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High Resolution Land Use Change 

Analysis for Sumatra and Kalimantan 

Circa 2000

Final Report

Task Order #4

BPA-09-03/GS-23F-0076M

April 8, 2011

Technical Point of Contact:

Maria Busuioceanu

5160 Parkstone Drive, Suite 230

Chantilly, VA 20151

Voice: (703)-378-8672 x416

Fax: (703)-378-8978

Email: mbusuioceanu@integrity-apps.com

Contractual Point of Contact:

Ms. Jane Brady

5160 Parkstone Drive, Suite 230

Chantilly, VA 20151

Voice: (703) 674-3156

Fax: (703) 378-8754

Email: jbrady@integrity-apps.com

5160 Parkstone Drive Chantilly, VA 20151 (703) 378-8672 
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Table of Contents

1 Overview ...............................................................................................................................................1

2 Map Oil Palm Plantations with Remote Sensing Analysis.....................................................................1

2.1 Oil Palm Overview.........................................................................................................................1

2.2 IAI Analysis of Oil Palm Plantations...............................................................................................3

2.3 Landsat-based Analysis of Oil Palm Plantations ...........................................................................4

2.3.1 Manual Detection .................................................................................................................5

2.3.2 Automatic Detection Algorithm............................................................................................6

2.3.3 Minimum Detectable Oil Palm Plantation Size and Accuracy...............................................7

2.4 High Resolution Data Analysis ......................................................................................................8

2.5 Final Analysis and Uncertainty Discussion..................................................................................11

2.6 Comparison of Time Periods and Analysis..................................................................................13

3 Summary .............................................................................................................................................14
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1 Overview

Integrity Applications, Incorporated (IAI) has identified and mapped oil palm plantations in major oil 

palm producing regions, specifically the Indonesian islands of Sumatra and Kalimantan, using available 

remote sensing data from the circa 2000 time period (1999 -2002). IAI used and expanded upon the 

analytical approach outlined in their 2010 study of oil palm plantations in Sumatra and Kalimantan for 

the circa 2010 time period1to evaluate land use categories in palm oil producing regions.

IAI executed a multi-faceted approach to map Indonesian oil palm plantations. The approach leveraged 

data from the complete Landsat archive, high resolution data via Google Earth, and data from the

National Geospatial-Intelligence Agency (NGA) Unclassified National Informational Library (UNIL), among 

others. Analysis of oil palm plantation areas using Landsat data was performed both visually and 

through an automated detection algorithm to ensure a robust analysis. The results from the Landsatbased analysis were exported into Google Earth. Where available, high resolution imagery was obtained

to augment the Google Earth environment. The final analysis of Indonesian oil palm plantations was 

conducted visually within the augmented Google Earth environment, which fused the Landsat-based

analysis with high resolution imagery in a very flexible analysis environment. IAI mitigated cloud cover 

and data gaps, executed final plantation identification, and estimated the total area of medium- to 

large-scale oil palm plantations by searching for and manually analyzing the imagery available via data 

archives. Using high resolution remote sensing data yielded an estimated ground cover area for oil palm 

of 2,424,144 hectares for Sumatra and 867,012 hectares for Kalimantan for the circa 2000 time period.

2 Map Oil Palm Plantations with Remote Sensing Analysis

2.1 Oil Palm Overview

Oil palm trees, which are native to Africa, Central America, and South America, were introduced to 

South East Asia around 1920. It was not until the 1960’s and 1970’s that many rubber estates gave way 

to oil palm plantations. Global oil palm area increased by 43 percent between 1990 and 20022. By 2007, 

oil palm plantations covered 11% of Malaysia2 and Malaysia’s biggest publically traded oil palm 

company’s land bank exceeded 633,000 hectares, with plantations across the Malaysian and Indonesian 

islands of Sumatra, Kalimantan, and to a lesser extent Sulawesi. Indonesia’s agricultural minister stated 

in December 2009 that Indonesia had 18 Million hectares of land suitable for oil palm cultivation3, nearly 

twice the 9.7 Million hectares that had already been allocated for plantations. Currently, new 

plantations are under development from forest land in the Labuk-Sugut district, Tongod, as well as in 

Kinabatangan and lower Sagama3. 

