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- 1. SLASH AND BURN Tracking Fire-Related Land Use Change in Kalimantan, Indonesia LUKE MENARD
- 2. INDEX Introduction Background ....................................................................................... 1 Objectives .......................................................................................... 1 Factors ................................................................................................ 2 Methodology ..................................................................................... 3 Scoring Criteria ............................................................................... 4 Data Presentation Map Display ......................................................................................... 6 Layer Display ...................................................................................... 6 Frame Display ..................................................................................... 7 Scene Display ..................................................................................... 8 Data Preparation Data Acquisition .............................................................................. 10 Data Creation .................................................................................. 13 Data Conversion ............................................................................. 14 Data Interpretation Environmental Layer ..................................................................... 18 Economic Layer ............................................................................... 21 Social Layer ..................................................................................... 23 Combined Layer ............................................................................... 26 Chapter 01 Chapter 02 Chapter 03 Chapter 04
- 3. Data Analysis Model ................................................................................................. 29 Fire Layers ........................................................................................ 30 Critical Land Layers ...................................................................... 31 Critical Land Burned .................................................................... 32 Regression ....................................................................................... 34 Conclusion Discussion ........................................................................................ 37 Sources Background Sources .................................................................... 38 Data Sources ................................................................................... 38 Chapter 05 Chapter 06 Chapter 07
- 5. Background Objectives Palm oil is an essential component of a wide variety of modern products, including foods, cosmetics, and biofuels. Indonesia is the world’s largest producer of palm oil, providing nearly half of the world’s supply of the commodity. Palm oil represents the country’s top export and is a significant economic driver. However, palm oil production has lead to significant deforestation throughout Indonesia, which has the third largest tropical forest in the world.This deforestation and land use change, often conducted through slash-and- burn methods on carbon-sequestering peatland, accounts for nearly 80 percent of the country’s total green- house gas emissions and results in a drastic loss of biodiversity. Additionally, the smoke associated with the large-scale burning is a serious public health hazard, impacting the wellbeing of both Indonesian citizens and the health of residents of neighboring countries. Indonesia is capable of growing its economy, while also reducing its greenhouse gas emissions and protecting critical land with both environmental and social value. By expanding oil palm production onto already degrad- ed land and fully protecting peatland and primary and secondary forest, Indonesia can reduce its carbon emis- sions by an estimated 35 percent, with a minimal reduction in projected profits. With that in mind, this study seeks to accomplish two goals: 1. Estimate the most suitable land for future oil palm production in Kalimantan, Indonesia 2. Track 2015 fire-specific land use change to determine the characteristics of the land actually burned to clear space for future oil palm production INTRODUCTION 1
- 6. Factors To estimate the suitability of land for oil palm production, various factors were scored based on importance. Those scores were then used to construct the following 3 layers, each representing an aspect of the land’s value that influences the appropriateness of its allocation for future oil palm production: • Environmental: land that will limit the environmental impact associated with oil palm production • Economic: land that will maximize productivity and allow for the most efficient growth of oil palm • Social: land that meets community and legal needs The factors associated with each layer and within the scope of this research are as follows: Environmental Land Cover Peat Depth ProtectedAreas Water Body Buffers Economic Elevation Slope Rainfall SoilType Soil Depth Soil Acidity Soil Drainage Social Legal Classification of Land Current Forest Moratorium Logging Concessions Timber Concessions Oil Palm Concessions Indigenous Land Claims 2
- 7. Methodology 3 For each of the 3 base layers, factors were scored conditionally using Python script on a 1-3 scale: • 1: land that is suitable for oil palm production • 2: land that may be suitable for oil palm production • 3: land that is not suitable for oil palm production Inputs for each layer were then combined and scores summed spatially to determine the areas most and least suited for future palm oil production for that layer. The environmental, economic, and social layers were combined to provide overall scores across the region. This combined layer was then overlain with 2015 fire data for the region in order to calculate the total impact- ed area and to determine the characteristics of the land burned this year. Land Cover Peat Depth ProtectedAreas Water Bodies Elevation Slope Rainfall SoilType Soil Depth SoilAcidity Soil Drainage Legal Classification Forest Moratorium Logging Concessions Timber Concessions Oil Palm Concessions Land Claims Land Burned by Attribute Economic Score Social Score Overall Score 2015 Fire Data Environmental Score
- 10. Map Display Features: • North Arrows • Scales • Text • Neatlines • Legends Layer Display Features: • Symbology • Transparency • Labels Data Presentation 6 Kalimantan is the Indonesian portion of the island of Borneo in SoutheastAsia. It is divided into 56 districts across four provinces: 1. Kalimantan Barat 2. KalimantanTengah 3. KalimantanTimur 4. Kalimantan Selatan
- 11. 7 Frame Display Features • Reprojecting • Clipping • Rotating • Annotation Grouping Kalimantan is covered in en- vironmentally, economically, and socially valuable land. The primary and secondary forest on the island amount to the world’s third largest tropical forest.
