Historical land cover classification and land cover change in the kfcp site and the kapuas district by Communications IAFCP

TECHNICAL PAPER

Historical Land Cover Classification and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) Site and the Kapuas District Florian Siegert, Peter Navratil, Jonas Franke, and Karin Kronseder Kalimantan Forests and Climate Partnership

TECHNICAL PAPER

December 2013

Historical Land Cover Classifications and Land Cover Change in the 2 Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 2

ACKNOWLEDGEMENTS This report was prepared for the Indonesia-Australia Forest Carbon Partnership (IAFCP) and the Kalimantan Forests and Climate Partnership (KFCP) program by researchers at RSS â&#x20AC;&#x201C; Remote Sensing Solutions GmbH. Researchers included Florian Siegert, Peter Navratil, Jonas Franke and Karin Kronseder (see below). The work was technically coordinated and reviewed by Grahame Applegate for IAFCP in 2012 and further reviewed by Nikki Fitzgerald and Rachael Diprose for IAFCP editing and publication in 2013. The IAFCP communications team (James Maiden and Nanda Aprilia) also provided assistance with layout for publication.

This research was carried out in collaboration with the Governments of Australia and Indonesia, but the analysis and findings presented in this paper represent the views of the authors and do not necessarily represent the views of those Governments. Any errors are the authorsâ&#x20AC;&#x2122; own. The paper constitutes a technical scientific working paper and as such, there is potential for future refinements to accommodate feedback and emerging evidence.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 3

ABOUT THE AUTHORS Prof. Dr. Florian Siegert Is the managing director of RSS GmbH, has a strong scientific background in ecology and has more than 15 years’ experience in remote sensing. He is an accredited expert for the European Space Agency (ESA) and the German Space Agency (DLR) and was/is Principal Investigator (PI) for ERS, ENVISAT, TerraSAR-X, ALOS and RapidEye. In addition he has extensive experience in international development cooperation and the management and execution of international projects with a special focus on Asia and Africa. Dr. Siegert is also a Professor at the University of Munich, Faculty of Biology, teaching biology and global change ecology at the GeoBio Center of the Ludwig-Maximilians-University and has successfully supervised and trained more than 15 PhD candidates and Master’s course students. His major scientific interest lies in studying ecology and natural resource functions of tropical rain forests and their role in global climate change. Dipl. Geogr. Peter Navratil Has broad expertise in remote sensing and environmental monitoring with a special focus on forestry, carbon accounting and climate change mitigation. He has worked as a consultant for a wide variety of development cooperation and nature conservation projects in Central and Southeast Asia since 2005. Mr. Navratil has a well-founded knowledge of digital image processing of optical and SAR satellite data, especially in automation of data processing, object-based classification and validation. He has extensive experience in conducting aerial and field surveys and capacity building measures. At RSS he is responsible for the project management and technical implementation of forestry and climate change mitigation projects. Dr. Jonas Franke Is a geographer with broad expertise in environmental remote sensing and GIS. His technical remote sensing-related research interests are multitemporal and multisensoral approaches, imaging and field spectroscopy as well as geo-statistics. In the past five years, he coordinated various remote sensing flight campaigns in Germany and East Africa and gave lectures at the Center for Development Research (ZEF), University of Bonn. He was/is responsible for various projects that aim at providing innovative EO-solutions developed to meet specific user requirements (such as DeCOVER 2 (German Federal Ministry for Economic Affairs and Energy (BMWi), DLR), REDD-FLAME (EU), Malareo (EU) or REDD demonstration activities of the Kalimantan Forests and Climate Partnership (KFCP). Dipl. Biol. Karin Kronseder She is specialized on the ecology of tropical forest ecosystems and biodiversity. Miss Kronseder graduated in Biology at the Ludwig-Maximilians University of Munich. Her focus lies on the application of optical and LiDAR remote sensing for forest biomass assessment in the context of REDD+ and climate change mitigation. She is well experienced in the planning and conduction of forest inventory for aboveground biomass assessments in tropical forest, and has spent more than 5 months in the peat swamp forests of Central Kalimantan for field survey in collaboration with the University of Palangkaraya. Contact details: RSS – Remote Sensing Solutions GmbH Isarstr. 3 82065 Baierbrunn/München Germany www.rssgmbh.de info@rssgmbh.de

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 4

CONTENTS ACKNOWLEDGEMENTS

ABOUT THE AUTHORS

LIST OF FIGURES

LIST OF TABLES

INTRODUCTION

PROJECT AREA

DATA AND METHODS

3.1

Remote Sensing data

3.2

Data pre-processing

3.2.1

Atmospheric correction

3.2.2

Geometric correction

3.2.3

Cloud masking

3.2.4

Cloud and gap filling

3.3

Design of the classification scheme

3.4

Development of a district wide land cover/vegetation type map

3.4.1

Multi-temporal change-based classification

3.4.2

Elimination of residual data gaps in the classification layers

3.4.3

Generation of MOFor- and UKP4-conform land cover maps

3.5

Land cover change assessment

3.6

Accuracy Assessment

3.7

Estimation of uncertainties

RESULTS

4.1

District wide land cover maps

4.2

Land cover statistics

4.2.1

Individual land cover statistics for the Kapuas district

4.2.2

Land cover statistics for the for the Kapuas district with accumulated no data areas

4.2.3

Historical land cover statistics for the KFCP site

4.3

Input to the determination of Reference GHG emission levels (REL)

4.4

District wide land cover change statistics

CONCLUSION

LIMITATIONS OF USE

BIBLIOGRAPHY

DELIVERABLES

APPENDIX 1

9.1

Cloud masking procedure applied in this study

9.2

Land cover maps

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 5

LIST OF FIGURES Figure 1 Overview of the location of the study area on the island of Borneo................................................ 11 Figure 2: Cloud (white) and shadow (red) classification (left) and resulting masked image (right) .............. 15 Figure 3: Example for the gap-filling process ................................................................................................... 16 Figure 4: Ancillary data used for the land cover classification ........................................................................ 19 Figure 5: Classification scheme of the land cover/vegetation type classification of the Kapuas district ...... 20 Figure 6: Post-classification data gap land cover modeling ............................................................................ 22 Figure 7: Class transformation scheme used to produce MOFor- and UKP4-conform land cover maps ...... 23 Figure 8: Thumbnails of the land cover maps of the Kapuas district for 1991, 1997, 2000, 2005 and 2009 . 29 Figure 9: Spatial extent of the land cover classes 1991 in thousands of hectares ......................................... 31 Figure 10: Spatial extent of the land cover classes 1997 in thousands of hectares ....................................... 32 Figure 11: Spatial extent of the land cover classes 2000 in thousands of hectares ....................................... 33 Figure 12: Spatial extent of the land cover classes 2005 in thousands of hectares ....................................... 34 Figure 13: Spatial extent of the land cover classes 2009 in thousands of hectares ....................................... 35 Figure 14: Spatial extent of the land cover classes with accumulated no data 1991 ..................................... 36 Figure 15: Spatial extent of the land cover classes with accumulated no data 1997 ..................................... 37 Figure 16: Spatial extent of the land cover classes with accumulated no data 200 ....................................... 38 Figure 17: Spatial extent of the land cover classes with accumulated no data 2005 ..................................... 39 Figure 18: Spatial extent of the land cover classes with accumulated no data 2009 ..................................... 40 Figure 19: Percent land cover per class in the KFCP site based on Landsat classifications ............................ 41 Figure 20: Land cover change in the Kapuas district for primary forest and non-forest vegetation classes . 42 Figure 21: Land cover change in the KFCP site for primary forest and non-forest vegetation classes .......... 43 Figure 22: Land cover change per class between 1991 and 1997 in hectares ................................................ 44 Figure 23: Land cover change per class between 1997 and 2000 in hectares ................................................ 45 Figure 24: Land cover change per class between 2000 and 2005 in hectares ................................................ 46 Figure 25: Land cover change per class between 2005 and 2009 in hectares ................................................ 47 Figure 26: Overall land cover change per class between 1991 and 2009 in hectares .................................... 48 Figure 27: Folder map showing the structure of the folders and files delivered. .......................................... 52 Figure 28: Workflow of the RSS cloud masking routine. ................................................................................. 53 Figure 29: 1991 Landsat based land cover classification for the Kapuas District ........................................... 55 Figure 30: 1997 Landsat based land cover classification for the Kapuas District ........................................... 56 Figure 31: 2000 Landsat based land cover classification for the Kapuas District ........................................... 57