Oil palm plantations generally follow similar patterns for establishment and growth. Once a plantation 

site is identified, boundaries are marked, and trees are removed from the perimeter to prevent fire 

spread to the surrounding or adjacent forest. Where permitted, fire is used to eliminate existing 

vegetation and, at the beginning of the rainy season following land clearing, leguminous cover plants are 

 

1

IAI (2010). Renewable Fuel Lifecycle Analysis – High Resolution Land Use Change Analysis. Final Report for EPA 

Task Order #3, BPA-09-03/GS-23F-0076M, September 20, 2010.

2

Hansen, Sune (2007) Feasibility Study of Performing an Life Cycle Assessment on Crude Palm Oil Production in 

Malaysia, International Journal of Life Cycle Assessment Vol 12 No 1: 50-58

3

http://news.mongabay.com/2009/1202-indonesia_palm_oil.html
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established to prevent soil erosion, improve soil structure and palm root development, increase the 

response to mineral fertilizer, and reduce the danger of micronutrient deficiencies. Healthy young oil 

palm plants are then established after eight months in the nursery. They should be 0.8 – 1 meter in 

height and display 5-8 full-grown leaves. Oil palm trees are typically planted in rows and columns as 

shown in Figure 14 with spacing of 7.8 meters between each row and 9 meters between each individual 

tree to allow each tree to have enough space to grow. Mature oil palm trees can grow up to 20 meters 

tall and have a canopy of leaves that cover 6-10 meters. The growing point of the adult oil palm tree

produces 20-25 leaves every year. Once planted, an oil palm tree can produce for more than 30 years. 

Figure 1: Illustration of planting patterns for oil palm plantations.

IAI leveraged knowledge of oil palm plantation growth patterns and the distinctive crown shape of oil 

palm trees in their Landsat and high resolution imagery analyses. The typical growth pattern of oil palm 

plantations is a major component of the spectral signature of an oil palm plantation as it appears in a 30 

meter pixel. This unique spectral signature was used in the automatic detection algorithm described in 

Section 2.3.2. Plantation growth pattern and the distinctive crown shape of oil palm trees were also 

used in the high resolution data analysis described in Section 2.4 to differentiate between agricultural 

vegetation types such as oil palm, coconut, and rubber tree. Figure 9-(A) shows how the distinctive 

crown shape of oil palm trees is easily seen in high resolution imagery.

 

4

http://www.fao.org/docrep/006/t0309e/T0309E03.htm
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2.2 IAI Analysis of Oil Palm Plantations

Figure 2: Oil palm plantation approach overview and plow diagram.

IAI constructed a multi-faceted approach to map Indonesian oil palm plantations [Figure 2], which used 

the analytical approach outlined in their previous study of oil palm plantations in Sumatra and 

Kalimantan for the circa 2010 time period1. The approach leveraged data from the complete Landsat 

archive, high resolution data via Google Earth, and high-resolution data from the National GeospatialIntelligence Agency (NGA) Unclassified National Informational Library (UNIL), among others. Analysis of 

oil palm plantation areas using Landsat data was performed both visually and quantitatively to ensure a 

robust analysis. The results from the Landsat-based analysis were exported into a Google Earth 

environment augmented with high-resolution data from the NGA UNIL. The augmented Google Earth 
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environment was used to solidify the analysis of oil palm plantation regions. The following sections 

discuss each major phase of the analysis.

2.3 Landsat-based Analysis of Oil Palm Plantations

IAI performed an assessment of oil palm plantation land use for the islands of Sumatra and Kalimantan

for the circa 2000 time period using three independent image analysts and an automated target 

detection algorithm developed as part of their previous analysis of oil palm plantations circa 20101. This 

analysis leveraged 15-30 meter Landsat imagery available from the Landsat Global Land Survey (GLS)5

for the Indonesian Islands of interest. For this analysis, Landsat imagery from 1999-2002 was used,

referred to as the circa 2000 time period. Within this time period, additional Landsat imagery was 

obtained through USGS Global Visualization Viewer [http://glovis.usgs.gov/] to mitigate cloud-filled 

regions. 

