Landcover Delineation of Bukit Tigapuluh Landscape ... - Planet Action

LANDCOVER DELINEATION OF BUKIT TIGAPULUH LANDSCAPE FOR
YEAR 2011
REPORT
by: Paska Ariandy Iswanto* & Oktafa Rini Puspita**
* GIS Consultant, World Wildlife Fund – Indonesia
** GIS Officer, Frankfurt Zoological Society
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©Bukit TIgapuluh National Park (flickr.com)

I. Introduction
Sumatra, in Indonesia, is the sixth largest island in the world which is home to a rich variety of flora and
fauna. Amongst 11 national parks of Sumatra, Bukit Tigapuluh National Park is unique because it
comprises hilly lowland forest landscape that is separated from Bukit Barisan Mountain range, creating
an ecosystem not found in other national parks. The 144,223 hectares national park is actually only
small representation of the much larger continuous forest landscape, such as 651,232 hectares (KKI
WARSI, Frankfurt Zoological Society, Eyes on the Forest, WWF-Indonesia, 2010) used as baseline area
here (Figure1).
Figure1. Location of baseline area relative to Sumatra Island and zoom-in map (baseline area with altitude information)
Since the New Order regime, Indonesian government had granted several concessions to selectivelylogged
the forest landscape, even inside the current national park boundary. This situation worsened
when the selective logging concessions ceased their operation in early 21th century where the high
conservation value forests that are still left were granted to be cleared by pulp & paper, oil palm, and
mining companies. The landscape baseline area lost 50% of its original natural forest cover between
1985 & 2010 (KKI WARSI, Frankfurt Zoological Society, Eyes on the Forest, WWF-Indonesia, 2010).
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In 2010, slightly more than 50% of natural forests were located outside the protection areas. Most of
them were either owned or applied for non-conservation purposes (KKI WARSI, Frankfurt Zoological
Society, Eyes on the Forest, WWF-Indonesia, 2010). If this is allowed to continue, some endangered
endemic flora and species will be doomed to extinction in the near future. Therefore, as environmental
NGOs concern with the forest degradation and deforestation in Bukit Tigapuluh Landscape, World
Wildlife Fund – Indonesia and Frankfurt Zoological Society are planning to apply for a multi-blocks
ecosystem restoration concessions in area where there are no legal owners. This effort at least will halt
and stop illegal and legal forest destruction in some of Bukit Tigapuluh’s crucial areas.
One of the requirements to apply for such concession is a landcover map. Because of the rapid changes
of landcover in the landscape, a new and accurate existing landcover map is needed. This is very
important for making decisions on future spatial planning and also making us aware of the main agents
and future threats of deforestation and forest degradation in the area of interest. The following
discussion will discuss about the methodology and analysis of Bukit Tigapuluh Landscape landcover
delineation for year 2011.
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II.
Methodology
Satellite images have been heavily utilized for forest monitoring around the world. The 2011
landcover delineation discussed here took advantage of the freely available Landsat data from
United States Geological Survey (USGS) and SPOT data from FZS 2010-2011 Planet Action project.
First of all, all available January – July 2011 Landsat 7+ ETM images were scanned for the image
quality (clouds), and eight images from three different paths/rows were selected and downloaded.
As for SPOT images, because of the limitation from the grant’s quota, only 2 images could be
obtained. Figure 2 illustrates the contribution of each image to the landcover delineation.
Figure2. Satellite Coverage for Landcover Delineation
Groundtruthing data were taken from the survey report of Frankfurt Zoological Society patrol teams
(Wildlife Protection Unit) that spanned from January to August 2011 (8189 points). The data mainly
covers southern and western part of Bukit Tigapuluh Landscape (Figure3). Beside that, several
ancillary data were also used for aiding landcover interpretation especially in the area where
groundtruthing data were unavailable. These data are summarized in Table1.
