BIOCLIME WP2 report Remote Sensing Solutions GmbH Final

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integration into a forest monitoring system in South Sumatra, Indonesia. Project number: 12.9013.9-001.00

Survey of biomass, carbon stocks, biodiversity, and assessment of the historic fire

regime for integration into a forest monitoring system in the Districts Musi Rawas,

Musi Rawas Utara, Musi Banyuasin and Banyuasin, South Sumatra, Indonesia

Work Package 2

Forest benchmark map and

monitoring

Prepared by:

Peter Navratil, Werner Wiedemann, Sandra Englhart, Matthias Stängel, Uwe Ballhorn, Natalie Cornish, Florian Siegert

RSS – Remote Sensing Solutions GmbH Isarstraße 3

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Table of Content

Executive summary 5

1 Introduction 7

2 Methodology 9

2.1 Preprocessing: Geometric correction 9

2.2 Preprocessing: Radiometric and atmospheric correction 11

2.3 Image segmentation 12

2.4 Object-based land and forest cover classification 13

2.5 Ground truth: Collection of land cover reference data to validate the forest benchmark and

updated map 16

2.6 Accuracy assessment of the land cover classifications 17

2.7 Land cover change detection 17

2.8 Carbon calculation for land cover classes 17

2.9 Calculation of the deforestation rate 18

3 Results 19

3.1 Time step 1 (2014): Land cover maps and tables 19

3.2 Time step 2 (2016): Land cover maps and tables 40

3.3 Land cover change analysis 61

3.4 Time step 1 (2014): Carbon stock maps and tables 84

3.5 Time step 2 (2016): Carbon stock maps and tables 104

3.6 Carbon Stock Change 123

3.7 Deforestation rate 133

4 Summary and Conclusions 134

5 Deliverables 135

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Executive summary

With the Biodiversity and Climate Change Project (BIOCLIME), Germany supports Indonesia's efforts to reduce greenhouse gas emissions from the forestry sector, to conserve forest biodiversity of High Value Forest Ecosystems, maintain their Carbon stock storage capacities and to implement sustainable forest management for the benefit of the people. Germany's immediate contribution will focus on supporting the Province of South Sumatra to develop and implement a conservation and management concept to lower emissions from its forests, contributing to the GHG emission reduction goal Indonesia has committed itself until 2020.

One of the important steps to improve land-use planning, forest management and protection of nature is to base the planning and management of natural resources on accurate, reliable and consistent geographic information. In order to generate and analyze this information, a multi-purpose monitoring system is required.

The concept of the monitoring system consists of three components: historical, current and monitoring. This report presents the outcomes of the work package 2 “Forest benchmark mapping and monitoring” which is part of the current and monitoring components.

The key objective of this work package was the processing of high resolution satellite images in order to create a forest benchmark map for the project areas KPHP Benakat, KPHK Bentayan, Dangku Wildlife Reserve, Kerinci Seblat National Park, KPHP Lakitan, KPHP Lalan, Mangrove, PT Reki and Sembilang National Park, including information on forest carbon stock, forest types, deforestation and in particular forest degradation. The first update of the map was done in 2016 based on newly acquired data in order to assess/monitor the changes of forest carbon stocks for the years 2014 to 2016 under project conditions.

The benchmark land cover maps produced have a high overall thematic accuracy (84.4 % according to the BAPLAN classification scheme) which makes them an ideal basis for future monitoring. The most recent classification was completed in 2016 and was based on RapidEye imagery. Kerinci Seblat National Park is the only region where RapidEye coverage was incomplete and a filling with Landsat-8 imagery was required. The way the classification algorithm was designed allowed an easy transfer and application to the images from the second time step and also enabled the maps from the second time step to achieve an overall accuracy of 87%.

A combination of these land cover maps, forest inventory and relevant LiDAR based AGB modeling provided information on forest carbon stocks. This data showed a decrease in carbon stocks across each AOI during the study period.

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KPHP Bentayan experienced a reduction of average carbon stock per hectare by around 6%, documenting a deforestation rate of 5.4%. Furthermore, although carbon losses in non-forest classes were greater than those in forest classes, net forest loss still amounted to 1,196 ha in Bentayan.

Dangku Wildlife Reserve experienced immense carbon stock losses within medium-density lowland dipterocarp forests (315,051 t). These led to a 6% reduction in average carbon stock per hectare, a net forest loss of 759 hectares and finally, a deforestation rate of 1.5%.

PT Reki, Sembilang National Park and Mangrove experienced an 8% loss in average carbon stock per hectare. However the percent deforestation rate in Sembilang National Park (7.4%, 21,244 ha) and Mangrove (7.1%, 1,936 ha) was more than twice the amount (3%, 3,732 ha) lost in PT Reki.

The second largest reduction in average carbon stock amounted to 11% and was found in KPHP Lakitan. Within Lakitan, medium-density lowland dipterocarp forest lost approximately 0.6 million tons, the net forest loss amounted to 3,717 hectares and a deforestation rate of 10.4% was recorded.

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1

Introduction

The key objective of this work package was the processing of high resolution satellite images in order to create a forest benchmark map for the project areas KPHP Benakat, KPHK Bentayan, Dangku Wildlife Reserve, Kerinci Seblat National Park, KPHP Lakitan, KPHP Lalan, Mangrove, PT Reki and Sembilang National Park, including information on forest carbon stock, forest types, deforestation and in particular forest degradation. The first update of the map was done in 2016 based on newly acquired data in order to assess/monitor the changes of forest carbon stocks for the years 2014 to 2016 under project conditions.

Because it was not possible to cover the whole project area by SPOT images, it was necessary to acquire additional data with similar spectral and spatial characteristics. On this account also RapidEye data was used for generating the benchmark maps.

Figure 1: The BIOCLIME districts and the nine project areas (Land cover maps TS1).

The forest and land cover benchmark maps for the BIOCLIME project area is therefore based on high resolution SPOT-6 and RapidEye satellite images. This benchmark maps form the basis of the monitoring system. The maps accurately distinguish different forest types and degradation stages and deliver accurate spatial carbon stock information.

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methodology for monitoring. The processing methodology is designed in such a way that it can further be applied to future SPOT or RapidEye images so that the monitoring process can be continued. The analysis procedures are described in detail in the following sections.

Figure 2: Workflow of the land cover/ forest benchmark mapping.