Figure 3: Oil palm ROIs created by the three independent analysts (red, green and blue) and the automated target detection 

algorithm (teal).

For each area positively identified as oil palm by one of the analyses, a Region of Interest (ROI) was 

created. This resulted in a total of four sets of ROIs, one for each analysis. The full suite of ROIs from the 

three independent analysts and the automated analysis was used to select regions for a more detailed 

visual analysis using high resolution remote sensing data in Google Earth. Figure 3 illustrates the ROIs 

from each of the analyses overlaid on a Google Earth image of the islands of Sumatra and Kalimantan. 

 

5

Gutman, G., Byrnes, R., Masek, J., Covington, S., Justice, C., Franks, S., and R. Headley, Towards monitoring land 

cover and land-use changes at a global scale: The Global Land Survey 2005, Photogrammetric Engineering and 

Remote Sensing, 74, 6-10, 2008.
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2.3.1 Manual Detection

IAI constructed two “cloud-free” mosaics of the islands of Sumatra and Kalimantan using the highest 

resolution (15-30 meters) Landsat GLS data available from the circa 2000 time period. Three 

independent analysts each performed a visual inspection of these mosaics based on the known spectral 

and spatial characteristics of oil palm plantations. Figure 4 illustrates an example of the unique spatial 

structure and visual distinction of oil palm through a false color infrared (FCIR) Landsat composite 

display. In Figure 4 (A), the plantation structure is well defined, as are the expected field colors of 

orange, with some green or blue depending on stage of growth. Figure 4 (B) shows a variety of structure 

in the scene, with structure characteristic of a plantation identified. Such areas were selected using a 

Region of Interest (ROI) tool provided by ENVI.

Manual analysis revealed many areas where there are plantations currently being expanded and where 

deforestation is evident. For example, Figure 5 shows a plantation along the edge of a forest (forest 

indicated by the lack of structure and dark orange color in the FCIR). This example demonstrates a 

plantation that is likely to expand since structure can be seen in the forest; however, only the areas 

demonstrating both the structure and colors characteristic of oil palm plantation were selected as 

Regions of Interest (ROIs).

For each independent, Landsat-based analysis, an ROI file was created in ENVI and overlaid on a Google 

Earth map. The manual analysis ROIs created by the independent analysts are indicated by the colors 

red, green, and blue in Figure 3 above. 

Figure 4: Two different 30 meter resolution false color infrared composite (FCIR) Landsat images illustrate the structure 

and color of oil palm plantations (expected field color depends on stage of growth) and display uniquely identifiable 

characteristics of oil palm plantations.
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Figure 5: Example of a plantation along the edge of a large forest region. Notice the road structures identified in the forest 

region (indicates likely expansion).

2.3.2 Automatic Detection Algorithm

IAI’s study on data availability for oil palm identification in the circa 2010 time period illustrated the 

potential for quantitative detection of oil palm within Landsat imagery. Leveraging the results of this

previous study, IAI implemented an operation workflow to quantitatively identify oil palm in Landsat 

imagery for their circa 2010 time period analysis of oil palm plantations in the Indonesian islands of 

Sumatra and Kalimantan1. This workflow was used again for the circa 2000 time period analysis 

discussed here.

Using a site identified in IAI’s study on data availability for oil palm identification for the circa 2010 time 

period (Carey Island, Malaysia) as truth, a typical mean spectrum for oil palm was identified. To mitigate

the seasonal impacts of solar illumination angle and monthly atmospheric effects, a mean spectrum for 

oil palm was obtained from this known oil palm plantation site for each month of the year. Using these 

spectra, data pre-processing, and the statistical target detection algorithms Constrained Energy 

Minimization (CEM) and Adaptive Coherence Estimator (ACE), a quantitative detection algorithm was 

developed. Each Landsat image within Sumatra and Kalimantan was processed individually.

