and weakness in
public society participation (Riswan et al., 2005).
Different agencies or departments can have
different responsibilities regarding green open
space provision. The division of statutory powers
and
ineffective
communication
amongst
departments can be major cause of stationary
management of green open space. The
complexity of green space management by
public sector (Riswan et al., 2005), thus, can
lower the implementation of green open space
plan.
The weakness of green open space planning is
enforced by the spread of responsibilities among
variety of departments and severe constraints of
skills and resources (Baycan-Levent and
Nijkamp, 2009). In Tangerang Municipality, the
responsibilities of developing the spatial plan
and the green open space master plan lie in two
Jurnal Perencanaan Wilayah dan Kota B SAPPK V4N1 | 105
Analysing Urban Green Open Space Planning (Case Study: Tangerang Municipality, Indonesia)
different agencies. City Planning Agency has the
responsibility to create the municipal spatial
plan, whereas the City Cleaning and
Landscaping Agency is in charge for creating the
green open space master plan.
Those differences between agencies will result
in the poor realization in green open space
provision. Green open space provision, by law,
need to be planned, acquired, built and
managed properly; and hence accurate data is
required.
Remote sensing (RS) can be an appropriate tool
to analyse discrepancies between the current
and future plans of public green open space.
Vegetation indices, such as Normalized
Difference Vegetation Index (NDVI) can depict
the location of green areas and their size as
base data for planning and management
purposes. Furthermore, when combined with
appropriate Geographic Information System
(GIS) tools, remote sensing can also be helpful
to determine the fitness of green open space
plans, for example compared to existing green
areas.
images with less than 10% cloud coverage
within the time span from 1994 to 2013. To
prevent variations in NDVI values derived from
remote sensing imagery (Abuzar et al., 2014),
only images from two sensors were used
although there were two usable Landsat 7 ETM
in 1999-2000.
Green Areas Analysis
Green area in the study area was obtained using
the NDVI analysis. Radiometric calibrations to
convert DN value into top-of atmosphere
reflectance were conducted beforehand using
data from metadata of the remote sensing data.
The Normalized Difference Vegetation Index
(NDVI) is used to transform multispectral
remote sensing data into single band image
representing vegetation distribution. The single
band NDVI images were then re-classified into
binary map using an arbitrary threshold. The reclassification and accuracy assessment steps
were conducted in several iterations until the
highest accuracy value was achieved for each
NDVI image.
Datasets
Classification
accuracy
assessment
is
systematically compare pixels or polygons in a
remote sensing derived classification map with
the ground reference test information (Jensen,
2005). Since there were limitations in time and
resources, ground reference information was
derived using Google Earth’s time shift feature.
Table 1 shows the datasets acquired and used
in this research. The Landsat images used in
this research were selected from the archive of
Kappa analysis is used to assess accuracy in
remote sensing classification analysis. Kappa
analysis yields K hat statistic which is estimate of
The general aim of this study is to analyse the
conditions and the planning of public green
open space using remote sensing and GIS.
Method
Table 1. Datasets obtained and used in this research
No
1
2
3
4
5
6
Data
Land cover map
Municipal spatial plan
Green open space inventory
Green open space list
Green open space master plan
Tangerang municipality in figures
Source
City Planning Agency
City Planning Agency
City Cleaning and Landscaping Agency
City Cleaning and Landscaping Agency
City Cleaning and Landscaping Agency
Central Statistical Bureau
7
Landsat images
USGS
106 | Jurnal Perencanaan Wilayah dan Kota B SAPPK V4N1
Year
2012, 2013
2012
2008
2012
2012
2009, 2011, 2013
Sept. 1994, Aug. 1999,
July 2006, July 2013
Evan Wiraksana Maharta
Kappa (Jensen, 2005). It is measure of
agreement or accuracy between the remote
sensing derived classification map and the
reference data.
The last
landscape
calculated
using the
as input.
step in green areas analysis is
metrics calculation. The FRAGSTATS
the landscape metrics of green areas
resulted binary maps of green areas
Urban Green Open Space Analysis
The (public) green open space maps were
derived from the municipal data. City Cleaning
and Landscaping Agency provided green open
space data whereas City Planning Agency
supplied land use/land cover data.
The data received from the municipality need
first to be checked for errors, inconsistencies or
redundancies caused by different processing
extent and data lineage. The next step is
topology check and fix for all the spatial data
against ‘must not overlap’ rules. The green open
space data then were converted into raster
format to be analysed in FRAGSTATS for
landscape metrics.
Landscape Metrics and Fragmentation Analysis
The computation of landscape metrics of
categorical maps were conducted using
FRAGSTATS. In this study, two levels of
heterogeneity were analysed: class level metrics
and landscape level metrics. Landscape level
metrics were assessed for green open space
data only.
Urban Green Open Space Analysis
The (public) green open space maps were
derived from the municipal data. City Cleaning
and Landscaping Agency provided green open
space data whereas City Planning Agency
supplied land use/land cover data.