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2

Methodology

2.1Preprocessing: Geometric correction

The first step of the preprocessing was the geometric correction of the used SPOT-6 and RapidEye images. The SPOT and RapidEye imagery was delivered as “Primary product” (SPOT) and Level 1b, which are the “Basic Product” preprocessing level of both data types. The images are oriented in sensor geometry (path-up), with an equalized radiometry in 12-bit resolution (16-bit data type) and not georeferenced. The exterior orientation in terms of random polynomial coefficients (RPC) is provided with the data.

A geometric correction including orthorectification of the images was carried out based reference ground control points derived from a) the aerial photos collected during the LiDAR survey in October 2014 (which was referenced to a network of benchmarks of the Indonesian Geodetic Agency BIG) and Landsat satellite imagery (which was used in the historic land cover assessment by ICRAF). In order to apply terrain rectification, the digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) with a global resolution of 30 m was used. Based on the RPCs of the imagery, the GCPs and the DEM, a sensor specific model for SPOT-6 and RapidEye was used for geometric correction in the software ERDAS Imagine 2014. Geometric accuracy is generally below 0.5 pixels of the reference data.

Table 1: Accuracy of the orthorectification per scene.

AOI Scene ID

Numbe r of GCPs RMSE (input pixels ) Ti m e st ep 1 SPOT-6 Sembilang National Park

dim_spot6_ms_201407150307149_sen_1132422101 271 0.67 dim_spot6_ms_201406210252359_sen_1132423101 117 0.52 dim_spot6_ms_201403280255215_sen_1132424101 282 0.41

Mangrove dim_spot6_ms_201407150307482_sen_1132425101 94 1.11 dim_spot6_ms_201406210252359_sen_1132426101 149 0.56

KPHP Lalan

dim_spot6_ms_201407150307312_sen_1132427101 106 0.84 dim_spot6_ms_201407150307149_sen_1132428101 50 0.88 dim_spot6_ms_201403280255215_sen_1132429101 148 0.72

RapidEye

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2015-06-22T041141_RE5_1B-NAC_22087364_308153

123

2.28

KPHP Benakat 2015-06-27T041634_RE5_1B-_{NAC_22087400_308152} 217 _1.68

KPHP Lakitan 2015-06-27T041631_RE5_1B-NAC_22087821_308156 183 2.43

2014-06-17T042407_RE1_1B-NAC_22087555_308154 107 3.54

2013-08-23T042741_RE3_1B-NAC_22088153_308156 269 2.50 2013-06-19T042307_RE5_1B-NAC_22087822_308154 22 3.45 Dangku Wildlife Reserve 2015-06-27T041623_RE5_1B-NAC_22087002_308151 283 0.33 2015-07-23T042348_RE2_1B-NAC_22087008_308151 80 1.85

PT Reki 2013-06-19T042253_RE5_1B-_{NAC_22087098_308150} 323 _0.33

Kerinci Seblat National Park

2013-08-23T042744_RE3_1B-NAC_22088418_308154 540 2.25

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2016-08-07T035516_RE3_1B-NAC_27170946_457582 473 0.89

Kerinci Seblat National Park 2016-05-24T040146_RE4_1B-NAC_26411814_468622 473 1.54 2016-08-06T041820_RE1_1B-NAC_27153188_457582 433 2.66 KPHP Lalan 2016-02-15T040653_RE5_1B-NAC_26410050_468622 650 1.08 2016-08-07T035516_RE3_1B-NAC_27170946_457582 473 0.89

KPHP Lakitan 2016-07-01T035853_RE4_1B-_{NAC_26481814_457582} 643 _1.2

Mangrove

2016-02-15T040653_RE5_1B-NAC_26410050_468622 650 1.08

2016-06-26T035406_RE4_1B-NAC_26391888_457582 300 1.28 PT Reki 2016-07-01T035853_RE4_1B-NAC_26481814_457582 643 1.2 2015-06-27T041623_RE5_1B-NAC_22087002_308151 283 0.33 Sembilang National Park 2016-02-15T040653_RE5_1B-NAC_26410050_468622 650 1.08 2016-06-26T035406_RE4_1B-NAC_26391888_457582 300 1.28

2.2Preprocessing: Radiometric and atmospheric correction

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2.3Image segmentation

The satellite images were then used as input to land cover classification using an object-based image analysis. This methodology classifies spatially adjacent and spectrally similar groups of pixels, so called image objects, rather than individual pixels of the image based on the multiresolution segmentation algorithm. Table 2 lists the segmentation parameters used.

Table 2: Segmentation parameters.

Satellite Parameter Setting

SPOT Bands blue, green, red, nir

Band weighting 0, 2, 2, 1

Scale parameter 45

Shape criterion 0.7

Compactness criterion 0.8

RapidEye Bands blue, green, red, red edge, nir

Band weighting 0, 1, 1, 1, 1

Scale parameter 85

Shape criterion 0.8

Compactness criterion 0.7

Traditional pixel-based classification uses multi-spectral classification techniques that assign a pixel to a class by considering the spectral similarities with the class or with other classes. The resulting thematic classifications are often incomplete and non-homogeneous, in particular when being applied to high resolution satellite data and when mapping spectrally heterogeneous classes such as forest. The received signal frequency does not clearly indicate the membership to a land cover class, e.g. due to atmospheric scattering, mixed pixels, or the heterogeneity of natural land cover.

Improving the spatial resolution of remote sensing systems often results in increased complexity of the data. The representation of real world objects in the feature space is characterized by high variance of pixel values, hence statistical classification routines based on the spectral dimensions are limited and a greater emphasis must be placed on exploiting spatial and contextual attributes (Matsuyama, 1987; Guindon, 1997, 2000). To enhance classification, the use of spatial information inherent in such data was proposed and studied by many researchers (Atkinson and Lewis, 2000).

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Kartikeyan et al., 1998). In this context, the use of object-oriented classification methods on remote sensing data has gained immense popularity, and the idea behind it was subject to numerous investigations since the 19970’s (Kettig and Landgrebe, 1976; Haralick and Joo, 1986; Kartikeyan et al., 1995).

In the object-oriented approach in a first step a segmentation of the imagery generates image objects, combining neighboring pixel clusters to an image object. Here the spectral reflectance, as well as texture information and shape indicators are analyzed for generating the objects. The attributes of the image objects like spectral reflectance, texture or NDVI are stored in a so called object database (Benz et al., 2004).