Figure 6 illustrates an example of the results from the quantitative detection algorithm. Figure 6 (A)

illustrates a visible-band Landsat image, where Figure 6 (B) demonstrates the corresponding results of 

detected oil palm (displayed in white) via the IAI automatic detection algorithm. A visual inspection of 

the imagery illustrates the strong capabilities offered by the quantitative detection algorithm for the 

identification of oil palm using the input spectral data. To ensure performance and reduce false alarms,

ROIs were manually obtained from the quantitative results [Figure 6 (B)] by IAI analysts. The use of the 
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quantitative analysis provides an independent investigation on a pixel-to-pixel level to identify likely oil 

palm pixel locations. This measure was taken to augment the manual detection analysis performed on 

the Landsat data by the three independent analysts, as well as to provide a cue for further investigation 

in the high resolution, Google Earth based analysis.

Figure 6: Example of the results of the quantitative detection algorithm. (A) Illustrates a visible band Landsat image; (B) 

demonstrates the corresponding results of detected oil palm (white). Note that the detection results provide a strong cue to 

investigate regions of further analysis with high resolution imagery, potentially identifying regions that are challenging for 

visual analysis. 

2.3.3 Minimum Detectable Oil Palm Plantation Size and Accuracy

The use of FCIR Landsat data in the Landsat-based analyses limits the size of oil palm plantation that can 

be detected through our method. The spatial resolution of the FCIR product is 30 meters, meaning that 

a single pixel would overlap approximately three rows by three columns of oil palm trees planted as in 

Figure 1. To help reduce false-alarms in the quantitative analysis, a minimum cluster of oil palm pixels is 

needed, further increasing the minimum detectable oil palm plantation size. The same is true for ROIs 

identified through visual analysis by trained image analysts during manual detection.

Figure 7: Comparison of 10 hectares (the minimum candidate oil palm plantation size identifiable through 30 meter Landsat 

analysis) and 1 hectare (the minimum candidate oil palm plantation size identifiable through high resolution imagery) in the 

District of Columbia, USA.
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Through examination of the ROIs identified in the visual and quantitative analyses, we were able to 

determine that the minimum candidate oil palm plantation size we were able to identify using 15-30 

meter Landsat resolution was approximately 10 hectares [Figure 7]. This implies that small-scale or 

neighborhood-sized plantations would not be detected for areas where the Landsat 15-30 meter data 

was the best resolution imagery available. 

As this is the limiting factor in our analysis, we describe the accuracy of our oil palm plantation 

detection based on the accuracy we were able to achieve with the Landsat data, even though higher

resolution data was used when available (see Section 2.4). With a minimum threshold size of 10 

hectares, we assume the uncertainty in our area estimates of oil palm plantations is in the ones place. 

Therefore, we report our values accurate to 1 hectare throughout this report.

2.4 High Resolution Data Analysis

Federal agencies, to include the U.S. Environmental Protection Agency, can access remote sensing data 

via existing government archives. The National Geospatial-Intelligence Agency (NGA) operates the NGA 

Unclassified National Informational Library (UNIL), an archive of domestic and non-domestic commercial

imagery available for discovery and dissemination. The Web-Based Archive and Retrieval Portal (WARP) 

is the interface that provides on-line access and retrieval capability via the World Wide Web (WWW) at 

no cost. WARP-UNIL is available at https://warp.nga.mil. Users may register at the WARP-UNIL website 

and receive a login and password using a .mil or .gov email extension. An initial survey of image data 

over Malaysia and Indonesia available via the WARP archive [Figure 8] conducted in IAI’s data availability 

study in 2010 indicates that data availability is much more limited around 2000 than around 2010. This is 

not entirely surprising because fewer remote sensing satellites were available to the general public in 

2000 compared to 2010.

Figure 8: Initial survey of image data over Malaysia and Indonesia available via the WARP archive conducted in IAI’s data 

availability study in 2010.

0

100

200

300

400

500

600

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Number of Images

Year

Malaysia: # of Images Available

Indonesia: # of Images Available
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In both the circa 2000 and circa 2010 analyses, after a detailed review of the ROIs identified in the 30 

meter Landsat analysis, IAI examined the available high resolution data in Google Earth for Sumatra and 

Kalimantan within the analysis time period. Analysts then identified sites where the combination of 

Landsat and Google Earth data was inconclusive and additional high resolution data from the WARP was 

sought. The total number of images in the WARP database that met the search criteria was higher than 

the number of images downloaded, but due to excessive cloud cover or images taken of the same 

location, not all available images were used in the high resolution analysis. 