- 12. 8 Scene Display Features • Base Heights • Offsets • Shading Elevation • Kalimantan’s elevation ranges from 0 to 2,294 meters Slope • Kalimantan’s slope ranges from 0% to 80% Population Density • Generally low with some increased populaton density in coastal cities • Balikpapan has the highest population density Rainfall • Precipitation ranges from 15,000 to 34,000 mm/year
- 14. Data Acquisition • Land cover • Protected Areas • Peat Depth • Water Bodies Data Preparation 5 Environmental Layer 10
- 15. Data Acquisition • Soil Acidity • Soil Drainage • Elevation • Soil Type • Soil Depth • Slope • Rainfall 5 Economic Layer 11
- 16. Data Acquisition • Legal Classification • Logging Concessions • Forest Moratorium • Timber Concessions • Indigenous Land Claims • Oil Palm Concessions 5 Social Layer 12
- 17. Data Creation Features: • Spatial Editing • Tabular Editing • Spatial Adjustment • Georeferencing 513
- 18. Data Conversion Features: • Density Mapping 5 Areas with High Oil Palm Plantation Density Areas with High Fire Density Legend Kalimantan Districts 14
- 19. Features: • Interpolation 5 2015 fires in Kalimantan, interpolated based on their brightness 15
- 20. Features: • TINning 5 Kalimantan ElevationTIN overlain with district boundaries 16
- 22. Tools Used: • Clip Data Interpretation 18 Environmental Layer A shapefile of all protected land throughout Indonesia was clipped with the Kalimantan “Districts” shapefile to create a new input layer shapefile of protected land only within Kalimantan.
- 23. Tools Used: • Union • Raster to Polygon • Add Field • Calculate Field • Buffer • Dissolve 19 Input Layers Union used to create 1 “Lakes” file Raster files converted to polygons “Score” field added to each layer Conditional python script used to calculate scores Water re- sources and protected areas buffered Layers dissolved across “score” field Union used to create 1 overall environmental layer “EnvirScore” field added Sub scores added to calculate over- all spatial scores Environmental Layer
- 25. Tools Used: • Reclassify • Raster to Polygon • Add Field • Calculate Field • Dissolve • Union 21 Large raster files reclassi- fied Raster files converted to polygons “Score” field added to each layer Conditional python script used to calculate scores Layers dissolved across “score” field Union used to create 1 overall economic layer “ProdScore” field added Sub scores added to calculate over- all spatial scores Economic Layer Input Layers
- 26. Final Economic Map 22 Overall Economic Layer
- 27. Tools Used: • Select by Attribute 23 Social Layer Indonesia’s indigenous land claims were selected by attribute and exported to create a new input layer shapefile of land claims only within Kalimantan.
- 28. Tools Used: • Dissolve • Buffer • Feature Envelope to Polygon • Add Field • Calculate Field • Union 24 “Indigenous Land Claims” points buff- ered “Legal Classifi- cation” dissolved across type Buffered points con- verted to Conditional python script used to calculate scores Layers dissolved across “score” field Union used to create 1 overall social layer “SocScore” field added Sub scores added to calculate over- all spatial scores Social Layer Input Layers “Score” field added to each layer
- 29. Final Social Map 25 Overall Social Layer
- 30. Tools Used: • Dissolve • Union • Add Field • Calculate Field 26 Scores dissolved across type Union used to create 1 combined layer “TotalScore” field added Sub scores added to calculate over- all spatial scores Combined Layer Input Layers
- 31. Final Combined Map 27 Combined Layer Combined Score 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
- 32. • Interpolation of Combined Layer Scores • Kernel Density of Combined Layer Scores • Centroids of Combined Layer Polygons 28 Combined Layer
- 34. Tools Used: • Buffer • Dissolve • Feature Envelope to Polygon • Add Geometry Attributes • Merge Branch • Select • Clip • Spatial Join • Summary Statistics Data Analysis 30 Combined lay- er dissolved by overall score Input Layers Fires buffered by 0.5 km Fires converted to 1 km2 polygons Polygon areas calculated for each layer Model branches merged Critical land deter- mined by selecting overall scores > 15 2015 burned area clipped with critical land Area of critical land burned spatially joined within districts Summary statis- tics calculated Dissolve used to calculate critical land burned per district Analysis Model
- 35. Fire Layers: • 2015 Fire Incidences • Land area burned in 2015 31 Metadata for the point shapefile of 2015 fires describes each point as “the center of a 1km pixel...containing one or more fires within that pixel. Thus, each point was buffered by 0.5km and converted to a 1km2 polygon to cre- ate a shapefile of area burned in 2015.