2.4Object-based land and forest cover classification

The classification of the image objects was built on expert knowledge and a complex database query by formulating rule-set with threshold based classification rules, combined with training based machine learning techniques (Support Vector Machine) for certain class discriminations. Sample objects for the training of the SVM classifier were selected randomly and assigned to their respective class by the interpreter in order to accurately distinguish the land cover classes.

This approach consists of three procedures (Figure 3):

 Design of a class hierarchy: Definition of land cover classes and inheritance rules between parent and child classes

 Image segmentation: The input image raster dataset is segmented into homogenous image objects according to their spectral and textural characteristics

 Classification:The image objects are assigned to the predefined classes according to decision rules which can be based on spectral, spatial, geometric, thematic or topologic criteria and expert knowledge

RapidEye satellite image Image segmentation Classification

Figure 3: Example of the basic procedures of an object-based image analysis. The input image dataset (left) is first segmented into homogenous image objects (middle) which are then assigned to predefined classes according to decision rules (left).

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Spectral enhancements have been applied to the satellite images, e.g. the calculation of vegetation indices or Spectral Mixture analysis, in order to enhance classification accuracy.

The rule-set was designed in such a way that it can easily be applied to images acquired at different points in time with only minor adjustments. This design facilitates the application of the rule-set in the framework of a monitoring system for the BIOCLIME project area.

Table 3: Classification scheme

BAPLAN Classification scheme Indonesian name Baplan Code

Bioclime class

Baplan-enhanced code

Primary dry land forest

Hutan lahan kering primer

2001 High-density Lowland Dipterocarp Forest

2001-1

High-density Lower Montane Rainforest

2001-2

High-density Upper Montane Rainforest

2001-3

Secondary/ logged over dry land forest Hutan lahan kering sekunder/ bekas tebangan

2002 Medium-density Lowland Dipterocarp Forest

2002-1

Low-density Lowland Dipterocarp Forest

2002-2

Medium-density Lower Montane Rainforest

2002-3

Low-density Lower Montane Rainforest 2002-4 Medium-density Upper Montane

Rainforest

2002-5

Low-density Upper Montane Rainforest 2002-6 Primary swamp forest Hutan rawa primer

2005 High-density peat swamp forest 2005-1 Permanently inundated peat swamp

forest

2005-2

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High-density freshwater swamp forest 2005-4

Heath forest 2005-5

Secondary/ logged over swamp forest

Hutan rawa sekunder/ bekas tebangan

20051 Medium-density peat swamp forest 20051-0 Low-density peat swamp forest 20051-1 Regrowing peat swamp forest 20051-2 Low-density back swamp forest 20051-3 Regrowing back swamp forest 20051-4 Medium-density Freshwater Swamp

Forest

20051-5

Low-density Freshwater Swamp Forest 20051-6 Primary mangrove

forest

Hutan mangrove primer

2004 Mangrove 1 2004-1

Mangrove 2 2004-2

Nipah Palm 2004-3

Secondary/ logged over mangrove forest Hutan mangrove sekunder/ bekas tebangan

9999 Degraded mangrove 2007-1

Young mangrove 2007-2

Mixed dryland agriculture/mixed garden Pertanian lahan kering campur semak / kebun campur

20092 Dryland agriculture mixed with shrub 20092-1

Rubber agroforestry 20092-2

Tree crop plantation

Perkebunan/ Kebun

2010 Oil palm plantation 2010-1

Coconut plantation 2010-2

Rubber plantation 2010-3

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tanaman Industrial forest 2006-2

Scrub Semak

belukar

2007 Scrubland 2007

Swamp scrub Semak

belukar rawa

20071 Swamp scrub 20071

Rice fields Sawah/ persawahan

20093 Rice field 20093

Dry land agriculture

Pertanian lahan kering

20091 Dry land agriculture 20091

Grass Rumput 3000 Grassland 3000

Open land Tanah

terbuka

2014 Bare area 2014

Settlement/ developed land

Pemukiman/ lahan terbangun

2012 Settlement 2012-1

Road 2012-2

Water body Tubuh air 5001 Water 5001

Swamp Rawa 50011 Wetland 50011

Embankment Tambak 20094 Aquaculture 20094

2.5Ground truth: Collection of land cover reference data to validate the forest

benchmark and updated map

A field survey was conducted in 2015 to assess the accuracy of the land cover and forest benchmark map, as well as the forest degradation information. This included collecting vegetation type and height, canopy cover and further information at 373 sampling sites. Ground truth data was also collected during a field survey in 2016, and used to validate the maps from time step 2. Data on a further 429 sampling locations was documented during this time.

All field samples were collected in accordance with the FAO Land Cover Classification System (LCCS) and the class hierarchy designed for Bioclime.

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In the case of the benchmark map, this was accomplished by interpreting aerial photos (acquired in 2014) with very fine spatial resolution. In order to guarantee a statistically representative validation dataset and taking time and budget into consideration, it was decided that a further 1,127 reference polygons would be derived using this method.

To ensure adequate validation of the updated map, orthomosaics collected by a UAV were used in the same fashion as the aerial photos used for time step 1. This data was acquired as part of the field survey in 2016. Additional RapidEye images which were not used for classification were also used for validation purposes. In total, 217 reference polygons generated from UAV data and 759 taken from independent RapidEye images were used to validate the maps for time step 2.

All polygons incorporated into the validation were selected by random sampling within the aerial photo transects, the UAV orthomosaics and the unused RapidEye images. This method ensures that each sample unit in the study area has an equal chance of being selected. The advantage of this approach is that it minimizes bias and is assumed to deliver representative results.

2.6Accuracy assessment of the land cover classifications

An independent accuracy assessment and verification of the classification results is an essential component of the processing chain. This is done with the help of reference data and results in an accuracy matrix that details user and producer accuracies, overall accuracy and the Kappa index (Table 13 and Table 23).

The overall accuracy represents the percentage of correctly classified reference samples, the producer’s accuracy indicates how well the reference site is classified and the user’s accuracy defines the probability that a polygon classified into a given category actually represents that category on the ground. Finally, the Kappa index indicates the extent to which the percentage correct values of an accuracy matrix are due to “true” agreement versus “chance” agreement (Lillesand et al., 2008).