In an effort to maximize the utility of limited high resolution data for the circa 2000 time period, analysts 

utilized the high resolution imagery in concert with the 30 meter resolution Landsat imagery available 

for the entire region [Figure 9]. Figure 9 (A) illustrates a clear example of the effectiveness of using high 

resolution imagery within Google Earth to aid in the identification of oil palm plantations. Each high 

resolution image used was manually inspected by image analysts. New ROIs were created for each 

positively identified oil palm region. 

Positive oil palm identification was based on the guidance of the Landsat-based analysis and further 

refined, where possible, by the plantation structure as well as unique oil palm canopy characteristics 

visible in the high resolution data. Figure 9 (B) illustrates how a single high resolution image can be 

leveraged to positively identify an oil palm plantation through visual analysis in conjunction with Landsat 

FCIR imagery. Once the oil palm plantation is positively identified in high resolution data, image analysts 

were able to overlay the Landsat FCIR image and extrapolate an identifying color for oil palm 

plantations. This method is also used to disregard regions [black arrows in Figure 9 (B)] that do not share 

similar characteristics as defined in the high resolution image.

Figure 9 (A): Imagery analyzed in the Google Earth Environment: high resolution data was leveraged to positivity identify oil 

palm plantations in the Landsat FCIR imagery.
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Figure 9 (B): Imagery analyzed in the Google Earth Environment: visual extrapolation process was used to enhance the final

analysis.

For areas where high resolution imagery was available, the minimum candidate oil palm plantation size 

we were able to identify was on the order of 1 hectare. Figure 10 demonstrates how small-scale 

plantations, undetectable in 30 meter resolution Landsat FCIR imagery, are clearly identifiable in high 

resolution imagery.

Figure 10: (A) Illustrates small-scale oil palm plantations identified using high resolution imagery in Google Earth; (B) Shows 

a high resolution image zoom, where the distinctive crown shape of oil palm trees are identified; (C) Demonstrates the 

inability to identify small-scale oil palm plantations on the order of 1 ha in 30 meter Landsat FCIR imagery.
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2.5 Final Analysis and Uncertainty Discussion

Figure 11: Regions of Interest identified as oil palm regions for the circa 2000 time period using available Landsat and high 

resolution commercial remote sensing data.

The final map of oil palm plantations in Indonesia for the circa 2000 time period is shown in Figure 11. 

The final analysis served to rectify and refine the ROIs identified in the three visual Landsat-based 

analyses and the Landsat-based automated detection algorithm, leveraging the Google Earth 

environment and high resolution data where available. In many cases, ROIs for oil palm regions 

identified in the Landsat-based analysis [Figure 3] were refined with high resolution data in the final 

analysis [Figure 9]. An example of the ROI refinement is presented in Figure 12. In this case, an oil palm 

region was identified by multiple analysts as well as by the automated detection algorithm using Landsat 

data. Analysts were then able to more precisely identify and mark the region’s boundaries using the high 

resolution data. However, due to the limited availability of high resolution data from the circa 2000 

time period, this method was less often available to image analysts than IAI’s circa 2010 time period 

analysis of oil palm plantations in Sumatra and Kalimantan1. We have high confidence that the approach 

used in the Landsat analysis discussed in Section 2.3 differentiates native forest and medium to large 

agricultural land-use types. However, in the absence of high resolution imagery, greater uncertainty is 

present when attempting to differentiate between the major agricultural vegetation types in this region 

(specifically oil palm, rubber tree and coconut). 
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Figure 12: Example of ROI refinement using high resolution data. This region was detected in the Landsat-based analysis 

(identified ROIs indicated by the colored lines) and refined using high resolution data (purple area). Figure courtesy of (IAI, 

2010)1.