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Table1. Ancillary Data used for assisting delineation process
No Data Type Source(s) From Year(s)
1 ALOS PALSAR Landcover Groundtruthing Survey Sata WWF Riau 2010
JAXA
2 Bukit Tigapuluh Landscape Landcover FZS 2010
3 Riau Landcover WWF -Indonesia 2007
4 Jambi Landcover WWF -Indonesia 2008
5 Settlement Distribution Bukit Tigapuluh 2008-2010
National Park Office
FZS
KKI WARSI
6 Former and Existing HTI and HPH Concessions Ministry of Forestry 2010-2011
WWF- Indonesia
FZS
KKI-WARSI
7 Existing Mining Concessions Ministry of Energy 2010-2011
and Mineral
Resources
8 Existing Plantation Concessions WWF-Indonesia 2011
9 National Park and Protected Forest Boundary Bukit Tigapuluh -
National Park Office
10 Road FZS
2008-2011
KKI-WARSI
WWF-Indonesia
11 River Bakosurtanal 1985
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Figure3. Landcover grountruthing data distribution
Because multitemporal satellite images were used, normalization had to be undertaken to reduce
the effect of atmospheric and radiometric disturbances. Corrections involved two steps, obtaining
radiance value from raw digital number and surface reflectance value from radiance value. Radiance
describes the amount of energy being emitted or reflected per unit solar angle and per unit time,
usually expressed in n units of watts per square meter per steradian per micrometer
(W/(m2*sr*µm)) ( (Tempfli, Kerle, Huurneman, & Jansen, 2009; ITT). The spectral radiance is
calculated automatically in ENVI using the following formulas:
1. For Landsat images: L
where QCAL is the calibrated and
quantized scaled radiance in units of digital numbers, LMIN is the spectral radiance at QCAL = 0,
LMAX is the spectral radiance at QCAL = QCALMAX, and QCALMAX is the range of the rescaled
radiance in digital numbers. LMIN and LMAX are derived from tables provided in the Landsat
Technical Notes (August 1986) with the information provided through the TM Calibration
Parameters dialog in ENVI (ITT).
2. For SPOT images: L = (X/A) + B where L = the equivalent irradiance at the input of the
instrument, , X = the count (0 to 255),, A = absolute calibration gain, for the considered spectral
band, and B = absolute calibration offset, for the considered spectral band.
Reflectance is the portion of incident energy on a surface that is reflected. ENVI's Fast Line-of-sight
Atmospheric Analysis of Spectral Hypercubes (FLAASH) module were used to derive surface
reflectance with input data such as: scaled radiance images to nanometer, scene center location,
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sensor type, sensor altitude (km), ground elevation (km), pixel size (m), flight data, flight time,
atmospheric model (tropical), aerosol model (rural), initial visibility (km) from BMKG Muaro Jambi,
etc. (Figure4)
Figure4. FLAASH input parameters for 23 July 2011 image.
Not all satellite images underwent the correction, for this case, only Landsat 7 ETM+ path/row
126/61 and SPOT images were taken part. This is because other satellite images were in JPEG format
and it was assumed that the coverage areas are too insignificant, thus can be ignored. Visual
observations revealed that the correction results are fairly good to make the images directly
comparable, especially inside the gap areas(Figure5)
Figure5. Left: Raw Images before correction, Right: Images after correction.
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After correction processes were done, the images were ready to be delineated. All the images were
visualized in RGB (Red: Shortwave Infrared, Green: Infrared, Blue: Red) with RGB adjustments when
needed (usually Red: Infrared, Green: Red, Blue: Green) and the delineation scale was fixed at 1:50,000.
All digitization and topology editing processes were done in ArcGIS 9.3 software. Landcover class
definition followed Anderson, Hardy, Roach, & Witmer (1976) with some modifications. Seven
interpretation elements were considered in categorizing landcover classes: tone/hue, texture, pattern,
size, shape, height/elevation, and location/association (Tempfli, Kerle, Huurneman, & Jansen, 2009).
Diagram1 summarizes the landcover delineation processes.
Landsat and SPOT Images
Radiometric and Atmospheric
Corrections
View in RGB
Delineation Process
Ancillary Data
Groundtruthing Data
Landcover 2011
Undirect Relationship
Direct Relationship
Diagram1. Landcover Delineation Process Tree
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III.