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 6

Figure 32: 2005 Landsat based land cover classification for the Kapuas District ........................................... 58 Figure 33: 2009 Landsat based land cover classification for the Kapuas District ........................................... 59 Figure 34: 1991 Landsat based land cover classification for the GoI Kapuas Area......................................... 60 Figure 35: 1997 Landsat based land cover classification for the GoI Kapuas Area......................................... 61 Figure 36: 2000 Landsat based land cover classification for the GoI Kapuas Area......................................... 62 Figure 37: 2005 Landsat based land cover classification for the GoI Kapuas Area......................................... 63 Figure 38: 2009 Landsat based land cover classification for the GoI Kapuas Area......................................... 64 Figure 39: 1991 Landsat based land cover classification for the KFCP site ..................................................... 65 Figure 40: 1997 Landsat based land cover classification for the KFCP site ..................................................... 66 Figure 41: 2000 Landsat based land cover classification for the KFCP site ..................................................... 67 Figure 42: 2005 Landsat based land cover classification for the KFCP site ..................................................... 68 Figure 43: 2009 Landsat based land cover classification for the KFCP site ..................................................... 69 Figure 44: 1991 Landsat based MOFor-conform land cover classification for the Kapuas district ................ 70 Figure 45: 1997 Landsat based MOFor-conform land cover classification for the Kapuas district ................ 71 Figure 46: 2000 Landsat based MOFor-conform land cover classification for the Kapuas district ................ 72 Figure 47: 2005 Landsat based MOFor-conform land cover classification for the Kapuas district ................ 73 Figure 48: 2009 Landsat based MOFor-conform land cover classification for the Kapuas district ................ 74 Figure 49: 1991 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area ............. 75 Figure 50: 1997 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area ............. 76 Figure 51: 2000 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area ............. 77 Figure 52: 2005 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area ............. 78 Figure 53: 2009 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area ............. 79 Figure 54: 1991 Landsat based MOFor-conform land cover classification for the KFCP site ......................... 80 Figure 55: 1997 Landsat based MOFor-conform land cover classification for the KFCP site ......................... 81 Figure 56: 2000 Landsat based MOFor-conform land cover classification for the KFCP site ......................... 82 Figure 57: 2005 Landsat based MOFor-conform land cover classification for the KFCP site ......................... 83 Figure 58: 2009 Landsat based MOFor-conform land cover classification for the KFCP site ......................... 84 Figure 59: 1991 Landsat based UKP4-conform land cover classification for the Kapuas district .................. 85 Figure 60: 1997 Landsat based UKP4-conform land cover classification for the Kapuas district .................. 86 Figure 61: 2000 Landsat based UKP4-conform land cover classification for the Kapuas district .................. 87 Figure 62: 2005 Landsat based UKP4-conform land cover classification for the Kapuas district .................. 88 Figure 63: 2009 Landsat based UKP4-conform land cover classification for the Kapuas district .................. 89 Figure 64: 1991 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area................ 90

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 7

Figure 65: 1997 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area................ 91 Figure 66: 2000 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area................ 92 Figure 67: 2005 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area................ 93 Figure 68: 2009 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area................ 94 Figure 69: 1991 Landsat based UKP4-conform land cover classification for the KFCP site ............................ 95 Figure 70: 1997 Landsat based UKP4-conform land cover classification for the KFCP site ............................ 96 Figure 71: 2000 Landsat based UKP4-conform land cover classification for the KFCP site ............................ 97 Figure 72: 2005 Landsat based UKP4-conform land cover classification for the KFCP site ............................ 98 Figure 73: 2009 Landsat based UKP4-conform land cover classification for the KFCP site ............................ 99

LIST OF TABLES Table 1: Landsat scenes used for the district wide land cover mapping......................................................... 13 Table 2: Class description table ........................................................................................................................ 17 Table 3: Class confusion matrix per class by the use of 373 reference samples ............................................ 25 Table 4: Class confusion matrix per class in percent accuracy by the use of 373 reference samples............ 25 Table 5: User accuracy matrix per class in percent accuracy by the use of 373 reference samples .............. 26 Table 6: Producer accuracy matrix per class in percent accuracy by the use of 373 reference samples....... 26

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 8

a.s.l.

Above sea level

ESRI

Environmental Systems Research Institute

ETM+

Enhanced Thematic Mapper +

GISTDA Geo-Informatics and Space Technology Development Agency GOFCGOLD

Global Observation of Forest Cover and Land Dynamics

GOI

Government of Indonesia

IPCC

Intergovernmental Panel for Climate Change

KFCP

Kalimantan Forest and Climate Partnership

LCCS

FAO Land Cover Classification System

MoF

Ministry of Forestry (used in figures)

MOFor

Ministry of Forestry (used to distinguish from the Ministry of Finance in the main text)

NDVI

Normalized Difference Vegetation Index

REL

Reference Emission Level

RSS

Remote Sensing Solutions GmbH

SLC

Scan Line Corrector

SPOT

Système pour l'Observation de la Tèrre

SRTM

Shuttle Radar Topography Mission

Thematic Mapper

UKP4

Unit Kerja Presiden Pengawasan dan Pengendalian Pembangunan (President’s Unit for Development Control and Monitoring)

USGS

United States Geological Survey

WRS

Worldwide Reference System

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 9

1. INTRODUCTION The present report describes the historical land cover and land cover change assessment for the Kapuas District and the project area of the Kalimantan Forests and Climate Partnership (KFCP) in Central Kalimantan, Indonesia. RSS - Remote Sensing Solutions GmbH conducted this multi-temporal land cover change assessment based on the evaluation of 29 Landsat satellite images for the years 1991, 1997, 2000, 2005 and 2009. The land cover/vegetation type maps were generated with a minimum mapping unit of 2 ha and provide information on forest status and forest disturbances. The applied classification legend is compatible to the FAO Land Cover Classification System (LCCS). MOFor-conform land cover maps as well as UKP4conform land cover maps (Ketua Unit Kerja Presiden Pengawasan dan Pengendalian Pembangunan President’s Unit for Development Control and Monitoring) were also generated. The objectives of this study were to undertake the following: 

Acquisition and pre-processing of a Landsat time series covering the Kapuas district.



Historical analysis of land cover within the Kapuas district and the KFCP project area for the years 1991, 1997, 2000, 2005 and 2009 with a minimum mapping unit of 2 ha.



Accuracy assessment.



Assessment of land cover changes by a post classification based approach.

The report first describes the project area in Section 2, followed by a summary of the remote sensing data which was used for the analysis. A detailed description of the data processing methodology is given in Section 3 and all the individual processing steps are described. Finally, in Section 4 the results of the historic land cover and land cover change and the land cover change are analysed.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 10

2. PROJECT AREA The Kapuas District and KFCP project area are both located in Central Kalimantan province, Indonesia (Figure 1). The district stretches approximately 340 km, from the Java Sea in the south, along the Kapuas and Mantangai Rivers, to the M端ller mountain range in the north. The southern part of the district and KFCP site cover sections of the former Mega Rice Project. The southern Kapuas district and the KFCP site, are characterized by peatland, whereby the most southern parts are agricultural and fallow land and the northern part is covered primarily by peat swamp forests which have been logged-in the early and mid 1990s. The Kapuas district and the KFCP project area have been under severe anthropogenic pressure for the last three decades. The most severe impact was caused by the Mega Rice Project, conceptualized by the Indonesian government in 1995 to convert an uncultivated area of 988 568 ha for rice cultivation through the construction of about 6000 km of drainage and irrigation channels in peatland between 1996 and 1997. Currently, two different district boundaries exist for the Kapuas District (Figure 2). The present land cover and land cover change assessment was conducted for both district boundaries, on the one hand for the Kapuas District boundary as used by the KFCP and the district authorities, and on the other hand for the Kapuas District boundaries as used by the Government of Indonesia (GOI) Kapuas boundary. Figure 1 Overview of the location of the study area on the island of Borneo

Source: Base map: ESRI; Outlines: Provided by KFCP.

Note: The Kapuas district and the KFCP project area in Central Kalimantan, Indonesia (left). The detailed map (right) shows the different boundaries of the Kapuas district as used by the Government of Indonesia (GoI; white) and used by district authorities (yellow).

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 11

3. DATA AND METHODS 3.1 Remote Sensing data For the historical land cover assessment, the selection of sensors is limited to those in operation during the whole observation period in order to ensure data consistency. Between 1990 to date, applicable and operational satellite instruments were two satellites from the NASA Landsat mission, Landsat 5 TM and Landsat 7 ETM+ and two satellites from the French SPOT mission. A detailed data survey showed that only Landsat provides almost complete spatial and temporal coverage for the Kapuas district. The relatively long north-south orientation of the Kapuas District requires three Landsat footprints 1 for complete wall-to-wall coverage. Due to the failure of the Scan-Line Corrector (SLC) of the Landsat-7 ETM+ sensor in May 2003, and the resulting gaps in the satellite images, at least six satellite scenes are necessary for complete coverage, and to achieve minimal cloud cover and non-overlapping data gaps. It is well known that the use of optical satellite imagery in the humid tropics is often seriously limited by the very frequent and widespread cloud cover in this region. The Kapuas district experiences frequent cloud cover resultant from its location at the coast and its proximity to mountainous terrain in the north. Based on the results of an extensive search of all available Landsat images in the USGS (U.S. Geological Survey), GISTDA (Geo-Informatics and Space Technology Development Agency) and other open archives, it was decided to focus on the years 1991, 1997, 2000, 2005 and 2009 as observation periods, because the chances to get the highest possible data coverage were best in those years in regards to cloud cover and data availability. Imagery of other years did not allow for sufficient coverage of the Kapuas district. In order to optimize coverage, up to three images were selected for each scene and time step in order to fill SLC-off caused data gaps or cloud-affected areas. Table 1 lists all Landsat satellite scenes used for the five time steps of the land cover mapping on a district scale. In total 29 Landsat scenes have been processed.