2.7Land cover change detection

A detailed description of the method to assess biomass and carbon stock difference can be found in the reportWork Package 1: Quality assessment report of ICRAF historic land cover change dataset; 2.3.1

Land cover change detection methodology.

The analysis of all possible changes between the benchmark - and the updated map shows in this case a possibility of 1936 change vectors. So similar to the change analysis based on ICRAF classifications, groups of several change vectors into meaningful classes where created.

2.8Carbon calculation for land cover classes

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2.9Calculation of the deforestation rate

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3

Results

3.1Time step 1 (2014): Land cover maps and tables

The benchmark land cover map for each project area is presented in Figure 4 to Figure 12. The maps are derived from ruleset-based classifications created in eCognition.

Classifying according to the BAPLAN classification scheme produced an overall accuracy of 84.4% and a Kappa index of 0.83. The accuracy matrix can be found in Table 13.

Table 4: Spatial extent of the land cover classes in 2014 for KPHP Benakat. KPHP Benakat

Class name Area [ha] %

Medium-density lowland dipterocarp forest 260 0.4

Low-density lowland dipterocarp forest 4,978 8.1

Dryland agriculture mixed with shrub 5,852 9.5

Oil palm plantation 1,521 2.5

Acacia plantation 14,087 22.9

Industrial forest 24,319 39.5

Scrubland 1,226 2.0

Dryland agriculture 1,864 3.0

Bare area 78 0.1

Settlement 238 0.4

Road 905 1.5

Water 101 0.2

NoData 6,085 9.9

61,514 100.0

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Table 5: Spatial extent of the land cover classes in 2014 for KPHK Bentayan. KPHK Bentayan

Class name Area [ha] %

Medium-density lowland dipterocarp forest 4,633 6.3

Medium-density freshwater swamp forest 891 1.2

Low-density freshwater swamp forest 2,146 2.9

Acacia plantation 0 0.0

Scrubland 2,425 3.3

Swamp scrub 3,708 5.0

Bare area 10 0.0

Settlement 581 0.8

Road 1,591 2.2

Water 280 0.4

Wetland 906 1.2

NoData 4,495 6.1

73,846 100.00

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Table 6: Spatial extent of the land cover classes in 2014 for Dangku Wildlife Reserve. Dangku Wildlife Reserve

Class name Area [ha] %

Rubber 8 0.0

Scrubland 326 0.8

Dryland agriculture 687 1.6

Bare area 118 0.3

Road 624 1.5

Water 13 0.0

NoData 1,121 2.7

42,255 100.0

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Table 7: Spatial extent of the land cover classes in 2014 for Kerinci Seblat National Park. Kerinci Seblat National Park

Class name Area [ha] %

High-density lowland dipterocarp forest 47,733 18.5

High-density lower montane rain forest 20,515 7.9

High-density upper montane rain forest 2,017 0.8

Medium-density lower montane rain forest 5,442 2.1

Low-density lower montane rain forest 631 0.2

Low-density upper montane rain forest 17 0.0

Oil palm plantation 5 0.0

Rubber Agroforestry 62,224 24.1

Scrubland 17,041 6.6

Bare area 497 0.2

Settlement 402 0.2

Road 152 0.1

Water 2,066 0.8

Wetland 9 0.0

NoData 8,398 3.3

258,299 100.0

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Table 8: Spatial extent of the land cover classes in 2014 for KPHP Lakitan. KPHP Lakitan

Class name Area [ha] %

Rubber 19,370 22.0

Scrubland 1,058 1.2

Bare area 1,484 1.7

Water 479 0.5

NoData 45 0.1

88,044 100.0

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Table 9: Spatial extent of the land cover classes in 2014 for KPHP Lalan. KPHP Lalan

Class name Area [ha] %

High-density peat swamp forest 37,702 37.0

Permanently inundated peat swamp forest 2,377 2.3

Heath forest 934 0.9

Low-density peat swamp forest 25,848 25.4

Regrowing peat swamp forest 6,447 6.3

Dryland agriculture mixed with shrub 542 0.5

Scrubland 9,964 9.8

Bare area 3,453 3.4

Settlement 5 0.0

Water 246 0.2

NoData 1,995 2.0

101,856 100.0

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[image:30.842.56.582.110.487.2]

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[image:31.595.67.519.140.562.2]

Table 10: Spatial extent of the land cover classes in 2014 for Mangrove. Mangrove

Class name Area [ha] %

Low-density back swamp forest 603 0.6

Regrowing back swamp forest 508 0.5

Mangrove 1 7,696 7.7

Mangrove 2 289 0.3

Young mangrove 5,649 5.7

Nipah Palm 21,128 21.3

Coconut plantation 8,199 8.2

Bare area 3,148 3.2

Settlement 227 0.2

Water 21,956 22.1

Aquaculture 578 0.6

NoData 357 0.4

99,391 100.0

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[image:32.842.64.581.114.487.2]

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[image:33.595.65.521.137.489.2]

Table 11: Spatial extent of the land cover classes in 2014 for PT Reki. PT Reki

Class name Area [ha] %

High-density lowland dipterocarp forest 2,528 3.4

Dryland agriculture mixed with shrub 659 0.9

Rubber 78 0.1

Scrubland 2,120 2.9

Bare area 106 0.1

Road 608 0.8

Water 367 0.5

Wetland 2 0.0

74,152 100.0

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[image:34.842.162.677.118.486.2]

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[image:35.595.66.520.143.731.2]

Table 12: Spatial extent of the land cover classes in 2014 for Sembilang National Park.

Sembilang National Park

Class name Area [ha] %

High-density peat swamp forest 25,793 9.2

Permanently inundated peat swamp forest 1,327 0.5

High-density back swamp forest 16,557 5.9

Heath forest 1,870 0.7

Low-density peat swamp forest 6,927 2.5

Regrowing peat swamp forest 1,364 0.5

Low-density back swamp forest 16,557 5.9

Regrowing back swamp forest 10,180 3.6

Mangrove 1 73,192 26.1

Mangrove 2 6,521 2.3

Nipah palm 6,976 2.5

Degraded mangrove 2,563 0.9

Young mangrove 981 0.3

Coconut plantation 942 0.3

Bare area 1,434 0.5

Settlement 215 0.1

Water 30,262 10.8

Aquaculture 1,559 0.6

NoData 35,861 12.8

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[image:39.842.60.811.135.519.2]

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3.2Time step 2 (2016): Land cover maps and tables

For time step 2 all land cover maps are shown Figure 13 to Figure 21. The maps are derived from the rule based classification created by the use of eCognition under consideration of the benchmark maps from 2014.

