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# High Resolution Land Use Change Analysis of Oil Palm P l antations in Sumatra and Kalimantan Circa 2010

Final Report Task Order # 3

BPA

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- 0076M
September 20, 201 0

Technical Point of Contact: Frank Padula 5160 Parkstone Drive, Suite 230 Chantilly, VA 20151 Voice: (703)

- 378

- 8672 x 400 Fax: (703)

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- 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

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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

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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

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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 use d 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 construct ed 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 t o ensure a robust analysis

. The results from the Landsat

- based analysis were exported into Google Earth

. H igh 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 fus ed the
L andsat

- based analysis with high resolution imager y in a very flexible analysis environment

. IAI mitigated c loud 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 archive s

. This detailed assessment using high resolution remote sensing data yielded an estimated ground cover area fo r oil palm of 240187 1 hectares for Sumatra and 157373 3 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 Malaysia 1 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 agr icultural 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 Sagama 2

. Oil palm plantations generally follow similar patterns for establishment and growth. Once a plantation site is ident ified, 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

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2 http://news.mongabay.com/2009/1202

- indonesia_palm_oil.htmlUNCLASSIFIED

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- 180 Page 2 of 10 de ficiencies. 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 a nd columns as shown in Figure 1 3 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.htmUNCLASSIFIED

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### 2.2 IAI Analysis of Oil Palm Plantations

Figure 2 : Oil palm plant ation 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 t o 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 assessment s of oil palm plantation land use for the islands of Sumatra and
Kalimantan using three independ ent 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 (on e fo r each analysis) . The full suite of ROIs from theUNCLASSIFIED

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- 180 Page 4 of 10 three independent analysts and the automated analysis w as 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 R OIs created by the three independent analysts (r ed, green and orange ) and the autom ated target detection algorithm (teal)

.

2.3.1 Visual Analysis
IAI created two cloud

- free mosaics of the islands of Sumatra and Kalimantan using t he highest quality
Landsat data available from 2008 to 2010 to perform an initial visual analysis of the islands

. Three independent analysts ea ch 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 illustrate s an example of the unique spatial structure and visual distinction of oil palm through a false color infrared
Landsat composite display . In Fig ure 4 (a) , the plantation structure is well defined, as are the expected
fie ld 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 character istic of a plantation identified

. Such areas were selected using a Region of Interest (ROI) tool provided by ENVI.UNCLASSIFIED

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- 180 Page 5 of 10 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 str ucture and dark orange color )

. This example demonstrates a plantation that is likely to expand since structure can be seen in the forest , however o nly the areas demonstrati ng 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 alo ng the edge of a large forest region . Notice the road structures identified in the forest region ( indicates likely expansion)

. Forest O il Palm 

## Spatial Structure A B

Forest

O il Palm
Plantation 

## Spatial StructureUNCLASSIFIED

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- 180 Page 6 of 10 For each independent analysis, a ROI file was created and overla i d on a Google Earth map

. The ROIs created by the independent analysts are shown above in Figure 3 and are in dicated 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 th e results of the previous study, IAI constructed an operation workflow to quan titatively 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
spectr a for oil palm was obtained f rom this kn own oil palm plantation site, for each month of the year. Using these spectra , da ta pre

- processing and the statistical target detection algorithms : Constrained Energy Minimization ( CEM ) and Adaptive Coherence Estimator (ACE) a quantitative detection algorithm was developed. E ach Landsat image for the regions of interest was processed uniquely

. Figure 6
illus tr ates an example of the results of the quantitative detection algorithm. Figure 6 (A) illustrates a visible band Landsat image , where Figur e 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 pal m using the input spectral data . To ensure performance and reduce false alarms ROIs were manually obtained from the qua n tit at ive results [Figure 6 (B)] by IAI analy st s. 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 Lan dsat 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 a re challenging visual analyst.UNCLASSIFIED

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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. A n 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 Indonesi a 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 hig h resolution image was manually inspected by image analysts. ROI s w ere 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 with in
the high resolution data. Figure 9 shows the final full suite of ROIs identi fied 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 AvailableUNCLASSIFIED

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- 180 Page 8 of 10 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.UNCLASSIFIED

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- 180 Page 9 of 10 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 ; h owever , 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

area s 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 provide d 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 exami ning 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).