Landsat satellite images are recorded and stored in a predefined worldwide grid of paths and rows, the Worldwide Reference System (WRS). One image footprint measures 180 km by 180 km. Individual images, also called scenes are identified by the satellite they were recorded by, their Path and Row number in the WRS, and the acquisition date.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 12

Table 1: Landsat scenes used for the district wide land cover mapping. Year 1991

1997

2000

2005

2009

Path/Row

Sensor (Landsat 5 or 7)

Acquisition date Master

Fill scene 1

Fill scene 2

118/060

Landsat 5 TM

30 June 1991

27 April1991

No image 2 available

118/061

Landsat 5 TM

30 June 1991

No image available

118/062

Landsat-5 TM

30 June 1991

No image available

118/060

118/061

Landsat-5 TM

29 May 1997

01 August 1997

No image available

118/062

Landsat-5 TM

29 May 1997

01 August 1997

No image available

118/060

Landsat-7 ETM+

16 July 2000

20 October 2000

No image available

118/061

Landsat-7 ETM+

16 July 2000

02 September 2000

No image available

118/062

Landsat-7 ETM+

16 July 2000

No image available

118/060

Landsat-7 ETM+

09 April 2005

03 November 2005

02 October 2005

118/061

Landsat-7 ETM+

02 October 2005

09 April 2005

31 August 2005

118/062

Landsat-7 ETM+

31 August 2005

02 October 2005

03 November 2005

118/060

Landsat-7 ETM+

22 May 2009

07 June 2009

No image available

118/061

Landsat-7 ETM+

22 May 2009

07 June 2009

No image available

118/062

Landsat-7 ETM+

22 May 2009

07 June 2009

25 July 2009

For some points in time, there are few or no satellite images available, either because there were few or none recorded and archived, or because the archived data contained too much cloud or smoke cover to be processed.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 13

3.2 Data pre-processing Prior to the image classification, a series of pre-processing procedures were conducted on the Landsat imagery listed in Table 1 in order to optimize data quality and to correct any errors in the data. The individual pre-processing steps are described in detail below.

3.2.1 Atmospheric correction The Landsat-7 satellite imagery was delivered in Level-1T format. The DNs in the Level 1T format still represent "at-sensor-radiance", which means the radiance measured on the top of the atmosphere by the sensor. In order to compensate for atmospheric distortions (scattering, illumination effects, adjacency effects), induced by water vapor and aerosols in the atmosphere, seasonally different illumination angles etc., an atmospheric correction was applied to each image using ATCOR-2 (Richter 2006). This pre-processing step means a calibration of the data into an estimation of the surface reflectance without atmospheric distortion effects. This calibration method facilitates a better scene-to-scene comparability, which is a necessary precondition for the segment-based rule-set classification method subsequently applied.

3.2.2 Geometric correction The satellite data were procured from the USGS archive in Level 1T pre-processing format. A geometric correction including terrain correction had already been applied. However, it was found that this geometric pre-processing by USGS was not adequate for the multi-temporal analysis. Therefore, the images were geometrically co-registered by an automated co-registration procedure using the ENVI 4.8 software (Research Systems Inc., Boulder, CO, USA).

3.2.3 Cloud masking In order to generate image mosaics with the highest possible data quality and coverage, a cloud and cloudshadow masking was applied using an object-based cloud classification with the eCognition software (Trimble GeoSpatial, Munich, Germany). Individual thresholds for brightness and NDVI values as well as a specially developed Cloud Index were applied. The detailed procedure is described in Appendix 1. This ruleset was used for all Landsat scenes, with slight adjustment of the thresholds where appropriate. In order to mask heavily haze contaminated areas, which could not covered by the cloud detection, a manual postclassification was applied. Figure 2 shows an example for an area strongly affected by clouds. Areas classified as cloud (white) or cloud shadow (red) are combined into a single mask band and applied to the image. The masked areas (black) contain the background value 0 in all spectral bands.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 14

Figure 2: Cloud (white) and shadow (red) classification (left) and resulting masked image (right)

Source: Remote Sensing Solutions GmbH

3.2.4 Cloud and gap filling Next, an image compositing procedure was carried out whereby gaps in the master scene, resulting from the cloud masking process and SLC failure of the Landsat 7 sensor, were filled with additional scenes. Figure 3 shows an example of the resulting image mosaic. Due to the varying location and width of the SLC-off gaps in the imagery, and the presence of clouds in the fill scenes, some residual "no data" areas remain in the gap filled image. These were later filled by a post-classification land cover modelling approach as described below.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 15

Figure 3: Example for the gap-filling process

B Fill image 1

Master

C Fill image 2

D Result

Source: Remote Sensing Solutions GmbH Note: A, B and C show the cloud-masked Landsat images, containing data gaps resulting from the SLC-failure and clouds. In the fill process, the master scene is superimposed over the two fill scenes, and all no data areas are filled with the values of the underlying fill scenes. The resulting filled image is shown in D, still containing residual gaps where no valid data was available in any of the scenes.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 16

3.3 Design of the classification scheme The classification scheme for the present land cover assessment was designed according to the Landsat data capabilities (thematic detail depends on the data characteristics) and classes were defined that are transferable to other classification systems such as the one used by the Ministry of Forestry (MOFor) and the one used by "Ketua Unit Kerja Presiden Pengawasan dan Pengendalian Pembangunan" (Presidentâ&#x20AC;&#x2122;s Unit for Development Control and Monitoring - UKP4). In total, 27 ecological classes (9 primary forest classes, 9 secondary forest classes and 9 non-forest land cover classes) were defined. These classes are based on expert knowledge on tropical forest ecosystems and the spatial and spectral capability of the Landsat sensor to discriminate vegetation types. Due to technical feasibility some vegetation types with similar phenology had to be combined into one class, since they cannot be differentiated with sufficient accuracy. Table 2 lists the 27 ecological classes and describes the vegetation/land cover types that are included. For comparison, the original MOFor classification only discriminates 14 land cover classes in the Kapuas District. Table 2: Class description table Ecological Classes

Description

Upper Montane Dipterocarp Forest (prim.)

Primary Upper Montane Dipterocarp Forest > 1500 m asl

Upper Montane Dipterocarp Forest (sec.)

Logged over or regrown Upper Montane Dipterocarp Forest > 1500 m asl

Lower Montane Dipterocarp Forest (prim.)

Primary Lower Montane Dipterocarp Forest 1500 m - 900 m asl

Lower Montane Dipterocarp Forest (sec.)

Logged over or regrown Lower Montane Dipterocarp Forest 1500 m - 900 m asl

Hill-/ Sub-Montane Dipterocarp Forest (prim.)

Primary Hill-/ Sub-Montane Dipterocarp Forest 900 m â&#x20AC;&#x201C; 300 m asl

Hill-/ Sub-Montane Dipterocarp Forest (sec.)

Logged over or regrown Hill-/ Sub-Montane Dipterocarp Forest 900 m - 300 m asl

Lowland Dipterocarp Forest (primary)

Primary Lowland Dipterocarp Forest < 300 m asl

Lowland Dipterocarp Forest (secondary)

Logged over or regrown Lowland Dipterocarp Forest < 300 m asl

Heath Forest (prim.)

Primary forest on nutrient-poor sand substrate

Heath Forest (sec.)

Logged over or regrown forest on nutrient-poor sand substrate

Peat Swamp Forest (prim.)

Primary Peat Swamp Forest

Peat Swamp Forest (sec.)

Logged over or regrown Peat Swamp Forest

Riparian Forest (primary)

Dominantly natural forest along rivers and water bodies

Riparian Forest (secondary)/ Agroforestry

Dominantly natural forest along rivers, small scale plantations, agroforestry, regrowing trees after shifting cultivation

Coastal Forest (prim.)

Primary mixed forest growing near the coast on nutrientpoor soil

Coastal Forest (sec.)

Logged over or regrown Coastal Forest

Mangrove Forest (prim.)

Primary Mangrove Forest

Mangrove Forest (sec.)

Logged over or regrown Mangrove Forest

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 17

Bush/Shrubs/ Regrowth/ Agroforestry

Evergreen broadleaved woody vegetation usually less than 15 meters in height

Bush/Shrubs/ Regrowth/ Agroforestry (Swamp)

Evergreen broadleaved woody vegetation usually less than 15 meters in height on peat or in swampy areas

Plantation

Different types of large-scale plantations (oil palm, pulp wood, tree crops)

Grassland/ Fern/ Agriculture

Mixture of Alang alang and other grasses, ferns, small bushes and small cultivated areas (shifting cultivation)

Non-Vegetated

Streets, harvested fields, recently burned area etc.

Water

Flowing and standing water bodies

Wetland

Wetlands with grasses and sedges, floating plants, Pandanus thickets

Settlement

Settlements, buildings etc.