[image:40.595.66.519.247.523.2]

Classifying according to the BAPLAN classification scheme produced an overall accuracy of 87% and a Kappa index of 0.86. The accuracy matrix can be found in Table 23.

Table 14: Spatial extent of the land cover classes in 2016 for KPHP Benakat.

KPHP Benakat

Class name

Area [ha]

%

Medium-density lowland dipterocarp forest

250

0.4

Low-density lowland dipterocarp forest

5,409

8.8

Dryland agriculture mixed with shrub

6,269

10.2

Oil palm plantation

1,792

2.9

Acacia plantation

17,487

28.4

Industrial forest

23,190

37.7

Scrubland

576

0.9

Dryland agriculture

1,104

1.8

Bare area

51

0.1

Settlement

239

0.4

Road

1,090

1.8

Water

116

0.2

NoData

3,941

6.4

61,514

100.0

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[image:41.842.56.585.121.489.2]

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[image:42.595.65.523.136.447.2]

Table 15: Spatial extent of the land cover classes in 2016 for KPHK Bentayan.

KPHK Bentayan

Class name

Area [ha]

%

4,720

6.4

3,603

4.9

Medium-density freshwater swamp forest

516

0.7

Low-density freshwater swamp forest

1,591

2.2

Dryland agriculture mixed with shrub

35,132

47.6

Oil palm plantation

12,169

16.5

Scrubland

2,059

2.8

Swamp scrub

4,415

6.0

Dryland agriculture

2,764

3.7

Bare area

15

0.0

Settlement

492

0.7

Road

1,657

2.2

Water

282

0.4

Wetland

906

1.2

NoData

3,524

4.8

73,846

100.0

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[image:43.842.56.586.120.490.2]

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[image:44.595.64.517.135.502.2]

Table 16: Spatial extent of the land cover classes in 2016 for Dangku Wildlife Reserve.

Dangku Wildlife Reserve

Class name

Area [ha]

%

9,288

22.0

16,051

38.0

3,005

7.1

Oil palm plantation

2,375

5.6

Rubber

12

0.0

Industrial forest

2,906

6.9

Scrubland

477

1.1

Dryland agriculture

766

1.8

Bare area

70

0.2

Road

615

1.5

Water

17

0.0

NoData

6,675

15.8

42,255

100.0

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[image:45.842.67.588.119.490.2]

(46)

[image:46.595.69.521.135.704.2]

Table 17: Spatial extent of the land cover classes in 2016 for Kerinci Seblat National Park.

Kerinci Seblat National Park

Class name

Area [ha]

%

High-density lowland dipterocarp forest

43,262

16.7

High-density lower montane rain forest

21,601

8.4

High-density upper montane rain forest

2,567

1.0

64,207

24.9

15,917

6.2

Medium-density lower montane rain forest

5,345

2.1

Low-density lower montane rain forest

617

0.2

Medium-density upper montane rain forest

12

0.0

Low-density upper montane rain forest

17

0.0

11,770

4.6

Oil palm plantation

156

0.1

Rubber Agroforestry

59,845

23.2

Scrubland

16,123

6.2

Dryland agriculture

2,025

0.8

Bare area

730

0.3

Settlement

438

0.2

Road

190

0.1

Water

2,048

0.8

Wetland

13

0.0

NoData

11,415

4.4

258,299

100.0

(47)

(48)

[image:48.842.57.580.111.487.2]

(49)

[image:49.595.65.519.140.479.2]

Table 18: Spatial extent of the land cover classes in 2016 for KPHP Lakitan.

KPHP Lakitan

Class name

Area [ha]

%

11,286

12.8

2,829

3.2

18,414

20.9

Oil palm plantation

25,883

29.4

Rubber

16,366

18.6

Industrial forest

1,031

1.2

Scrubland

2,109

2.4

Dryland agriculture

1,150

1.3

Bare area

1,366

1.6

Water

487

0.6

NoData

7,124

8.1

88,044

100.00

(50)

[image:50.842.56.578.113.487.2]

(51)

[image:51.595.68.517.139.531.2]

Table 19: Spatial extent of the land cover classes in 2016 for KPHP Lalan.

KPHP Lalan

Class name

Area [ha]

%

High-density peat swamp forest

11,437

11.2

Permanently inundated peat swamp forest

1,362

1.3

Heath forest

515

0.5

Low-density peat swamp forest

6,294

6.2

Regrowing peat swamp forest

999

1.0

964

0.9

Oil palm plantation

2,909

2.9

Acacia plantation

14,771

14.5

Scrubland

54,676

53.7

Bare area

7,548

7.4

Settlement

6

0.0

Water

310

0.3

NoData

66

0.1

101,856

100.00

(52)

[image:52.842.159.681.118.483.2]

(53)

[image:53.595.66.519.139.582.2]

Table 20: Spatial extent of the land cover classes in 2016 for Mangrove.

Mangrove

Class name

Area [ha]

%

Low-density back swamp forest

168

0.2

Regrowing back swamp forest

83

0.1

Mangrove 1

6,177

6.2

Mangrove 2

159

0.2

Young mangrove

5,146

5.2

17,425

17.5

Nipah Palm

16,424

16.5

Oil palm plantation

4,958

5.0

Coconut plantation

8,563

8.6

Scrubland

10,812

10.9

Bare area

1,937

1.9

Settlement

276

0.3

Water

20,225

20.3

Aquaculture

477

0.5

NoData

6,563

6.6

99,391

100.0

(54)

[image:54.842.64.584.114.483.2]

(55)

[image:55.595.68.519.140.532.2]

Table 21: Spatial extent of the land cover classes in 2016 for PT Reki.

PT Reki

Class name

Area [ha]

%

2,431

3.3

38,165

51.5

18,154

24.5

Dryland agriculture mixed with shrub

2,048

2.8

Rubber

117

0.2

Acacia plantation

3,532

4.8

Scrubland

2,364

3.2

Dryland agriculture

2,656

3.6

Bare area

18

0.0

Road

755

1.0

Water

344

0.5

Wetland

2

0.0

NoData

3,567

4.8

74,152

100.0

(56)

[image:56.842.159.677.117.486.2]

(57)

[image:57.595.68.520.138.762.2]

Table 22: Spatial extent of the land cover classes in 2016 for Sembilang National Park.