Analysis
After assigning each polygon with their respective landcover class, 17 landcover classes could be
identified in the baseline area. Amongst them, there are three oil palm classes, two acacia classes, and
four mosaic classes. A polygon is classified as mosaic where individual landcover couldn’t be separated
at mapping scale and neither predominate each other. The landcover classes will be described in detail
below.
Forest
Forests are stocked with trees capable of producing timber or other wood products, and exert an
influence on the climate or water regime (Anderson, Hardy, Roach, & Witmer, 1976). Forest appears in
the darkest shade of green compare to other vegetation features (Figure6). It can be found inside the
conservation areas (national park and protection forest and also in the buffer zones.
Figure6. Left: Forest in clear atmosphere inside Bukit Sosah Protection Forest, Right: Forest in hazy atmosphere inside and in the buffer
zone of the National Park
Acacia Plantation /Young Acacia Plantation
Acacia can be easily identified with its apple green colour and association with Hutan Tanaman Industri
(HTI) aka pulp and paper concessions. Because eucalyptus is usually planted side by side with acacia in
insignificant amounts, it was included in the acacia class. While the spectral characteristics of young
acacia may appear very similar to other vegetation features, especially shrub/forest regrowth, it is
differentiated by its more compact size, uniform textures, and most likely either borders acacia classes
or located inside newly licensed concessions. Both acacia and young acacia spectral characteristic can be
seen in Figure7.
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Figure7. Left: Acacia in Bukit Batabuh Sei Indah Concession, Middle: Acacia in WKS District VIII concession, Right: Young Acacia in Tebo
Multi Agro Concession. HTI Concession boundaries are shown in red color.
Cleared
Cleared or barren land is an area of thin soil, sand, or rocks (Anderson, Hardy, Roach, & Witmer, 1976).
Because no major settlement existed inside the baseline area, it was included in the cleared class.
Cleared can appear in various shades of red color, from pink to purple, (Figure8) depend on how barren
the area is, soil type, etc.
Figure7. Left: Pink shade in the south of WKS District VIII concession, Middle: Pinkish Red in the east of National Park, Right: Pinkish purple
inside Bukit Sosah-Bukit Limau Protection Forest.
Dryland Agriculture
Agricultural Land may be defined broadly as land used primarily for production of food and fiber
(Anderson, Hardy, Roach, & Witmer, 1976). Only one dryland agriculture class was identified in the
baseline area. There is no unique spectral characteristic belongs to this class (indistinguishable from
shrubs/forest regrowth). The class was primarily assigned based on the extensive groundtruthing data
and its location near a Talang Mamak settlement (Semarantihan) (see Figure8)
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Figure8. Dryland Agriculture near Semarantihan sub-village
Mining
Despite many coal mining licenses had been issued in the area, no evidence of open pit mining was
found. Thus, the mining class here refers to sand extraction along big river that is common in the
province. Sand mining appears in whitish pink and forms a long strip in both side of a river (Figure9).
Figure9. Sand mining in both sides of Langsisip River
Oil Palm Plantation/Young Oil Palm Plantation/Smallscale Oil Palm
To delineate oil palm plantation and young oil palm plantation, oil palm concessions data were used as
the guideline although most polygons didn’t precisely follow the plantation boundary. Oil Palm
plantation class can be differentiated by its uniform undulating texture. Its green color is also a little
brighter than it is in acacia class (Figure10Left). Young oil palm plantation appears in yellowish green and
is identified by its location near oil palm plantation and presence of grid road network (Figure10Middle).
Smallscale oil palm was delineated mostly from the groundtruthing data because it is hardly
distinguishable from mosaic oil palm+rubber class. (Figure10Right).