In some cases, the high resolution analysis revealed oil palm producing regions that had not been 

detected in the Landsat-based analysis. Failure to detect oil palm regions in the Landsat analysis may 

have occurred if the oil palm region was smaller than the Landsat resolution (15-30 meters) or if the 

land use changed in the time lag between the Landsat collection and the high resolution data collection. 

Figure 13 illustrates an example in which only one analyst detected oil palm in the Landsat data, but the 

high resolution analysis confirmed the presence of oil palm and revealed additional oil palm areas that 

had not been detected in the Landsat analysis.

Figure 13: Example of a region that was partially detected using Landsat data (green line). High resolution data confirmed 

presence of oil palm and indicated additional area supporting oil palm (purple). Figure courtesy of (IAI, 2010)1.
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2.6 Comparison of Time Periods and Analysis

The IAI circa 2000 time period analysis found a cumulative 2,424,144 hectares of oil palm on the island 

of Sumatra and a cumulative 867,012 hectares of oil palm on the island of Kalimantan while the circa 

2010 time period analysis found a cumulative 2,401,871 hectares of oil palm on the island of Sumatra 

and a cumulative 1,573,733 hectares of oil palm on the island of Kalimantan. 

Table 1. Comparison of cumulative oil palm area covered: circa 2000 time period to circa 2010 time period.

2000 Oil Palm [ha] 2010 Oil Palm [ha] Difference (2010-2000) [ha]

Percent 

Difference [%]

Sumatra 2,424,144 2,401,871 -22,273 -0.92

Kalimantan 867,012 1,573,733 706,720 81.51

Table 1 illustrates that the total percent area change in Sumatra was roughly -1%, where Kalimantan 

demonstrates a percent area change of roughly 82% in reference to the circa 2000 time period analysis. 

The difference found in Sumatra is anticipated to be within the uncertainties (i.e. errors associated with 

cloud cover and data coverage) of the analysis methodology. This suggests that minimal change 

occurred in cumulative area of oil palm plantations between the circa 2000 and circa 2010 time periods. 

Kalimantan, however, demonstrated broad expansion in cumulative area of oil palm plantations 

between the circa 2000 and circa 2010 time periods. Figure 14 provides a visual comparison of the 

identified regions, with the circa 2000 time period results in magenta and the circa 2010 time period 

results in teal. 

Figure 14: Comparison of Regions of Interest identified as oil palm regions in circa 2000 time period (magenta) and circa 2010 

time period (teal).
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3 Summary

The cumulative area of oil palm plantations found in this effort was calculated for the Indonesian islands 

of Sumatra and Kalimantan using the analysis discussed above. The IAI circa 2000 time period analysis 

found a cumulative 2,424,144 hectares of oil palm on the island of Sumatra and a cumulative 867,012

hectares of oil palm on the island of Kalimantan. For comparison, the IAI circa 2010 time period analysis 

found a cumulative 2,401,871 hectares of oil palm on the island of Sumatra and a cumulative 1,573,733

hectares of oil palm on the island of Kalimantan, shown in Table 1.

It is important to note that the scope of this effort did not support on-the-ground verification activities, 

though visual verification was possible when high resolution (1-2 m or better) imagery was available for 

the circa 2000 time period. Due to the coarser resolution (30 meters) of the FCIR spectral imagery used 

in this analysis, the identification of small areas (neighborhood scale) of oil palm, which populate both 

islands, was not possible. In addition, we chose not to identify regions that demonstrated characteristics 

of oil palm plantation expansion (i.e. large-scale land clearing), focusing instead on the unique spectral 

features of operational oil palm plantations. Consequently, for this study, only areas demonstrating the 

spatial and spectral characteristics of medium- to large-scale, active oil palm plantations were identified

in the absence of high resolution imagery. 

Future work could focus on examining the correlation between the ROIs identified in the Landsat-based 

analysis and the high resolution analysis to better understand the value of each type of analysis. 

Additionally, road structures and land clearing evident in the high resolution data could be studied to 

predict future oil palm expansion. Finally, the circa 2000 analysis provided here as well as IAI’s circa 

2010 analysis1 can be applied to future studies to better understand and monitor oil palm expansion in 

Sumatra and Kalimantan.