Sembilang National Park

Class name

Area [ha]

%

22,452

8.0

Permanently inundated peat swamp forest

978

0.3

High-density back swamp forest

5,075

1.8

Heath forest

1,839

0.7

Medium-density peat swamp forest

102

0.0

4,668

1.7

884

0.3

9,316

3.3

4,530

1.6

Mangrove 1

76,105

27.1

Mangrove 2

6,962

2.5

Nipah palm

6,236

2.2

Degraded mangrove

2,528

0.9

Young mangrove

867

0.3

7,487

2.7

Coconut plantation

1,290

0.5

Acacia plantation

13,620

4.9

Scrubland

50,651

18.0

Bare area

17,264

6.1

Settlement

293

0.1

Water

33,858

12.1

(58)

280,743

100.0

(59)

[image:59.842.64.585.121.489.2]

(60)

(61)

3.3Land cover change analysis

(62)

[image:62.842.58.779.140.476.2]

Table 24: Change process for all project areas in the investigated time period.

Process Benakat Bentayan Dangku Kerinci Lakitan Lalan Mangrove Reki Sembilang

Deforestation 456 1,168 748 2,395 3,683 53,065 5,319 3,709 21,214

12.8% 58.6% 29.9% 52.7% 64.1% 86.1% 41.7% 46.8% 86.6%

Forest degradation 2 92 1,645 118 629 2,236 0 3,895 366

0.0% 4.6% 65.7% 2.6% 10.9% 3.6% 0.0% 49.2% 1.5%

Agricultural expansion on non-forest

965 552 34 1,509 131 458 5,594 44 642

27.0% 27.7% 1.4% 33.2% 2.3% 0.7% 43.9% 0.6% 2.6%

Plantation expansion on non-forest

1,902 140 46 123 1,200 2,757 947 136 468

53.3% 7.0% 1.8% 2.7% 20.9% 4.5% 7.4% 1.7% 1.9%

Settlement expansion 8 0 0 2 0 0 44 0 22

0.2% 0.0% 0.0% 0.1% 0.0% 0.0% 0.3% 0.0% 0.1%

Flooding 0 5 6 13 10 55 38 5 280

0.0% 0.3% 0.2% 0.3% 0.2% 0.1% 0.3% 0.1% 1.1%

Land clearing 221 1 6 112 31 790 262 84 622

6.2% 0.0% 0.3% 2.5% 0.5% 1.3% 2.1% 1.1% 2.5%

Regeneration 16 35 19 274 64 2,253 550 48 889

0.5% 1.8% 0.7% 6.0% 1.1% 3.7% 4.3% 0.6% 3.6%

Total change 3,569 1,993 2,505 4,546 5,749 61,614 12,754 7,920 24,503

No change 48,881 64,227 32,047 234,206 75,126 38,182 79,731 62,665 209,418

No Data 9,063 7,626 7,703 19,547 7,168 2,061 6,906 3,567 46,821

(63)

[image:63.842.64.637.115.489.2]

(64)

Of the eight processes observed, deforestation was by far the most dominant. A minimum of 12.8% (Benakat) and as much as 86.6% of change in each project area was attributed to deforestation. Deforestation was strongest in Sembilang and Lalan, where 21,214 ha (86.6%) and 53,065 ha (86.1%) of forests were cleared respectively. Conversely, Benakat and Dangku experienced the least deforestation, losing 456 ha and 748 ha of forest respectively. Although these two regions lost the least amount of forested area, deforestation explains approximately one third (29.9%) of the changes that occurred in Dangku but only 12.8% of changes in Benakat. Sembilang and Lalan cleared the most forest but other regions saw higher rates of forest degradation. Reki lost less than one sixth of the forest removed in Sembilang but degraded almost ten times (3,895 ha) more than Sembilang. Mangrove and Benakat experienced the least forest degradation (0 and 2 ha respectively, or 0.0% of changes per region) while 1,645 hectares were degraded in Dangku, amassing to 65.7% of all changes observed there.

Although 41.7% of changes were attributed to deforestation, the highest proportion of changes in Mangrove occurred due to agricultural expansion on non-forest. This change in Mangrove (43.7%, or 5.594 ha) is more than five times greater than the next highest region (965 ha accounting for 27% of change in Benakat). In contrast, Dangku and Reki experienced the least agricultural expansion, converting 34 and 44 hectares of non-forest to agriculture respectively. Change in Reki was also only 0.6% due to agricultural expansion.

Reki also saw the least amount of plantation expansion onto non-forest (1.7% or 136 ha) with respective to total change. In terms of total area, however, Dangku converted the least non-forest to plantation (46 ha). The most plantation expansion by area was documented in Lalan (2,757 ha) but this contributed to a mere 4.5% of changed in the project area. Benakat and Lakitan converted approximately half the amount of non-forest to plantation (1.902 and 1,200 ha respectively) but found this process to be one of the leading causes of change in the region, explaining 53.3% of differences in Benakat and 20.9% of change in Lakitan.

The final variation of expansion assessed – settlement expansion – consistently described less than 0.3% of changes in any given study area. Settlements grew the most in Mangrove, where 44 hectares of land changed to settlement, and saw no change in five (Bentayan, Dangku, Lakitan, Lalan, Reki) of the nine regions.

Flooding similarly influenced very little of the changes documented but occurred consistently in each region other than Benakat. Sembilang was the most affected by flooding, where 280 hectares of dry land became wetted. Nevertheless, this change only accounts for 1.1% of all changes in Sembilang. Sembilang also cleared the second largest area of land (622 ha), surpassing all other regions but Lalan, where 790 ha were cleared. These changes describe less than 2.5% of changes in each aforementioned region, however, and explain up to 6.2% of the changes that took place in Benakat.

Benakat also revitalized the least amount of land in terms of area (16 ha), and Lalan regenerated the most (2,253 ha). This change did not explain as much of the changes in Lalan as it did in Mangrove and Kerinci, however, where 6% of overall change could be attributed to regeneration.

Overall, Lalan experienced at least four and one-half times more change than any other study area. In terms of hectares affected, Lalan also showed more change than all the other regions combined. Inversely, Bentayan varied the least between the two study periods, changing only 1,993 hectares of its land cover.