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Figure10. Left: Oil Palm Plantation of Sumber Andalas Kencana, Middle: Obvious unknown largescale young oil palm plantation in the
north of Ex Riau Andalan Pulp and Paper concession, Rigth: Smallscale oil palm plantation in the northern part of Ex Hatma Hutani HPH
concessions
Shrub/Forest Re-growth
Shrub and forest re-growth were differentiated in Setiabudi (2006), although both appears in small
coverage and sometimes mixed. Because the groundtruthing data took shrub and forest regwroth in one
category and haze obstruct the view to clearly differentiate both features, they were combined as one
class here. Shrubs/forest re-growth appears in the lightest green of the vegetation classes (Figure11). It
is mostly associated with previously cleared areas (identified from historical landcover maps).
Figure10. Left: Shrub between Artelindo Wiratama concession and Bukit Sosah Protection Forest, Right: Forest Regrowth in Ex Dalek
Hutani Esa concession
Smallscale Rubber
No big rubber plantation existed in the landscape so that all rubber plantations were classified as
smallscale rubber. Smallscale rubber has dense texture because of its canopy characteristics and
appears in brownish green (Figure11). Its location is closer to settlement than other homogenous
plantations.
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Figure11. Smallscale Rubber, southeast of Tebo Multi Agro concessions
Water Body
The delineation of water areas depends on the scale of data presentation and the scale and resolution
characteristics of the remote sensor data used for interpretation of land use and land cover (Anderson,
Hardy, Roach, & Witmer, 1976). River feature is easy to be identifed and it appears in dark blue color
(Figure 12).
Figure11. Water Body (Sungai Pemotongan)
Mosaic Classes
There are five mosaic classes identified: Mosaic Acacia+Oil Palm, Mosaic Acacia+Shrub, Mosaic Oil
Palm+Rubber, Mosaic Rubber+Dryland Agriculture, and Mosaic Shrub+Rubber. Mosaic classes have
both features’ spectral characteristics; either appears side by side in small patches or fuses into
intermediate colors (Figure13). Ancillary data plays a great role in determining mosaic classes especially
in the extremely confusing areas.
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Figure13. In clockwise order, Mosaic Acacia+Oil Palm in Arian Multi Kawa concession, Mosaic Acacia+Shrub west od Artelindo Wiratama
concession, Mosaic Oil Palm+Rubber inside Bukit Limau Production Forest, Mosaic Shrub+ Rubber in Tanah Datar-Tua Datai Talang Mamak
Settlements.
Above, the spectral characteristics and the terminology of each landcover class have been
described. Here (Figure14) are shown the landcover classes’ photographs taken from the field that
represents the landcover perception of the survey teams and the analysts.
1 2
5
3
4
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6
7 8
9 10
11
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Figure14. 1. Forest (WPU-FZS, 2011), 2. Acacia (GIS-FZS, 2009), 3. Cleared 1 (WPU-FZS,2011), 4. Cleared 2 (WPU-FZS,2011), 5. Dryland
Agriculture (WPU-FZS,2011), 6. Oil Palm Plantation (GIS-FZS, 2010), 7. Young Oil Palm Plantation (WPU-FZS, 2011). 8. Smallscale Oil Palm
Plantation (WPU-FZS, 2011), 9. Shrub (WPU-FZS, 2011), 10. Forest Regrowth (GIS-FZS, 2010), 11. Smallscale Rubber Plantation (WPU-FZS,
2011), 12. Water Body (WPU-FZS,2011).
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Spatial Pattern and Area Sizes
Figure15. Map showing delineation result
Figure15 shows delineation result for the whole baseline area. Forest is dominant in the center,
while mosaic oil palm+rubber is mostly visible in the periphery region. Forest forms three big
patches in which the westernmost patch is already severely deforested characterized by many very
small polygons. Mosaic shrub+rubber class can predominantly be found in the forest buffers, with
the exception in the southern and southwestern part of the baseline area. Large scale homogenous
plantations mostly follow their respective concessions boundary while smallscale homogenous
plantation such as oil palm and rubber marks the landscape’s northern, eastern, and southern
boundary. Large size of cleared areas is easily visible in the middle of forest patches near and inside
the protection forest. Mining appears in southwestern part forming a strip feature along the river.
