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High Resolution Land Use Change 

Analysis of Oil Palm Plantations in 

Sumatra and Kalimantan Circa 2010

Final Report

Task Order #3

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

September 20, 2010

Technical Point of Contact:

Frank Padula

5160 Parkstone Drive, Suite 230

Chantilly, VA 20151

Voice: (703)-378-8672 x400

Fax: (703)-378-8978

Email: fpadula@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 ...........................................................................3

2.3.1 Visual Analysis.......................................................................................................................4

2.3.2 Quantitative Analysis............................................................................................................6

2.4 High Resolution Data Analysis ......................................................................................................7

2.4.1 High Resolution Data Analysis Discussion.............................................................................9

3 Summary .............................................................................................................................................10
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1 Overview

IAI identified and mapped oil palm plantations in major oil palm producing regions, specifically the 

Indonesian islands of Sumatra and, using available remote sensing data. IAI used and expanded upon 

the analytical approach outlined in the Final Report for Task Order #2 to evaluate land use categories in 

palm oil producing regions.

IAI constructed 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 

quantitatively to ensure a robust analysis. The results from the Landsat-based analysis were exported 

into Google Earth. High resolution imagery was obtained where possible to augment the Google Earth 

environment and mitigate data gaps in the IAI analysis. 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 oil palm plantations to the extent possible by searching for and manually analyzing the imagery

available via data archives. This detailed assessment using high resolution remote sensing data yielded 

an estimated ground cover area for oil palm of 2401871 hectares for Sumatra and 1573733 hectares for 

Kalimantan.

2 Map Oil Palm Plantations with Remote Sensing Analysis

2.1 Oil Palm Overview

Oil palm trees, which are native to Africa, Central American 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 2002. By 2007, 

oil palm plantations covered 11% of Malaysia1 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 Sulawesi. Indonesia’s agricultural minister stated in December 

2009 that Indonesia had 18 Million hectares of land suitable for oil palm cultivation, 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 

Sagama2. 

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, ideally immediately following land clearing, 

leguminous cover plants are established to prevent soil erosion, improve soil structure and palm root 

development, increase the response to mineral fertilizer, and reduce the danger of micronutrient 

 

1

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

2

http://news.mongabay.com/2009/1202-indonesia_palm_oil.html
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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 functional leaves. Oil palm trees are typically planted 

in rows and columns as shown in Figure 13 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.

 

3

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]. 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 quantitatively to ensure a robust analysis. The results from the Landsat-based analysis were 

exported into an augmented Google Earth environment, where high resolution imagery 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 initial assessments of oil palm plantation land use for the islands of Sumatra and 

Kalimantan using three independent image analysts and an automated target detection algorithm 

developed as part of this effort. Landsat data from 2008 to 2010 was used (obtained through

http://glovis.usgs.gov/). For each area positively identified as oil palm, a Region of Interest (ROI) was 

created, resulting in a total of four sets of ROIs (one for each analysis). The full suite of ROIs from the 
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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 three independent analysts and the automated target detection algorithm overlaid on 

a Google Earth image of the islands of Sumatra and Kalimantan. 

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

detection algorithm (teal). 

2.3.1 Visual Analysis

IAI created two cloud-free mosaics of the islands of Sumatra and Kalimantan using the highest quality 

Landsat data available from 2008 to 2010 to perform an initial visual analysis of the islands. Three 

independent analysts each performed a visual inspection of the islands of Sumatra and Kalimantan

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 

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.
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Analysis additionally revealed many areas where there are plantations currently being expanded and 

where deforestation is evident. Figure 4 shows a plantation along the edge of a forest (forest indicated 

by the lack of structure and dark orange color). 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.)

Figure 4: False color infrared composite Landsat images illustrate the structure and display uniquely identifiable 

characteristics of oil palm plantations.

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).

Forest

Oil Palm Spatial Structure

A B

Forest

Oil Palm

Plantation

Spatial Structure
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For each independent analysis, a ROI file was created and overlaid on a Google Earth map. The ROIs 

created by the independent analysts are shown above in Figure 3 and are indicated by the colors red, 

green, and orange.

2.3.2 Quantitative Analysis

A study documented in the IAI Final Report for Task Order #2 illustrated the potential for quantitative 

detection of oil palm within Landsat imagery. Leveraging the results of the previous study, IAI 

constructed an operation workflow to quantitatively identify oil palm in Landsat imagery. Using the site 

identified in the Final Report for Task Order #2 (Carey Island, Malaysia) a typical mean spectra for oil 

palm was identified. To mitigate atmospheric effects on the automated detection scheme, a mean 

spectra 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 for the regions of interest was processed uniquely. Figure 6

illustrates an example of the results of 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, is displayed in white, via the IAI algorithm. A visual inspection of the imagery indicates strong 

performance in 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 quantitative analysis provides an independent investigation on a pixel-to-pixel 

level for the probability of oil palm. This measure was taken to augment the visually analysis performed

on the Landsat data, as well as to provided 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

visual analyst.
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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 which provides on-line access and retrieval capability via the World Wide Web (WWW). 

The data is available 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 7] indicates 

that sufficient data exists to execute a targeted analysis of oil palm plantation activities between 2000 

and 2010 using high resolution remote sensing data. 

Figure 7: Survey of image data over Malaysia and Indonesia available via the WARP archive.

For each ROI created in the Landsat-based analysis, IAI identified and acquired available resolution

imagery for 2008-2010 from the WARP database. Figure 8 illustrates a clear example of the 

effectiveness of using high resolution imagery within Google Earth to aid in the identification of oil palm 

plantations, especially in plantations that do not exhibit the gridded structure. Each high resolution

image was manually inspected by image analysts. ROIs were created for each identified oil palm region 

within the image; oil palm identification was based on the guidance of the Landsat-based analysis, and 

refined by the plantation structure as well as unique oil palm canopy characteristics visible from within 

the high resolution data. Figure 9 shows the final full suite of ROIs identified as oil palm regions as a 

result of the high resolution data analysis. 

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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 Figure 8: Example of high resolution image viewed in Google Earth showing an oil palm plantation.

Figure 9: Regions of Interest identified as oil palm regions using high resolution commercial remote sensing data.
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2.4.1 High Resolution Data Analysis Discussion

In many cases, ROIs for oil palm regions identified in the Landsat-based analysis were refined in the high

resolution analysis based on the enhanced content offered by the high resolution data. An example of 

ROI refinement is presented in Figure 10. In this case, an oil palm region was identified by multiple 

analysts as well as by the automated detection algorithm using Landsat data; however, the analysts 

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

Figure 10: 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).

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; another consideration is if 

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

collection. Figure 11 illustrates an example in which only one analyst detected oil palm in the Landsat 

data; 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.
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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 provided the analysis discussed above. The IAI analysis found a cumulative 

2401870.9422 hectares (24,018,709,422 m2) of oil palm on the island of Sumatra and a cumulative 

1573732.6533 hectares (15,737,326,533 m2) of oil palm on the island of Kalimantan. It is important to 

note that the scope of this effort did not support verification activities, identification of small areas 

(neighborhood scale) of oil palm that populate both islands, or identification of regions which 

demonstrate characteristics of oil palm plantation expansion. For this task order, only areas 

demonstrating the spatial and spectral characteristics of medium-large scale active oil palm plantations 

were identified. 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 ROIs provided by this task can be applied to 

future studies to better understand and monitor oil palm expansion in Sumatra and Kalimantan.

Figure 11: 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).