- 36. Critical Land Layers: • Combined Layer • Critical Land 32 Data selection was used to convert the combined layer into a shapefile of criti- cal land, least suited for future palm oil production. Regions achieving a combined score greater than 15 were considered criti- cal land.
- 37. Critical Land Burned: • Critical Land Burned • Critical Land Burned per District The critical land shapefile was clipped with total area burned in 2015 to de- termine the area of critical land burned in 2015. This land was then spatially joined within Kalimantan districts to display the amount of critical land burned per district. 33
- 38. Critical Land Burned: • Provinces Extruded by Critical Land Area Burned • Critical Land Burned per District 34 32,663 km2 of critical land were burned in 2015 Pulang Pisau had the greatest area of critical land burned in 2015, with 7,882 Km2
- 39. Regression Layers Regressed: • Total Land Burned • Land Cover • Legal Classification 35 Regression was used to determine whether a specific land use is associated was associated with a higher 2015 rate of burning in Kalimantan. Both the land cover and legal classification shapefiles were regressed with the polygon shapefile of total burned area. Neither land cover nor legal classification significantly predicted the prevalence of burning in Kalimantan, suggesting that fire-related land use change on the island does not currently occur on a specific land type. Area burned regressed with land cover Area burned regressed with legal classification
- 41. Conclusion This study sought to accomplish two goals: 1. Estimate the most suitable land for future oil palm production in Kalimantan, Indonesia 2. Track 2015 fire-specific land use change to determine the characteristics of the land ac- tually burned to clear space for future oil palm production Land was successfully valued based on various environmental, economic, and social criteria and the most and least suitable land for future palm oil production was mapped for the re- gion. Land that was deemed “critical” based on scoring of these criteria was then considered in relation to 2015 fire activity in Kalimantan. Large swaths of environmentally, economically, and socially critical land were burned throughout the region in 2015.This burning has been associated with land clearing for fu- ture oil palm production.While plantation production of the commodity is hugely profit- able to companies involved, this research has shown that current practices are degrading the landscape at an unsustainable rate. Statistical analyses found that there is currently no correlation between the land burned in 2015 and current land cover and classification. By more strategically choosing land on which future oil palm plantations are established, Indonesian companies can continue to garner significant profits, while also protecting ecologically and socially valuable land. By mapping land that is most suited to future oil palm production, we’ve taken an import- ant step towards enhancing the long-term sustainability of a booming world market. Discussion 37
- 42. Gingold, B., Rosenbarger, A., Muliastra, Y. I. K. D., Stolle, F., Sudana, I. M., Manessa, M. D. M., ... & Douard, P. (2012). “How to identify degraded land for sustainable palm oil in Indonesia.” Washington DC. Mollman, S. “Indonesia’s Palm Oil Production is Smoking Out Its Neighbors.” Quartz, 7 September 2015. Web. 14 December 2015. Stolle, F., Austin, K., Aris Payne, O. “Having it All: Indonesia Can Produce Palm Oil, Protect Forests, and Reap Profits.” World Resources Institute, 13 July 2015. Web. 14 December 2015. World Resources Institute. “Forests and Landscapes in Indonesia.” World Resources Institute, n.d. Web. 14 December 2015. Global Forest Watch. Open Data Portal. Web. Available at: http://data.globalforestwatch. org/ National Aeronautics and Space Administration. Active Fire Data. Web. Available at: https:// earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data United States Agency for International Development. Spatial Data Repository. Web. Available at: http://spatialdata.dhsprogram.com/data/#/ World Resource Institute. Suitability Mapper. Web. Available at: http://www.wri.org/applica tions/maps/suitability-mapper/index.html#v=suitability Background Sources Data Sources 38 Sources