Clouds

Clouds or cloud shadows (small cloud gaps are filled by taking all neighbouring land covers into account)

3.4 Development of a district wide land cover/vegetation type map For the Landsat image classification a segment-based classification approach based on a predefined hierarchical rule-set was applied. This methodology classifies spatially adjacent and groups of pixels with spectrally similar values, so called image objects, rather than individual pixels. Details about the object-based classification approach can be found in Blaschke (2010). The workflow of the used rule-set used in this study described in this section. 3 This approach consists of three basic procedures:   

Design of a class hierarchy: Definition of classes and inheritance rules between parent and child classes Image segmentation: The input image raster dataset is segmented into homogeneous image objects according to their spectral characteristics Image classification: The image objects are assigned to the predefined classes according to decision rules which are based on spectral, spatial, geometric, thematic or topologic criteria

Ancillary data was used for the classification, such as a digital surface model (derived from SRTM data) and historical logging railway layers as showed in Figure 4. These ancillary data allowed a more detailed class hierarchy. The historical railway layer derived from Landsat data (1991, 1997, 2000, 2005, 2009) was used to determine logged areas. Logging railways were buffered by 300 m (in mountainous areas) and 500 m (in lowland areas), because this is the distance logging impact is usually observed in the satellite imagery and in field studies. Image objects that are located within this buffer area were classified as secondary forest (logged over). The remaining forest area was classified as primary forest.

The classification rule-set described here is a proprietary RSS in-house development.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 18

Figure 4: Ancillary data used for the land cover classification

Source: Remote Sensing Solutions GmbH; SRTM data: United States Geological Survey (USGS); KFCP Outline: KFCP Note: Digital Surface Model from the KFCP site (left) derived from SRTM data (Shuttle Radar Topography Mission) used as ancillary data for classification. Logging Railways derived from historical Landsat data (right).

The Landsat image mosaics were segmented into objects of adjacent, spectrally similar pixels by the multiresolution segmentation algorithm implemented in eCognition, and subsequently classified according to the hierarchical classification scheme shown in Figure 5. Each of the nine forest types was discriminated into primary, previously logged or regrowing forest (secondary forest). The classification rule-set works in a hierarchical manner from coarse to fine thematic details. On the first hierarchy level, a discrimination between Forest areas, Non-forest areas and Cloud gaps is conducted. On the next level of the hierarchy, all forest objects are further discriminated into six ecogeographic forest types according to their spectral properties in the Landsat data. Non-forest land cover objects are further distinguished into Non-forest vegetation and non-vegetated areas. On the third hierarchy level the Dipterocarp Forest class is further distinguished according to the elevation range the objects are located in, into Lowland Diperocarp Forest (< 300 m a.s.l), Hill- and Submontane Dipterocarp Forest (300 â&#x20AC;&#x201C; 900 m a.s.l), Lower Montane Forest (900 â&#x20AC;&#x201C; 1500 m a.s.l.) and Upper Montane Diperocarp Forest (> 1500 m a.s.l.). This discrimination is based on the elevation information derived from the SRTM digital elevation model. Non-forest vegetation is further distinguished into the classes Bush/ Shrubs / Regrowth (Swamp), Bush/ Shrubs/ Regrowth, Grassland/ Fern/ Agriculture, Wetland and Plantation, based on the spectral properties of these classes in the Landsat image, as well as visual interpretation of the images. Non-Vegetation is further distinguished into Water, Non-vegetated bare land and settlements.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 19

Figure 5: Classification scheme of the land cover/vegetation type classification of the Kapuas district

Source: Remote Sensing Solutions GmbH Note: Grey boxes without frame represent parent classes, framed boxes represent the final classes.

3.4.1 Multi-temporal change-based classification To achieve a consistently accurate land cover classification, especially in regards to the class boundaries between time steps, the following multi-temporal change-based classification approach was implemented. First, the image mosaic from 1991 was classified. This layer served as blue-print for the classifications of the next time step, 1997, and so forth. Thereby, the class boundaries of the previous land cover map (e.g. 1991) were used during the segmentation of the imagery of the next time step (e.g. 1997) in order to produce consistent class boundaries over time.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 20

The degree of change between image pairs of two time steps was assessed by an image-to-image change detection approach. Therefore, a spectral difference image was calculated from the band 5 layer of the two points in time under consideration with the equation:

Where:

â&#x2C6;&#x2020;đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;5 = đ??ż_đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;5 đ?&#x2018;&#x2021;1 â&#x2C6;&#x2019; đ??ż_đ?&#x2018;?đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;5 đ?&#x2018;&#x2021;2

â&#x2C6;&#x2020;band5:

Spectral difference in Landsat Band 5

L_band5T1

Spectral reflectance in Landsat Band 5 at time T1

L_band5T2

Spectral reflectance in Landsat Band 5 at time T2

If an image object had a change value in this spectral difference image above a predefined threshold, this object was re-classified in an independent processing step, with the classification rule-set described in Section 4.4. If an image objects degree of change is below the specified threshold, the class of the previous time step was assigned. This procedure helps to avoid erroneously mapped change polygons where no real change is taking place due to the mixed pixel problem. 4 Furthermore it improves the automation of the multitemporal image classification, because implausible changes (e.g. from one forest type into another) are minized by carrying over the forest types from the previous classifications. If a higher temporal resolution time series (e.g. annual) is conducted, this approach would significantly reduce the required processing power and time because only those areas are classified which have changed in comparison to the last time step.

3.4.2 Elimination of residual data gaps in the classification layers Since the gap-filled image mosaics still have residual data gaps (as demonstrated in Figure 3d), these had to be filled in order to create spatially consistent map layers. Since there was no spectral information contained in the gap pixels, a topology-based post-classification fill approach was conducted (based on neighbouring class relationships). Figure 6 shows an example of the land cover modelling procedure for data gaps (SLC off etc.). Only small gaps (< 200 ha) were considered since the error rate would be too high for large data gaps. Data gaps that are completely enclosed by only one class were assigned to the surrounding class. All data gap areas that are surrounded by more than one class were segmented into individual raster cells (Fig. 6a and 6b). Then, these pixel objects were assigned to the class with which they share the longest common boundary (>= 50 %). This class assignment was iteratively done until all gap pixels had been assigned to one of the target classes (Fig. 6c). The advantage of this method is that gaps are more reliably modelled than by the usually applied procedure of assigning the whole data gap to one neighbouring class.

Pixels in remote sensing images can contain spectral information of more than one land cover type, in particular when the pixels are located at the boundary between to land cover types. This problem is commonly referred to as the â&#x20AC;&#x153;mixed pixel problemâ&#x20AC;?. In multi-temporal analyses, this can lead to a false detection of change because these pixels are classified into one class at one point in time, and into the other at the other point in time, albeit there is no real change taking place.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 21

Figure 6: Post-classification data gap land cover modeling

Source: Remote Sensing Solutions GmbH Notes - A: Residual cloud gaps in the image mosaic with class boundaries. B: Chessboard-segmentation of gaps to pixel level with the neighbouring classes. C: Gap-filled classification after iterative pixel by pixel assignment to neighbouring classes with longest shared border.

3.4.3 Generation of MOFor- and UKP4-conform land cover maps The base classification scheme consists of 27 classes that were additionally transformed into the MOFor and UKP4 land cover class structure by the use of a class transformation matrix (Figure 7). Since the MOFor and UKP4 classification schemes are less detailed in thematic terms than the class structure designed for the present study, this transformation actually means a loss of information due to merging of classes. For example, only 14 classes occur in the original 2006 MOFor classification in the Kapuas district area, whereas 27 ecological classes could be derived from Landsat imagery. By transforming the 27 classes into the MOFor and the UKP4 system, additional land cover maps that are conform to these systems were generated. Some classes in the MOFor system could not be discriminated on the project site due to confusion with other classes (e.g. Plantation Forest and other plantation crops). In order to include such classes into the classification scheme, ancillary data on the presence of such classes would be required.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 22

Figure 7: Class transformation scheme used to produce MOFor- and UKP4-conform land cover maps

MOFor system < Transformation < MOFor class english (MOFor class code) Primary Dryland Forest (2001)

Classes identified in Landsat imagery

> Transformation >

Ecological classes derived from Landsat imagery in Kapuas

UKP4 system UKP4/BSNI 1:250.000 classes 1.2.1.1 Hutan lahan kering primer

Upper montane Dipterocarp Forest (primary) Lower Montane Dipterocarp Forest (primary) Hill-/ Sub-Montane Dipterocarp Forest (primary) Lowland Dipterocarp Forest (primary) Heath Forest (primary) Coastal Forest (primary) Riparian Forest (primary)

Secondary Dryland Forest (2002)

1.2.1.2 Hutan lahan kering sekunder

Upper montane Dipterocarp Forest (secondary) Lower Montane Dipterocarp Forest (secondary) Hill-/ Sub-Montane Dipterocarp Forest (secondary) Lowland Dipterocarp Forest (secondary) Heath Forest (secondary) Coastal Forest (secondary) Riparian Forest (secondary)/ Agroforestry

Primary Swamp Forest (2005) Secondary Swamp Forest (20051) Primary Mangrove Forest (2004) Secondary Mangrove Forest (20041) Plantation Forest (2006) Bush/Shrub (2007) Swamp Shrub (20071) Estate Cropplantation Shrub-Mixed Dryland Farm (2014)