Dryland Agriculture and Mosaic Rubber+Dryland Agriculture show up in only one polygon (both near
Talang Mamak settlements) and both mosaic acacia classes appear in the northwestern part.
Although there are a lot of small and big rivers flowing inside the landscape, only one water body
polygon was delineated.
In Block I of applied ecosystem restoration concession, forested area is absence in the middle part
which is covered by mosaic shrub+rubber, smallscale rubber, cleared areas, and dryland agriculture.
In Block II, deforested area in the western and southern parts is mainly covered by mosaic oil
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palm+rubber. There are also noticeable shrubs polygons intrude into the forested areas in the
southeastern part. In Block III, smallscale oil palm can be seen in the northeastern part while mosaic
shrub+rubber is visible in the southeastern areas. Riau Block I is more complicated to explain. The
most dominant landcover class beside forest is mosaic oil palm+rubber and the block’s boundary
also coincides with oil palm and young oil palm plantations.
Graph1
Graph1 shows the proportion of each landcover class according to its size. It is revealed that forest class
is still the largest landcover class, accounts for 46% of the baseline area, followed by mosaic oil
palm+rubber (28%). Forest cover losses since 2010 (KKI WARSI, Frankfurt Zoological Society, Eyes on the
Forest, WWF-Indonesia, 2010) reaches almost 20,000 ha (6,2%) in just a year. This rate is a bit lower
than 2009-2010 forest cover loss (6,7%) but it is most likely because the satellite images used in this
landcover delineation is from the first half of year 2011 and the rate can be so much worse in the end of
year. Cleared, mosaic shrub+rubber, and acacia landcover classes presence in 8%, 6%, and 4% of the
baseline area, respectively. Each of other landcover classes only constitutes less than 2% with dryland
agriculture as the smallest size.
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IV.
Conclusion and Reccomendation
Bukit Tigapuluh Landscape landcover has been delineated for the year 2011 utilizing Landsat and SPOT
satellite images and extensive grountruthing survey. It is more detail than the previous landcover maps
ever produced for the region but still lacks accuracy checking for the un-surveyed areas, especially in the
northern side and inside the national park where survey is highly inaccessible. It was found that forest
and mosaic oil palm+rubber are the most noticeable landcover classes. It was also revealed that acacia,
oil palm, and rubber are the most popular planted crops in which acacia and oil palm are highly
preferred by corporate (big scale) plantations.
Though originally this landcover data is aimed to apply for a restoration ecosystem concession (see
Figure14 for concession extent), it can also be used for further studies especially those that want a new
and moderately detail data on existing condition of Bukit Tigapuluh Landscape, such as the planned
carbon calculation. Because of high difficulties in finding cloud-free images in the region, it was
recommended to employ Synthetic Aperture Radar (SAR) satellite images in the future, so that a
consistent and automatic landcover classification can be implemented. The relationship between SAR
images and landcover types should be extensively studied.
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Bibliography
Anderson, J. R., Hardy, E. R., Roach, J. T., & Witmer, R. E. (1976). A Landuse and Landcover Classsification
System for Use with Remote Sensor Data: Geological Survey Professional Paper. Washington: United
States Government Printing Office.
ITT. (n.d.). Envi 4.5. Help.
KKI WARSI, Frankfurt Zoological Society, Eyes on the Forest, WWF-Indonesia. (2010). Last Chance to Save
Bukit Tigapuluh. KI WARSI, Frankfurt Zoological Society, Eyes on the Forest,.
Setiabudi. (2006). Final Report: Analysis of 1990-1995-2000 and 2005 Landuse Dynamics in the Kampar
Penisula - Tesso Nilo - Bukit Tigapuluh Conservation Landscape, Riau, Sumatra, Indonesia. WWF
Indonesia Tesso Nilo Conservation Programme and Conservation Management Ltd.
Tempfli, K., Kerle, N., Huurneman, G. C., & Jansen, L. L. (2009). Principles of Remote sensing; An
Introdutory Textbook. Enschede: The International Institute for Geo-Information Science and Earth
Observation.
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