(65)

[image:65.842.63.576.119.477.2]

(66)

Table 25: Spatial extent of the land cover classes in 2014 and 2016 for KPHP Benakat in consideration of common no data.

KPHP Benakat

2014

2016

Change

[%]

Class name

Area [ha]

256

251

-1.8

4,906

4,447

-9.4

5,338

5,740

7.5

Oil palm plantation

1,521

1,792

17.9

Acacia plantation

13,340

16,809

26.0

Industrial forest

22,876

20,469

-10.5

Scrubland

1,206

440

-63.5

Dryland agriculture

1,742

1,056

-39.4

Bare area

76

48

-37.5

Settlement

226

233

3.2

Road

865

1,067

23.3

Water

100

0.1

NoData

9,063

0.0

61,513

0.0

(67)

[image:67.595.69.521.152.479.2]

Table 26: Spatial extent of the land cover classes in 2014 and 2016 for KPHK Bentayan in consideration of common no data.

KPHK Bentayan

2014

2016

Change

[%]

Class name

Area [ha]

4,595

4,431

-3.6

3,473

3,373

-2.9

Medium-density freshwater swamp forest

891

515

-42.2

2,146

1,591

-25.9

25,193

33,159

31.6

Oil palm plantation

10,673

10,921

2.3

Scrubland

2,406

1,932

-19.7

Swamp scrub

3,707

4,402

18.8

Dryland agriculture

9,951

2,708

-72.8

Bare area

10

13

28.9

Settlement

475

0.0

Road

1,516

1,517

0.1

Water

277

278

0.1

Wetland

906

0.0

NoData

7,626

0.0

73,846

0.0

(68)

[image:68.842.64.584.113.487.2]

(69)

(70)

Table 27: Spatial extent of the land cover classes in 2014 and 2016 for Dangku Wildlife Reserve in consideration of common no data.

Dangku Wildlife Reserve

2014

2016

Change

[%]

Class name

Area [ha]

10,980

9,127

-16.9

14,343

15,438

7.6

2,512

2,902

15.5

Oil palm plantation

2,320

2,336

0.7

Rubber

8

12

45.6

Industrial forest

2,747

2,831

3.0

Scrubland

255

452

76.8

Dryland agriculture

669

758

13.3

Bare area

112

70

-37.4

Road

594

610

2.7

Water

11

17

58.8

NoData

7,703

0.0

42,255

0.0

(71)

Table 28: Spatial extent of the land cover classes in 2014 and 2016 for Kerinci Seblat National Park in consideration of common no data.

Kerinci Seblat National Park

2014

2016

Change

[%]

Class name

Area [ha]

43,036

42,849

-0.4

19,197

19,185

-0.1

High-density upper montane rain forest

2,013

2,014

0.0

63,669

62,523

-1.8

16,560

15,517

-6.3

5,312

5,283

-0.5

Low-density lower montane rain forest

621

616

-0.8

2,013

2,014

0.0

2,292

11,591

405.7

Oil palm plantation

5

139

2,680.0

Rubber agroforestry

60,357

57,977

-3.9

Scrubland

16,259

15,822

-2.7

Dryland agriculture

6,383

1,898

-70.3

Bare area

489

718

46.8

Settlement

400

403

0.8

Road

140

185

32.1

Water

1,996

2,007

0.6

Wetland

9

0.0

NoData

19,547

0.0

(72)

(73)

(74)

(75)

Table 29: Spatial extent of the land cover classes in 2014 and 2016 for KPHP Lakitan in consideration of common no data.

KPHP Lakitan

2014

2016

Change

[%]

Class name

Area [ha]

14,786

11,280

-23.7

3,040

2,829

-6.9

14,557

18,411

26.5

Oil palm plantation

24,155

25,859

7.1

Rubber

16,098

16,366

1.7

Industrial forest

1,085

1,031

-5.0

Scrubland

1,026

2,099

104.6

Dryland agriculture

4,321

1,148

-73.4

Bare area

1,331

1,366

2.6

Water

476

487

2.1

NoData

7,168

0.0

88,043

0.0

(76)

Table 30: Spatial extent of the land cover classes in 2014 and 2016 for KPHP Lalan in consideration of common no data.

KPHP Lalan

2014

2016

Change

[%]

Class name

Area [ha]

37,676

11,141

-70.4

2,377

1,362

-42.7

Heath forest

934

515

-44.9

25,845

6,074

-76.5

6,425

989

-84.6

538

957

78.0

Oil palm plantation

1,313

1,310

-0.2

Acacia plantation

11,035

15,838

43.5

Scrubland

9,964

53,902

441.0

Bare area

3,436

7,405

115.5

Settlement

5

6

12.9

Water

246

296

20.5

NoData

2,061

0.0

101,857

0.0

(77)

(78)

(79)

Table 31: Spatial extent of the land cover classes in 2014 and 2016 for Mangrove in consideration of common no data.

Mangrove

2014

2016

Change

[%]

Class name

Area [ha]

476

168

-64.7

402

83

-79.3

Mangrove 1

7,211

6,167

-14.5

Mangrove 2

262

158

-39.7

Young mangrove

5,304

5,142

-3.1

11,436

17,342

51.6

Nipah Palm

19,843

16,368

-17.5

Oil palm plantation

4,936

4,958

0.4

Coconut plantation

7,589

8,464

11.5

Scrubland

11,170

10,763

-3.6

Bare area

2,967

1,923

-35.2

Settlement

227

275

21.0

Water

20,212

20,197

-0.1

Aquaculture

451

477

5.9

NoData

6,907

0.0

99,392

0.0

Mangrove’s forest classes experienced the highest declines in area. More specifically, low-density back swamp forest lost 64.7% of its cover with respect to 2014 and regrowing back swamp forest decreased by 79.3% of its original cover. The three mangrove classes also retreated in size, though not as strongly, losing between 3.1% (young mangrove) and 39.7% (mangrove 1) of their original areas. Dryland

(80)

Table 32: Spatial extent of the land cover classes in 2014 and 2016 for PT Reki in consideration of common no data.

PT Reki

2014

2016

Change

[%]

Class name

Area [ha]

2,436

2,431

-0.2

44,298

38,165

-13.8

15,753

18,160

15.3

624

2,048

228.0

Rubber

78

117

50.6

Acacia plantation

2,299

3,532

53.6

Scrubland

2,022

2,358

16.6

Dryland agriculture

2,043

2,656

30.0

Bare area

100

18

-82.1

Road

583

755

29.5

Water

346

344

-0.8

Wetland

2

0.0

NoData

3,567

0.0

74,152

0.0

(81)

(82)

(83)

Table 33: Spatial extent of the land cover classes in 2014 and 2016 for Sembilang National Park in consideration of common no data.