Peat Swamp Forest (secondary) Mangrove Forest (primary) Mangrove Forest (secondary)

≠

Rice Field (20093)

≠

Grassland (3000)

≠

Mining Area (20141) Water (5001) Swamp (50011) Cloud Covered (?) Settlement Area

cannot be discriminated in the project site from other plantation crops Bush/ Regrowth/ Agroforestry Bush/ Regrowth/ Agroforestry (Swamp) Plantation including oil palm, pulp wood, rubber, tree

≠

≠ ≠ ≠

Non-Vegetated (harvested or burned) not feasible in Kapuas district due to small scale patterns (included in Grassland/ Fern/ Agriculture) not feasible in Kapuas district due to small scale patterns (included in Grassland/ Fern/ Agriculture) does not exist in original MOFor map in Kapuas included in Grassland/ Fern/ Agriculture not a land cover (not applicable) = water not a land cover (not applicable) and does not exist in original MOFor map in Kapuas not a land cover (not applicable) Water Wetlands Clouds (small cloud gaps are filled) Settlement

1.1.4 Perkebunan 1.2.4 Semak dan belukar 1.1.4 Perkebunan 1.2.5 Padang rumput, alang-alang, sabana 2.1 Lahan terbuka

Grassland/ Fern/ Agriculture

Dryland Agriculture (20091 & 20092)

Fish Pond (20094) Transmigration Area (20122)

1.2.2.1 Hutan lahan basah primer 1.2.2.2 Hutan lahan basah sekunder 1.2.2.1 Hutan lahan basah primer 1.2.2.2 Hutan lahan basah sekunder

Peat Swamp Forest (primary)

≠

1.1 Daerah pertanian 1.1.1 Sawah/ 1.1.3 Sawah pasung surut 1.2.5 Padang rumput, alang-alang, sabana

≠

2.3.2 Tambak

≠

≠ ≠ 2.3 Perairan 1.2.6 Rumput rawa 2.2.1.1 Permukiman

forest classes non forest vegetation classes

non vegetated

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 23

3.5 Land cover change assessment To assess changes in land cover during the observation period between 1991 and 2009, a post classification change detection method was applied. The land cover maps from 5 observation dates were overlaid and the change vectors 5 (from-to-changes from one class into another) between two layer pairs were determined. The creation of the change layer was done in ArcGIS. Due to the high number of classes (27 + No Data), there are 784 possible change vectors (28 x 28). 6 Once two land cover tables were combined, implausible land cover changes (e.g. Lowland Dipterocarp Forest to Hill-/ Sub-Montane Dipterocarp Forest) were corrected. Even though the multitemporal change based classification method applied in this study greatly reduced the amount of post-classification corrections, there still were erroneously mapped implausible changes due to minimum mapping unit generalization or post classification gap filling procedure. These misclassified image objects were assigned to the same class as in the previous observation period. This step is required to achieve consistent change statistics.

3.6 Accuracy Assessment An independent accuracy assessment and verification of the classification results with reference data is an essential component of REL. The accuracy analysis must provide an accuracy matrix considering user and producer accuracies, the overall accuracy and the kappa index. For historical reference periods, an accuracy assessment is often challenging, due to a lack of reference data. According to the GOFC-GOLD Sourcebook (GOFC-GOLD 2011), a reinterpretation of samples of the original data in an independent manner is feasible in such cases. Such consistency assessment was conducted in the present study. Since there is no historical ground truth data on land cover available, the assessment was realized for the most recent 2009 time step. Since a change-based classification approach was applied, the classification errors and the resulting accuracy of the maps are dependent on each other. Therefore, the accuracy of the 2009 classification is representative for all other time steps (Congalton and Green 1999). A random sample of 373 image objects across the land cover classes was selected using ArcGIS, which were afterwards interpreted by an independent remote sensing expert not involved in the classification. Random sampling reduces the risk of bias and allows for an objective assessment of the uncertainty of the estimates. Sub-classes that were discriminated by the use of thematic layers (e.g. primary and secondary forest classes differentiated with historical logging railway layers) were combined per forest type, since the image interpreter should not use ancillary data. Several statistical measures for the accuracy (overall accuracy, Kappa coefficientâ&#x20AC;&#x2122;s of agreement, producerâ&#x20AC;&#x2122;s and userâ&#x20AC;&#x2122;s accuracy per class were calculated. Tables 4-7 show the detailed results of the accuracy assessment for the 2009 land cover map. An overall accuracy of 85.0 percent with a mean class accuracy of 88.1 percent and a Kappa coefficient of 0.83 was achieved.

Change vector means the transition of one land cover type into another.

The amount of theoretically possible change vectors is strictly dependent on the classes. Without any additional rules, each class can change into any other class. In this study, 27 land cover classes and one No Data class are used, which means 28 classes in total. Therefore the total amount of possible change vectors is 28x28 = 784.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 24

Table 3: Class confusion matrix per class by the use of 373 reference samples Class Confusion Matrix Validation class Classification class

Bush Regro.

Coast. Forest

Agrof. Bush/Regro./Agrof.

Dipterocarp Forest

Grassl/ Fern/ Agric.

Heath Forest

Man- Nongrove VegeFor. tated

Plantation

Peat Swamp For.

Riparian Forest

SettleWetWater ment lands

Sum

Coastal Forest

Dipterocarp Forest

Grassl./Fern/Agri.

Heath Forest

Mangrove Forest

Non-Vegetated

Plantation

Peat Swamp Forest

Riparian Forest

Settlement

Water

Wetlands

373

Number of samples

Table 4: Class confusion matrix per class in percent accuracy by the use of 373 reference samples Class Confusion Matrix (%) Validation class Bush Regro Classification class Bush/Regro./Agrof.

Coast. Forest

Agrof.

Dipter- Grassl/ Heath Manocarp Fern/ Forest grove Forest Agric. For.

NonVegetated

Plantation

Peat Ripa- Settle- Water WetSwamp rian ment lands For. Forest

59.2

0.0

6.3

0.0

5.0

0.0

4.3

0.0

Coastal Forest

4.1

100.0

0.0

2.1

0.0

Dipterocarp Forest

2.0

0.0

100.0

0.0

3.0

0.0

2.5

8.6

0.0

12.2

0.0

79.2

0.0

11.1

0.0

Heath Forest

0.0

93.9

0.0

5.0

5.7

0.0

Mangrove Forest

4.1

0.0

4.2

0.0

100.0

0.0

11.8

Non-Vegetated

4.1

0.0

6.3

0.0

77.8

0.0

Plantation

6.1

0.0

5.6

100.0

0.0

Peat Swamp Forest

4.1

0.0

5.6

0.0

77.5

2.9

0.0

Riparian Forest

2.0

0.0

3.0

0.0

10.0

82.9

0.0

4.3

0.0

Settlement

0.0

2.1

0.0

100.0

4.3

0.0

Water

0.0

87.0

0.0

Wetlands

2.0

0.0

88.2

100.0

Grassl./Fern/Agriculture

Number of samples

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 25

Table 5: User accuracy matrix per class in percent accuracy by the use of 373 reference samples User's Accuracy (%) Validation class Bush Regro.

Coast. DipterForest ocarp Forest

Grassl/ Fern/ Agric.

Heath ManForest grove For.

NonVegetated

Plan- Peat tation Swamp For.

Riparian Forest

Settle- Water Wetment lands

Sum

Classification

Agrof.

Bush/Regro./Agrof.

82.982,9

0.0

8.6

0.0

5.7

0.0

2.9

0.0 100.0

18.2

72.7

0.0

9.1

0.0

0.0 100.0

1.6

0.0

90.2

0.0

1.6

0.0

1.6

4.9

0.0

0.0 100.0

13.0

0.0

82.6

0.0

4.3

0.0

0.0 100.0

Heath Forest

0.0

88.6

0.0

5.7

0.0

0.0 100.0

Mangrove Forest

9.1

0.0

9.1

0.0

72.7

0.0

9.1 100.0

Non-Vegetated

10.5

0.0

15.8

0.0

73.7

0.0

0.0 100.0

Plantation

16.7

0.0

5.6

77.8

0.0

0.0 100.0

Peat Swamp Forest

5.7

0.0

2.9

0.0

88.6

2.9

0.0

0.0 100.0

Riparian Forest

2.8

0.0

2.8

0.0

11.1

80.6

0.0

2.8

0.0 100.0

Settlement

0.0

5.3

0.0

89.5

5.3

0.0 100.0

Water

0.0

100.0

0.0 100.0

Wetlands

6.3

0.0

93.8 100.0

166.7

72.7

90.2

130.4

93.0

72.7

86.4

77.8

112.8

94.0

89.5

Coastal Forest Dipterocarp forest Grassl./Fern/Agriculture

Sum

110.9 102.8

Table 6: Producer accuracy matrix per class in percent accuracy by the use of 373 reference samples Producer's Accuracy (%) Validation class Bush Regro. Classification Bush/Regro./Agrof.

Agrof.

