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# High Resolution Land Use Change Analysis for Sumatra and Kalimantan

Circa 2000

Final Report Task Order # 4
BPA

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

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- 8672 x4 16 Fax: (703)

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- 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

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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

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3 Summary

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## 1 Overview Integrity Applications, Incorporated ( IAI ) ha s 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 period 1 to 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 Lib rary (UNIL) , among others

. Analysis of oil palm plantation areas using Landsat data was performed both visually and through an automated detection algorithm t o ensure a robust analysis. The results from the Landsat

based analysis were exported into Google Earth

. Where available , h igh 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, w hich 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. U sing high resolution remote sensing data yielded an estimated ground cover area for oil palm of 2 , 424 , 14 4 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 2002 2

. By 2007, oil palm plantations covered 11% of Malaysia 2 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 cultivation 3 , 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 3

. 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

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- 58 3 http:/ /news.mongabay.com/2009/1202

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- 180 Page 2 of 14 established to prevent soil erosion, improve soil structur e 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 a nd columns as shown in Figure 1 4 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 oi l 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 pattern s and the distinctive crown shape of oil palm trees in their Landsat and high resolution imagery analys e s. The typical grow th 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. P lantation 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.htmUNCLASSIFIED

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### 2.2 IAI Analysis of Oil Palm Plantations

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

IAI constru cted 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 period 1

. The approach leveraged data from the complete Landsat archive, high resolution data via Google Earth , and high

- resolution 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 a Google Earth environment augmented with high

- resolution data from the NGA UNIL. The augmented Google EarthUNCLASSIFIED

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### 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 2010 1

. This analysis leveraged 15

- 30 meter Landsat imagery available from t he Landsat Global Land Survey (GLS) 5 for the Indonesian Islands of interest. For this analysis, Landsat imager y 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 result ed 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. H eadley, 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.UNCLASSIFIED

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- 180 Page 5 of 14 2.3.1 Manual Detection IAI constructed two “ cloud

- free ” mosaics of the islands of Sumatra and Kalimantan using the highest resolution (15

- 30 m eters ) 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 plantation s. Figure 4 illustrate s 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 col ors 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 a nalysis 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 col or 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 o f 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.UNCLASSIFIED

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- 180 Page 6 of 14 Figure 5: Example of a plantation along the edge of a large forest region. Notice the road structures identified in the for est region (indicates likely expansion).
2.3.2 Automatic Detection Algorithm IAI’s study on data availability for oil palm id entification in the circa 2010 time period illustrated the potential for quantitative detection of oil palm within Landsat imagery. Leveraging th e results of this previous study, IAI implemented an operation workflow to quan titatively identify oil palm in Landsat imagery for their circa 2010 time period analysis of oil palm plantations in the Indonesian islands of Sumatra and Kalimantan 1

. This work flow 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 spectr um for oil palm was identified. To mitigate the seasonal impacts o f solar illumination angle and monthly atmospheric effects , a mean spectr um for oil palm was obtained f rom 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. E ach Landsat image within Sumatra and Kalimantan was pro cessed 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 pal m using the input spectral data. To ensure perfo rmance 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 theUNCLASSIFIED

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- to

- pixel level to identify likely oil palm pixe l 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 invest igate 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 l imit s the size of oil palm plantation that can be detected through our method. The spatial resolution of the FCIR product is 30 m eters , meaning that a single pixel would overlap approximately three rows by three columns of oil palm trees planted as in Figu re 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 imag e analysts during manual detection

.

Figure 7 : Comparison of 10 hectares (the minimum candidate oil palm plantation size identifiable t hrough 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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- 180 Page 8 of 14 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 h ectares [ Figure 7 ]

. This implies that small

- scale or neighborhood

- sized plantations would not be detected for areas where th e Landsat 15

- 30 m eter 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 th e accuracy we were able to achieve with the Landsat data, even though high er 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 t o 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) ope rates 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 usi ng a .mil or .gov email extension. A n 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 con ducted 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 AvailableUNCLASSIFIED

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- 180 Page 9 of 14 In both the circa 2000 and circa 2010 analyses, a fter 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 an d Kalimantan within the analysis time period

. A nalysts 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 databa se that met the search criteria was higher than the number of images downloaded, but due to excessive cloud cover or i mages 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 con cert with the 30 m eter resolution Landsat imagery available for the entire region [ Figure 9 ]. Figure 9 ( A ) illustrates a clear exam ple 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 identi fied oil palm region.

Positive o il 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 resoluti on 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 characte ristics 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.UNCLASSIFIED

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- 180 Page 10 of 14 Figure 9 (B) : Imagery analyzed in the Google Earth Environment: visual e xtrapolation 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 FC IR imagery, are clearly identifiable in high resolution imagery.

Figure 10 : (A) I llustrates small

- scale oil palm plantations identified using high resolution imagery in Google Earth; (B) S hows a high resolution image zoom, where the distinctive crown shape of oil palm trees are identified; (C) D emonstrates the inability to identify small

- scale oil palm plantations on the order of 1 ha in 30 meter Landsat FCIR imagery.UNCLASSIFIED

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### 2.5 Final Analysis and Unc ertainty Discussion

Figure 11 : Regions of Interest i dentified 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 analys e s and the Landsat

- based automated detection algorithm , leveraging the Google Earth environment and high re solution 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 Kalimantan 1 . 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 (specificall y oil palm, rubber tree and coconut).UNCLASSIFIED

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- 180 Page 12 of 14 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 (purpl e area). Figure courtesy of ( IAI, 2010) 1

. In some cases, the high resolution analysis revealed oil palm producing regions that had not been detect ed 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 col lection 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 area s t hat 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 a rea 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 , 14 4 hectares of oil palm on the island of Sumatra and a cumulative 867 , 01 2 hectares of oil palm on the island of Kalimantan while the circa 2010 time period analysis found a cumulative 2 , 401 , 87 1 hectares of oil palm on the island of Sumatra and a cumul ative 1 , 573 , 73 3 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 , 14 4 2 , 401 , 87 1

- 22 , 27 3

- 0.9 2 Kalimantan 867 , 012 1 , 573 , 73 3 706 , 72 0 81.5 1

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 analys is. Th e 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 suggest s that minimal change occurred in cumulative area of oil palm plantation s between the circa 2000 and circa 2010 time periods

. Kalimantan, however, demonstrated broad expansion in cumulative area of oil palm plantation s between the circa 2000 and circa 2010 time periods

. Figure 14 provides a visual comparison of the identifie d 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 ).UNCLASSIFIED

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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 , 14 4 he ctares 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 , 87 1 hectares of oil palm on the island of Sumatra and a cumulative 1 , 573 , 73 3 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 m eters ) of the FCIR spectral imagery used in this analysis, the identification of small areas (neighborhood scale) of oil palm , which popu late both islands , was not possible. In addition , we chose not to identif y regions that demonstrate d 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, f or 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 ex amining 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 a s well as IAI’s circa 2010 analysi s 1 can be applied to future studies to better understand and monitor oil palm expansion in Sumatra and Kalimantan.