Sembilang National Park

2014

2016

Change

[%]

Class name

Area [ha]

25,186

21,191

-15.9

1,327

921

-30.6

High-density back swamp forest

4,371

4,065

-7.0

Heath forest

1,870

1,780

-4.8

0

99

-Low-density peat swamp forest

6,798

4,284

-37.0

1,363

839

-38.5

15,912

8,774

-44.9

10,108

4,368

-56.8

Mangrove 1

70,520

69,963

-0.8

Mangrove 2

6,287

6,215

-1.2

Nipah palm

6,032

5,625

-6.8

Degraded mangrove

2,113

2,291

8.4

Young mangrove

856

827

-3.4

4,721

5,119

8.4

Coconut plantation

942

1,125

19.4

Acacia plantation

6,651

6,981

5.0

Scrubland

35,743

42,035

17.6

Bare area

1,427

15,503

986.0

Settlement

215

234

8.7

(84)

NoData

46,820

0.0

280,742

0.0

With the exception of medium-density peat swamp forest, all forest classes in Sembilang National Park saw a drop in area between 2014 and 2016. These declines ranged in scale from a loss of 4.8% of the original heath forest to 56.8% of the original regrowing back swamp forest. The opposite holds true for non-forest classes. All non-forest classes expanded compared to their original size. These increases ranged from a 0.6% increase of water to a ten-fold (986%) increase of bare area.

3.4Time step 1 (2014): Carbon stock maps and tables

(85)

(86)

Table 34: Carbon stock per land cover classes in 2014 for KPHP Benakat.

KPHP Benakat

Class name

Carbon [t]

43,450

407,195

101,428

Oil palm plantation

20,529

Acacia plantation

426,869

Industrial forest

720,588

Scrubland

23,508

Dryland agriculture

41,804

Bare area

-Settlement

-Road

-Water

-1,785,371

(87)

Table 35: Carbon stock per land cover classes in 2014 for KPHK Bentayan.

KPHK Bentayan

Class name

Carbon [t]

781,169

288,288

Medium-density freshwater swamp forest

85,519

114,808

478,676

Oil palm plantation

144,087

Scrubland

46,925

Swamp scrub

27,803

Dryland agriculture

238,818

Bare area

-Settlement

-Road

-Water

-Wetland

9,965

2,216,057

(88)

(89)

(90)

Table 36: Carbon stock per land cover classes in 2014 for Dangku Wildlife Reserve.

Dangku Wildlife Reserve

Class name

Carbon [t]

1,866,598

1,190,509

47,736

Oil palm plantation

31,319

Rubber

739

Industrial forest

86,537

Scrubland

4,981

Dryland agriculture

16,050

Bare area

-Road

-Water

-3,244,469

(91)

Table 37: Carbon stock per land cover classes in 2014 for the Kerinci Seblat National Park AOI.

Kerinci Seblat National Park

Class name

Carbon [t]

12,566,512

6,267,821

High-density upper montane rain forest

305,976

10,823,730

1,374,480

1,405,024

Low-density lower montane rain forest

83,214

193,248

43,548

Oil palm plantation

68

Rubber agroforestry

5,251,059

Scrubland

317,051

Dryland agriculture

153,192

Bare area

-Settlement

-Road

-Water

-Wetland

99

38,785,021

(92)

(93)

(94)

Table 38: Carbon stock per land cover classes in 2014 for KPHP Lakitan.

KPHP Lakitan

Class name

Carbon [t]

2,513,673

252,298

276,581

Oil palm plantation

326,087

Rubber

1,489,039

Industrial forest

34,183

Scrubland

20,006

Dryland agriculture

103,707

Bare area

-Water

-5,015,574

(95)

Table 39: Carbon stock per land cover classes in 2014 for KPHP Lalan.

KPHP Lalan

Class name

Carbon [t]

5,406,553

290,044

Heath forest

104,610

1,421,457

353,392

10,221

Oil palm plantation

17,732

Acacia plantation

353,129

Scrubland

194,288

Bare area

-Settlement

-Water

-8,151,426

(96)

(97)

(98)

Table 40: Carbon stock per land cover classes in 2014 for Mangrove.

Mangrove

Class name

Carbon [t]

25,453

21,512

Mangrove 1

966,328

Mangrove 2

26,181

Young mangrove

172,374

Dryland agriculture mixed with shrub

217,278

Nipah Palm

1,140,997

Oil palm plantation

66,631

Coconut plantation

220,076

Scrubland

217,810

Bare area

-Settlement

-Water

-Aquaculture

-3,074,640

(99)

Table 41: Carbon stock per land cover classes in 2014 for PT Reki.

PT Reki

Class name

Carbon [t]

711,440

Medium-density lowland dipterocarp forest

7,530,680

1,307,494

11,859

Rubber

7,193

Acacia plantation

73,556

Scrubland

39,425

Dryland agriculture

49,042

Bare area

-Road

-Water

-Wetland

22

9,730,711

(100)

(101)

(102)

Table 42: Carbon stock per land cover classes in 2014 for Sembilang National Park.

Sembilang National Park

Class name

Carbon [t]

3,614,141

161,909

High-density back swamp forest

559,483

Heath forest

209,410

-Low-density peat swamp forest

373,880

74,945

Low-density back swamp forest

851,276

540,779

Mangrove 1

9,449,688

Mangrove 2

628,729

Nipah palm

346,862

Degraded mangrove

78,164

Young mangrove

27,823

89,700

Coconut plantation

27,317

Acacia plantation

212,822

Scrubland

696,995

Bare area

-Settlement

-Water

-Aquaculture

(103)

(104)

3.5Time step 2 (2016): Carbon stock maps and tables

The maps and tables in Chapter 3.5 show the carbon stock for time step 2 in the nine project areas. The reader is reminded that, in order to make the calculations comparable, a common no data mask was applied, i.e. only areas which are cloud free and covered with data in time steps 1 and time step 2 are considered in the assessment.

Table 43: Carbon stock per land cover classes in 2016 for KPHP Benakat. KPHP Benakat