Coast. Dipter- Grassl/ Heath ManForest ocarp Fern/ Forest grove Forest Agric. For.

NonVegetated

Plantation

Peat Ripa- Settle- Water WetSwamp rian ment lands For. Forest

Sum

59.2

0.0

6.3

0.0

5.0

0.0

4.3

0.0

74.8

Coastal Forest

4.1

100.0

0.0

2.1

0.0

106.2

Dipterocarp forest

2.0

0.0

100.0

0.0

3.0

0.0

2.5

8.6

0.0

116.1

12.2

0.0

79.2

0.0

11.1

0.0

102.5

Heath Forest

0.0

93.9

0.0

5.0

5.7

0.0

104.7

Mangrove Forest

4.1

0.0

4.2

0.0

100.0

0.0

11.8

120.0

Non-Vegetated

4.1

0.0

6.3

0.0

77.8

0.0

88.1

Plantation

6.1

0.0

5.6

100.0

0.0

111.7

Peat Swamp Forest

4.1

0.0

5.6

0.0

77.5

2.9

0.0

90.0

Riparian Forest

2.0

0.0

3.0

0.0

10.0

82.9

0.0

4.3

0.0

102.3

Settlement

0.0

2.1

0.0

100.0

4.3

0.0

106.4

Water

0.0

87.0

0.0

87.0

Wetlands

2.0

0.0

88.2

90.3

100.0

Grassl./Fern/Agriculture

Sum

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 26

3.7 Estimation of uncertainties Different components of land cover mapping approaches affect the quality of the outcomes (GOFC-GOLD 2011). These components, which are listed below, were carefully considered in the present land cover approach in order to follow best practices for land cover mapping. The components relevant for the quality of the results are: 1.

The quality and suitability of the satellite data in terms of spatial, spectral, and temporal resolution

In the present study, multispectral Landsat data with 30 m spatial resolution and a revisit time of 16 days was used for the historical land cover assessment in the Kapuas district. Landsat is the only sensor system that regularly provides almost complete coverage for the Kapuas district since the 1980s. In many previous studies during the last decades, Landsat was proven to be very suitable for vegetation analyses and land cover mapping of large areas, also due to its wide field of view. Particularly the shortwave-infrared band (SWIR) is very suitable for differentiating different forest types. Temporal inconsistencies of the imagery caused by seasonal variations can slightly affect the mapping results, since a total coverage of the district required the use of composites made from images which were acquired on multiple dates. 7 2.

The interoperability of different sensors or sensor generations

The major benefit of Landsat is that it provides data continuity since the 1980s. Therefore, it was possible to use a single sensor type in the present approach instead of a multi-sensor mosaic that would cause uncertainties. Even though Landsat 5 TM data was supplemented by Landsat 7 ETM+, the almost identical construction of the TM and ETM+ sensors ensured the acquisition of highly consistent time series. 3.

The radiometric and geometric pre-processing (i.e. correct geo-location)

By the radiometric and geometric corrections applied in the present approach (as described above), a consistent data base for the historical land cover mapping was generated. A standardized pre-processing chain was applied to all images including an atmospheric correction and image-to-image registration with an RMS error below one pixel. 4.

The cartographic and thematic standards (i.e. land category definitions and MMU)

Land category definitions (classes) followed the technical feasibility of the Landsat system. In total 27 land cover classes could be differentiated (assisted by the use of ancillary data) with low uncertainties, which is verified by the high mean class accuracy of 88.1 %, demonstrated in the accuracy assessment. In addition, the 27 land cover classes were defined in a way that they are transferable/compatible to other classification systems such as those from MOFor and UKP4. Additional land cover maps were generated from the 27 land cover classification that conform to MOFor and UKP4. Since some land cover classes were merged, in order to match the less detailed land cover scheme of MOFor and UKP4 a further reduction of uncertainties was obtained. Even though this aggregation into fewer land cover classes reduces the uncertainty of the land cover classification, it will increase the uncertainty of a GHG emission assessment based on these land cover maps because it will introduce a higher uncertainty due to variability in the Emission Factors to be used. A minimum mapping unit (MMU) of 2 ha was applied by removing image objects smaller than this size and assigning the land cover class which shares the longest boundary with the removed object, which is in

In order to get a complete coverage of the study areas and fill image gaps due to the SLC Error and Cloud cover, images from multiple dates need to be used for each time step in this analysis. Even though the aim was to combine images with small differences in acquisition time in order to minimize seasonal differences in the spectral response of land cover, it is not always possible to fulfil this criterium in scene selection.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 27

accordance to the IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (2003) and GOFC-GOLD standards. 5.

The interpretation procedure (i.e. classification algorithm or visual interpretation)

An object-based classification algorithm using a rule set approach was applied in this study, which is state-ofthe-art image analysis technology (described in Section 4.4) very suitable for land cover mapping. In order to ensure most consistent multitemporal mapping results, an innovative change-based methodology was used, whereby each classification was assisted by the use of the final land cover map of the previous time step. This procedure produced consistent class boundaries over time and thus reduced uncertainties in the land cover statistics. 6.

The post-processing of the map products

A cloud and gap filling procedure was applied prior to the classification of the data in order to minimize "no data" areas and to increase the mappable area. Nevertheless, "no data" areas remained due to persistent cloud cover in the imagery and remaining SLC-off gaps. Remaining large compact "no data" areas (>200 ha) were kept since a post-classification filling procedure would cause high uncertainties. Smaller "no data" areas were filled by an iterative land cover modelling approach that resulted in realistic land cover estimates in data gaps. Despite this proper treatment of areas with no data, any post-classification filling procedure causes local mapping uncertainties. 7.

The availability of reference data (e.g. ground truth data) for evaluation and calibration of the system

As mentioned in the accuracy assessment chapter, no suitable historical ground truth data is available for the Kapuas district. A limited quantity of available data was used for adjusting the classification process in order to improve mapping results. Due to the lack of historical ground truth data, a random sample of image objects of the most recent time step was interpreted by an image analysis expert with regional experience and used for accuracy assessment. The accuracy assessment was conducted in the most transparent manner and followed best practices. The experts of RSS have many years of first hands experience through repeated field visits in Kalimantan, and in particular in the province of Central Kalimantan, where the study area is located.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 28

4. RESULTS 4.1 District wide land cover maps Figure 8 shows thumbnails of the Kapuas-wide land cover maps for the years 1991, 1997, 2000, 2005 and 2009. Detailed land cover maps are shown in the annex, together with the land cover maps for the GOI Kapuas district, the KFCP site, all MOFor-conform land cover maps, as well as all UKP4-conform maps with complete legends. In total 45 land cover maps were generated. Figure 8: Thumbnails of the land cover maps of the Kapuas district for 1991, 1997, 2000, 2005 and 2009

1991

1997

2000 Legend

2005

2009

Source: Remote Sensing Solutions GmbH Note: Detailed land cover maps with legend can be found in the annex. All other land cover maps such as the maps for the GoI Kapuas boundary, the KFCP site, all MOFor-conform maps as well as all UKP4-conform maps are also shown in the annex. Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 29

4.2 Land cover statistics The land cover area statistics were calculated for each observation time and for both existing Kapuas district boundaries. This was accomplished in two different ways for the sake of completeness. First, the statistics were generated for each classification on an individual basis with varying amounts of the mappable area due to differences in remaining cloud cover and data gaps (after filling and modelling of small cloud gaps) at each observation. These classifications show the land cover statistics for each time step including remaining data gaps. The change result is not directly comparable over the investigation period. Therefore, to account for the varying amounts of mappable area in each classification and to generate a comparable land cover area statistics, a second calculation was performed on each dataset which considered the total mappable area for all five observation times (area with no cloud cover at all times). A cumulative cloud and data gap layer was created 8 and intersected with the land cover layer for each observation. This resulted in five land cover classifications with identical total mapped area. The size of each district boundary and the amount of cumulative remaining data gaps is listed below:  

GoI Kapuas district: 14 875 km2 with total data gaps of 2135 km2 (14 % No Data area) Kapuas district: 16 176 km2 with total data gaps of 2646 km2 (16 % No Data area)

Figure 9 to Figure 13 show the Kapuas land cover statistics calculated for the individual land cover maps and Figure 14 to Figure 18 show the land cover statistics of the total mappable area. It is important to note that several classes are severely or completely obscured by cloud cover or data gaps (particularly due to severe cloud cover in 1997), mainly those classes located in the mountainous area in the North Kapuas. These classes include Hill-/ Sub-Montane Dipterocarp Forest, Secondary Hill-/ Sub-Montane Dipterocarp Forest, and Lower Montane Dipterocarp Forest. Figure 18 shows the land cover statistics for the KFCP site.