Urban Green Open Space Planning Analysis
Several overlays or comparison were conducted
to analyse the planning of green open space.
Accuracy or discrepancy of categorical data from
municipality with the data derived from remote
sensing can also be examined by superimposing
those maps and examine any differences.
Results and Findings
Green Areas
The green areas map of Tangerang Municipality
is derived from Landsat images using NDVI
calculation. In the NDVI binary classification, a
dynamic threshold is used to determine where
the green areas in the Tangerang Municipality
are. Several iterations were carried out to find a
threshold that gave the highest accuracy.
Table 2. Threshold, accuracy and Kappa values
Image
22 Sept. 1994
19 Aug. 1999
5 July 2006
8 July 2013
Threshold
0.124
0.305
0.2235
0.248
Accuracy
84.14%
86.90%
90.34%
89.66%
Kappa
0.6571
0.7380
0.8051
0.7801
The different threshold value is caused by
variation in the NDVI value of those four
images. According to Jensen (2005), the
reflectance of near-infrared that form NDVI
value can also provide information about plant
health, senescence and stress. The seasonal
change and haze in the atmosphere can also
cause the variations. Although adequate image
calibration to achieve compatible top of
atmosphere reflectance have been conducted,
atmospheric conditions i.e. water vapour can
affect the NDVI calculation result.
El Nino/La Nina phenomena (also called El
Nino/Southern Oscillation or ENSO) can be a
major reason for climatic anomalies in equatorial
region such as Indonesia. According to data of
Multivariate ENSO Index (MEI) from NOAA,
Indonesia experienced the higher precipitation
than usual due to the “strong” La Nina effect in
the period of year 1999-2000, 2007-2008 and
2011. The NDVI anomalies can be associated
with ENSO indices (Erasmi et al., 2009). The
difference in precipitation will further generate
deviation in NDVI value due to both the plants
vigour and the atmospheric haze.
Jurnal Perencanaan Wilayah dan Kota B SAPPK V4N1 | 107
Analysing Urban Green Open Space Planning (Case Study: Tangerang Municipality, Indonesia)
(a)
(b)
(c)
(d)
Figure 1. Green areas results from NDVI analysis on Landsat images dated (a) 22 September 1994, (b) 19
August 1999, (c) 5 July 2006, and (d) 8 July 2013.
Generally, several inferences can be drawn from
the green areas maps in Figure 1 i.e. there are
changes in the amount, distribution, and
fragmentation in Tangerang Municipality in the
period from 1994 to 2013; and inequality of
green areas across the municipality.
Tangerang Municipality experienced massive
amount of green areas loss (i.e. 4,546 ha) in 20
years period from 1994 to 2013. The green
areas declined especially in the North West
(Neglasari, Benda, and Batuceper) and South
East (Larangan, Cipondoh and Karang Tengah)
of Tangerang Municipality.
Spatial metrics can be used to explain the
fragmentation in the green areas. The
108 | Jurnal Perencanaan Wilayah dan Kota B SAPPK V4N1
decreasing value of the total class area (CA)
over time when the number of patches (NP)
increases suggest that the green areas become
more fragmented (i.e. patches are smaller and
more dispersed). Furthermore, the decreasing
value of percentage of like adjacencies (PLADJ)
and aggregation index (AI) confirm the
fragmentation trend.
Table 3. Class-level spatial metrics of green areas
Year
1994
1999
2006
2013
CA
13614
9974
8981
9054
NP
655
1118
1459
1483
PLADJ
91.88
84.80
83.88
82.82
AI
92.12
85.07
84.15
83.08
Evan Wiraksana Maharta
(a)
(b)
(c)
(d)
Figure 2. Green open space: (a) according to LULC 2012 from DTK, (b) according to LULC 2013 from DTK, (c)
according to study conducted by DKP, and (d) loss in 2012 to 2013 according to LULC 2012-2013.
Urban Green Open Space
Central Statistical Bureau of Indonesia (2011)
reported that between 2010 and 2009, the
green areas in Tangerang Municipality
decreased by 35 hectares. Calculation using
green areas derived from remote sensing data
also concurs with the decreasing trend of green
open space.
The public green open spaces in Tangerang
Municipality are currently managed by the City
Cleaning and Landscaping Agency. According to
the agency’s report (2012), Tangerang
Municipality has 2,124.52 ha (12.60%) green
open space comprises 1,861.67 ha (11.04%)
public and 262.85 ha (1.56%) private green
open space. From that amount, only 47.54 ha is
currently managed and maintained by the
agency.
The green open space map can be derived from
two sources: land use and land cover (LULC)
update data provided by City Planning Agency
(DTK) and a result of green open space study
conducted by City Cleaning and Landscaping
Agency (DKP). Their classification, accuracy and
data consistency, however, differ due to
difference in point of view regarding green open
space. City Planning Agency classifies the green
open space among other land use and land
cover classes. City Cleaning and Landscaping
Agency,
however,
further
divide
(and
reclassifies) the green open space into three
classes.
Since DTK provided two years of LULC data, the
trend of green open space can also be
examined. Green open space in Tangerang
Jurnal Perencanaan Wilayah dan Kota B SAPPK V4N1 | 109
Analysing Urban Green Open Space Planning (Case Study: Tangerang Municipality, Indonesia)
Municipality has decreased by 137.83 ha from
4,865.91 ha in 2012 to 4,728.08 ha in 2013.
Due to discrepancies in the classification
employed, it is inevitable that the green open
space data also differs in both spatial extent and
amount. Difference in classification can also be
observed from the spatial metrics calculation.
Total area, number of patches and patch density
differs significantly, while contiguity index only
show slight difference.
Table 4. Class-level spatial metrics of green open
space, based on two source of data
Data
LULC
2013
DKP
2013
CA
NP
PD
CONTIG
4734
23485
129.14
0.1765
2476
10223
56.22
0.1757
Green Open Space Planning
The Ministry of Public Works regulation (2008)
stipulated the hierarchy and catchment area as
standard in planning green open space, as well
as green open space requirements per capita.
Taken into account the total and distribution of
population by district in 2012, the green open
space that should be provided by Tangerang
Municipality is 1,209 ha. The study conducted by
City Cleaning and Landscaping Agency (2012),
however, lists the requirement of green open
spaces which are 1,510.2 ha for public and
1,423.1 ha for private green open space.
Apart from discrepancies in classification and
standards employed, the planning products
related to the urban green open space also
differs
significantly.
Although
it
is
understandable due to different regulations that
should be followed by the two plans, both plan
should actually agree with each other. However,
only 60.32 ha of planned green open space in
the master plan were also designated as green
open space in the municipal spatial plan.
The green open space master plan was also
compared to the current green areas to examine
whether the plan takes into account the current
distribution of green areas and to estimates the
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land that need to be acquired. From 19.6 km2
planned green open space, only less than 7 km2
is currently green areas, while the other 12.6
km2 is mostly built-up area.
Discussion
This study demonstrates the use of freely
available remote sensing data as tool to derive
information about green open space in urban
environment. Archive of multi spectral Landsat
imagery can provide a time-related urban
vegetation analysis. Google Earth can be useful
in both acquire information on urban green open
space and checking or validating the
classification
result.
Those
opportunities
however also come with several limitations.
Vegetation indices derived from remote sensing
imagery can only provide information about the
vegetation. Other on-site information or expert
knowledge is required if the green open space
data is needed.
In the tropical areas, especially in areas that are
greatly influence by atmospheric (and oceanic)
variation such as Indonesia, obtaining and
deriving information from remote sensing data
can be perplexing. Obtaining remote sensing
image that is free from clouds and haze is also
challenging.
This research also reveals that municipal data
reliability is somewhat low. At least two reasons
can cause it. Firstly, the metadata about data
lineage and previous processing is not
maintained. Secondly, the different knowledge,
experience and viewpoints of agency’s employee
responsible for data management differ greatly.
The less-reliable municipal data can pose several
problems. First, the accuracy of the final product
is greatly influenced. Second, it will limit the
planning collaboration and information sharing.
Conclusion
According to the data extracted from the remote
sensing, Tangerang Municipality has 9,022 ha
(48.97%) green areas which are unevenly
distributed
across
districts.
Tangerang
Evan Wiraksana Maharta
Municipality experienced a large amount of
green areas loss (4,546 ha) in 20 years period
from 1994 to 2013. The green areas also
became more fragmented.
The remote sensing data can be useful to
extract information about green areas in a cost
effective and timely manner. However, the use
of remote sensing in a heterogenic urban setting
is limited by the spatial resolution, spectral
characteristic and radiometric. It also depends
on which vegetation index is used. This research
shows that large variation in both plant and
atmospheric condition may also limit the
applicability of remote sensing.
Three sources of green open space show
different number of green open space in
Tangerang Municipality. An estimate by City
Cleaning and Landscaping Agency shows 2,124
ha (12.60%) green open space (1,861 ha
10.10% public). According to map of green
open space obtained from the same agency,
however, the amount is significantly lower: only
1,313 ha or 7.13% (1,196 ha or 6.49% public).
City Planning Agency however show a larger
value (4,728 ha or 25.66%) of green open
space.
Municipal data also show the distribution,
fragmentation and loss of green open space.
Green open space has decreased 137 ha from
2012 to 2013.
It is evident that according to the municipal
data, Tangerang Municipality has yet meet the
national policy requirement. According to the
plan analysis, however, the green open space
plan and spatial plan do not comply with each
other. Only 3% of planned green open space in
master plan is also classified as planned green
open space in the spatial plan. When compared
to green areas derived from the remote sensing,
only less than 700 ha or 35.29% of planned
green open space is currently green areas. The
rest is located in the built-up area.
Acknowledgments
I would like to express my gratitude to my
supervisors, Monika Kuffer, M.Sc. from ITC, the
Netherlands and Prof. Roos Akbar from ITB for
their invaluable assistance, support and
guidance.
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