In order to obtain change statistics which are comparable between all different change periods, all areas which are “No Data” in at least one time step need to be excluded from the analysis. Otherwise, the reference area of the change statistics differs from time period to time period, and the results (e.g. deforestation rates) will not be comparable.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 30

Figure 9: Spatial extent of the land cover classes 1991 in thousands of hectares

50 0

GoI Kapuas District Area Page 31

Source: Remote Sensing Solutions GmbH

Kapuas District

185.40 174.34 8.88 8.36 23.88 22.07 1.39 1.55 7.13 22.73 0.67 0.64

100

0.00

150

148.55 116.17

200

80.74 89.89 21.18 11.05 4.17 2.27 1.35 0.27 0.00 0.00 0.00 0.00 0.00 0.00 67.65 79.97 2.58 3.06 0.44 0.37 0.24 0.13 123.50 137.22 11.05 10.76

250

81.68 88.60

300

1.14 1.08 0.03 0.03

350

211.81 272.32 141.67 161.24

400

Spatial extent of the land cover classes 1991

362.38 413.51

450

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

4.2.1 Individual land cover statistics for the Kapuas district

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

0.79 0.78

6.74 5.37 23.23 21.41 1.12 1.29

2.76 2.76

1.07 1.01 0.03 0.03 86.93 91.50

203.50 250.80

166.70 149.00

204.00 164.80

192.10 154.50 185.70

200

133.70

107.20 116.40

65.25 76.79

150

13.41 15.91

4.14 5.32 0.43 0.36 0.40 0.17

100 80.31 85.01

250

231.20 251.00

300

0.14 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 10: Spatial extent of the land cover classes 1997 in thousands of hectares

Spatial extent of the land cover classes 1997

Page 32

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

200

150

100

GoI Kapuas District Area

Source: Remote Sensing Solutions GmbH Kapuas District

2.32 2.21 22.68 20.73 1.13 1.30 19.42 52.27 0.82 0.81

4.60 4.80

250

240.10 216.30

250.70 209.70

21.62 10.40 7.41 3.93 1.78 0.39 0.08 0.07 0.00 0.00 0.00 0.00 61.33 72.55 6.52 7.69 0.05 0.01 0.28 0.12 88.71 96.35 15.24 18.29 80.47 138.20 112.70 149.10 1.02 0.97 0.03 0.03 135.10 154.40

300

126.40 137.30

350 287.10 319.60

Figure 11: Spatial extent of the land cover classes 2000 in thousands of hectares

Spatial extent of the land cover classes 2000

Page 33

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

100

GoI Kapuas District Area

Page 34

Source: Remote Sensing Solutions GmbH Kapuas District

13.99 11.18 24.08 22.42 1.08 1.21 5.86 28.57 0.78 0.77

Spatial extent of the land cover classes 2005 283.50 256.20

216.50 179.90

130.50 147.60

125.80 122.60 165.60

150

4.58 4.89

200

0.87 0.82 0.01 0.01

250

21.77 10.50 8.69 4.56 1.78 0.38 0.09 0.09 0.00 0.00 0.00 0.00 49.82 60.46 15.13 16.62 0.07 0.01 0.20 0.12 73.73 80.68 22.02 25.22 72.21

300

220.20 253.80 197.50 220.10

Figure 12: Spatial extent of the land cover classes 2005 in thousands of hectares

250

200

150

100

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

6.07 5.94 23.29 21.18 1.08 1.17 46.47 50.50 0.78 0.76

350

294.50 253.80

48.77 59.44 16.84 18.47 0.06 0.01 0.19 0.10 72.19 79.26 24.04 27.51 69.95 121.80 123.70 172.10 0.92 0.87 0.01 0.01 128.10 141.50 200.80 166.40 27.98 32.77

183.70 220.90 208.70 236.00

300

6.21 5.19 3.15 1.99 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 13: Spatial extent of the land cover classes 2009 in thousands of hectares

Spatial extent of the land cover classes 2009

Page 35

Figure 14: Spatial extent of the land cover classes with accumulated no data 1991

Spatial extent of the land cover classes with accumulated no data 1991

Page 36

Source: Remote Sensing Solutions GmbH

Kapuas District

0.67 0.56

210.70 260.20 7.95 7.65 22.67 20.94 1.39 1.55

0.00 0.00

1.14 1.08 0.03 0.03

60.54 61.44

GoI Kapuas District Area

10.45 9.96

2.58 3.06 0.43 0.37 0.24 0.13

2.81 0.06 0.29 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00

53.89 57.07

100

67.62 79.93

150

148.34 115.97

121.74 134.79

200

141.67 161.24

250

167.04 152.62

211.81

271.10

259.44 277.82

300

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

4.2.2 Land cover statistics for the for the Kapuas district with accumulated no data areas

Page 37

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

0.78 0.77

6.74 5.37 22.72 20.90 1.12 1.29

2.76 2.76

1.06 1.00 0.01 0.01 86.69 91.50

210.70

165.07 149.00

203.19 164.80

190.87 154.50 185.70

200

133.70

106.74 115.92

65.21 76.75

150

13.22 15.71

4.14 5.32 0.42 0.36 0.40 0.17

100 78.97 83.38

250 260.20

229.35 248.55

300

0.07 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 15: Spatial extent of the land cover classes with accumulated no data 1997

Spatial extent of the land cover classes with accumulated no data 1997

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

0.80 0.78

2.20 2.17 22.10 20.17 1.13 1.30 210.70 260.20

234.94 211.82

249.96 208.96

300

4.60 3.48

94.69 102.45

136.97 112.70 149.10

80.47

88.49 95.94

61.33 72.55

200

1.00 0.96 0.01 0.01

14.97 17.96

6.52 7.69 0.05 0.01 0.28 0.12

100

0.06 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

150

92.20 97.90

250 208.43 226.86

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 16: Spatial extent of the land cover classes with accumulated no data 200

Spatial extent of the land cover classes with accumulated no data 2000

Page 38

100

Page 39

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

0.75 0.74

300

210.70

260.20

272.01 246.19

216.16 179.56

Spatial extent of the land cover classes with accumulated no data 2005

13.51 10.81 22.56 20.60 1.08 1.21

91.83 98.61

124.57 122.60 165.60

250

4.58 3.41

0.86 0.81 0.00 0.00

49.82 60.46 15.13 16.62 0.06 0.01 0.20 0.12 73.43 80.20 21.71 24.84 72.21

150 149.89 164.19 148.44 158.64

200

0.06 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 17: Spatial extent of the land cover classes with accumulated no data 2005

200

150

100

Source: Remote Sensing Solutions GmbH GoI Kapuas District Area Kapuas District

0.74 0.73

300

6.05 5.92 22.77 20.67 1.08 1.17

350

210.70 260.20

288.23 248.79

48.77 59.44 16.84 18.47 0.06 0.01 0.19 0.10 72.02 78.88 23.73 27.12 69.95 121.80 123.70 172.10 0.91 0.86 0.00 0.00 87.34 91.80 200.50 166.10 26.42 29.81

128.76 143.24 158.74 170.37

250

0.06 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Area (ha in thousands)

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Figure 18: Spatial extent of the land cover classes with accumulated no data 2009

Spatial extent of the land cover classes with accumulated no data 2009

Page 40

4.2.3 Historical land cover statistics for the KFCP site Figure 19: Percent land cover per class in the KFCP site based on Landsat classifications

1991

2000

1997

2005

2009

Source: Remote Sensing Solutions GmbH Note: Not all land cover classes that exist in the Kapuas district are present in the KFCP site.

Historical Land Cover Classifications and Land Cover Change in the 41 Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 41

4.3 Input to the determination of Reference GHG emission levels (REL) The estimate of decreases or increases of carbon emissions and sequestration from deforestation, afforestation and changes in remaining forest areas requires assessing reference emission levels (REL) against which future emissions and sequestration can be compared. The reference emission level is commonly based on historical land cover change data and Emission Factors for the land cover classes. Estimates of land cover carbon stocks can be obtained from a variety of sources. The results of the change analysis presented in this study can be used as an input to determine the REL for the Kapuas district. Figure 20 and Figure 21 show land cover trends between 1991 and 2009 for the main primary forest classes and main non-forest classes for the Kapuas district and the KFCP site respectively. The figures demonstrate decreasing decrease of primary forests and an increase of non-forest vegetation in the Kapuas district and the KFCP site. When attributed with carbon stock data, the trends can be translated into carbon emissions and sequestration which can be directly used to project RELs.

Figure 20: Land cover change in the Kapuas district for primary forest and non-forest vegetation classes

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 42

Figure 21: Land cover change in the KFCP site for primary forest and non-forest vegetation classes

Source: Remote Sensing Solutions GmbH Note: Only some of the land cover classes of the Kapuas district exist in the KFCP site.

4.4 District wide land cover change statistics The change statistics for the individual observation periods 1991–1997, 1997–2000, 2000–2005 and 2005– 2009, as well as the overall change between 1991 and 2009 were calculated based on the land cover areas of the total mappable area as described in Section 3.5. The change statistics were calculated for both district boundary areas.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 43

Figure 22: Land cover change per class between 1991 and 1997 in hectares

Land cover class change 1991â&#x20AC;&#x201C;1997 Kapuas District

GoI Kapuas District Area

-262.6 -262.7 -36.8 -2278.5 -1203.1 -3618.5 -1964.9

206.9 117.9 0.0 0.0 47.3

2764.0 2764.0 30057.3 26148.8

-21.2 -21.2 -83.3 -81.0 -80224.9 -78114.4

48828.1 54842.1

24460.1 12825.8 5743.5 2767.0

-18872.9 -15002.8 -10.1 -10.1 -3181.3 -2409.3

-104.7 -2.6 -293.4 -2743.1 -29270.6 -30083.9

35.9 165.6 2260.4 1557.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 26310.1 25083.4

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 44

Figure 23: Land cover change per class between 1997 and 2000 in hectares

Land cover class change 1997â&#x20AC;&#x201C;2000 Kapuas District

GoI Kapuas District Area 13.6 13.6 0.0 0.0 11.1 3.9 -731.7 -618.1 -3199.1 -4543.1 62823.5 69868.2 716.2 1833.1 44161.8 46775.2 10949.2 8004.4 0.0 0.0 -39.7 -52.8

-36600.0 -41800.0 -53900.0 -53230.0 2257.5 1752.3 -19978.3 -18245.5 -42.9 -126.3 -353.4 -378.1 2370.2 2373.5 -4195.6 -3875.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -13.3 -10.3 14520.8 13228.2 -21684.1 -20920.9

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 45

Figure 24: Land cover change per class between 2000 and 2005 in hectares

Land cover class change 2000â&#x20AC;&#x201C;2005 Kapuas District

GoI Kapuas District Area -44.3 -44.3 0.0 0.0 -89.7 -46.6 433.0 453.1 8636.7 11305.7 34365.5 37070.1 -74.8 -18.3 -29400.1 -33802.4 -3834.8 -2862.5 -7.8 -7.8 -148.2 -148.7 16500.0 9900.0 -12400.0 -8260.0 6874.6 6740.0 -15739.3 -15066.1 -2.2 -71.4 0.0 16.3 8931.4 8614.0 -12090.0 -11510.0

-1.3

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.7 60742.1 56245.1

-62671.4 -58538.9

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 46

Figure 25: Land cover change per class between 2005 and 2009 in hectares

Land cover class change 2005â&#x20AC;&#x201C;2009 Kapuas District

GoI Kapuas District Area -10.6 -11.0 0.0 0.0 -41.4

2.6 67.7 215.7

-4890.8 -7460.8 2599.0

16225.3 26407.4 21840.9

-13465.4 -15658.7 -6815.3 -4490.3 0.0 0.0 47.8 49.9 1100.0

6500.0

-2771.9 -2260.0 2281.6 2023.2 -1325.0 -1407.4 -16.7 -16.6 3.4 1.4 1850.0 1710.0 -1020.0 -1050.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 11729.2 10299.7 -20952.5 -21139.5

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 47

Figure 26: Overall land cover change per class between 1991 and 2009 in hectares

Overall Land cover class change 1991â&#x20AC;&#x201C;2009 Kapuas District

GoI Kapuas District Area 165.6 76.2 0.0 0.0 -382.7 -302.8 -267.8

98.1

-1731.8 -1901.3 96169.5 121198.7 29812.7 26419.6 50124.3 52156.2 30356.4 26800.5 -29.1 -29.1 -223.5 -232.6 -17974.2

10860.1

-149296.7 -141864.4 17157.2 13282.4 -55915.5 -49721.8 -25.9 -48.7 -360.1 -370.4 15412.0 14255.4 -20486.9 -18845.1

-104.7 -2.6 -293.4 -8.2 -2754.6

0.0 0.0 0.0 0.0 0.0 0.0 0.0

113302.2 104856.4 -134578.5 -130683.2

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 48

5. CONCLUSION The objective of this study was to conduct a historic land cover change analysis for the Kapuas District in the Central Kalimantan province, Indonesia. Landsat satellite images from the years 1991, 1997, 2000, 2005 and 2009 were acquired in order to fulfil this task. The images underwent a standardized pre-processing workflow which removed geometric distortions as well as the influence of atmospheric conditions from the images. Areas affected by cloud cover were removed from the Landsat images by an object-based routine which uses a spectral normalized-difference index of the blue and thermal wavelengths in order to detect cloud cover. A multi-image composite was created for each point in time in order to facilitate a complete coverage of the study area and minimize cloud and SLC-error related no data areas. The image composites have been classified by a proprietary hierarchical classification rule-set, developed at RSS, within the eCognition processing software. The land cover classification scheme distinguished 27 different land cover types based on their spectral properties, the elevation, and visual interpretation. In order to minimize residual no-data areas, a topological neighbourhood approach was applied to fill gaps based on the adjacent land cover. In order to create spatially consistent datasets for the multi-temporal change analysis, a multi-temporal classification approach was applied using the map from the first point in time as a benchmark. This procedure greatly reduced the required post-processing effort in the change detection. A validation of the 2009 land cover map was carried out by using a stratified random sample of reference locations, which were interpreted by a skilled independent interpreter. A confusion matrix was calculated comparing the classification result and the reference dataset, and the overall classification accuracy as well as the producer’s and user’s accuracy for each class was calculated. The overall accuracy with 27 land cover classes was determined at 85 percent with a mean class accuracy of 88.1 percent. The accuracy of the 2009 map is representative also for the previous points in time because the classifications errors are dependent due to the chosen multi-temporal classification approach. The change detection was carried out by a post-classification technique, overlaying the maps of two points in time for each change period 1991–1997, 1997–2000, 2000–2005, 2005–2009 and the overall investigation period 1991–2009. In order to generate change statistics that are comparable between the different change periods, a cumulative no data mask had to be used. Land cover change statistics were calculated for each change period, as well as the overall investigation period. The land cover and land cover change maps created in this study can act as a valuable input for the establishment of Reference GHG Emission Levels from the Agriculture, Forestry and Land-Use (AFOLU) sector. The land cover classes need to be attributed with Carbon values, which can be gathered from a variety of sources, and then the emission factors for the individual land cover transitions can be calculated. This allows for a spatially explicit assessment of carbon emissions from land cover change.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 49

6. LIMITATIONS OF USE The land cover classification was derived from multi-temporal Landsat imagery captured between 1991 and 2009. The classification represents an assessment of land cover, created by independent mapping specialists. Landscape heterogeneity will influence the uniformity of local mapping accuracy, despite quality control and application of minimum mapping standards. The use of vector line class boundary delineation for land cover classes typically characterized by natural gradients (e.g. vegetation transitions) as well as the modeling of land cover in cloud gaps will cause local mapping errors. Due to the landscape mapping process used to generate the land cover data, the final dataset should be seen as an indication of the likely occurrence of a dominant land cover class in a given area as opposed to a definitive delineation of each and every land cover boundary.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 50

7. BIBLIOGRAPHY Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS J. Photgramm. Rem. Sens. 65(1):2â&#x20AC;&#x201C; 16. Congalton, R.G., and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL, USA. GOFC-GOLD. 2011. A sourcebook of methods and procedures for monitoring and reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forests remaining forests, and forestation. GOFC-GOLD Report version COP17-1. GOFC-GOLD Project Office, Natural Resources Canada. Alberta, Canada. IPCC. 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry. Kanagawa, Japan. Martinuzzi, S., Gould, W.A., and , O.M. Ramos Gonzalez. 2006. Creating cloud-free Landsat ETM+ data sets in tropical landscapes: cloud and cloud shadow removal. Gen. Tech. Rep. IITF-32. U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. Rio Piedras, PR. Richter, R. 2006. Atmospheric / Topographic Correction for Satellite Imagery. Wessling, Germany.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 51

8. DELIVERABLES •

• • •

15 Landsat-based land cover/vegetation type maps for the years 1990, 1997, 2000, 2005 and 2009 also with MOFor-conform and UKP4-conform classes (Shapefile) for the Kapuas district, the GoI district and the KFCP site. The shapefiles contain all data and the following layerfiles can be used to visualize the data in the different classification systems. 15 Landsat-based land cover/vegetation type maps for the years 1990, 1997, 2000, 2005 and 2009 (Layerfile) for each area 15 Landsat-based land cover maps with MOFor-conform classes for the years 1990, 1997, 2000, 2005 and 2009 (Layerfile) for each area 15 Landsat-based land cover maps with UKP4-conform classes for the years 1990, 1997, 2000, 2005 and 2009 (Layerfile) for each area

Figure 27: Folder map showing the structure of the folders and files delivered.

Note: Each year is structured in three folders containing the data for the different area boundaries (1 shapefile with the data and 3 layerfiles with the symbologies of the different classification systems).

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 52

9. APPENDIX 1 9.1 Cloud masking procedure applied in this study In order to mask out cloud and cloud shadow affected areas from the Landsat imagery, RSS developed an object based classification routine in the Trimble eCognition Software. The classification of clouds is based on a cloud index, which is calculated by the equation: đ?&#x2018; đ??ˇđ??śđ??ź =

Where:

đ??ż_đ??ľ6 â&#x2C6;&#x2019; đ??ż_đ??ľ1 đ??ż_đ??ľ6 + đ??ż_đ??ľ1

NDCI:

Normalized Difference Cloud Index

L_B6:

Emittance Landsat Band 6

L_B1:

Reflectance Landsat Band 1

Landsat Band 1 (blue) and Band 6 (Thermal) are very suitable for the discrimination of clouds from other image objects, due to two typical characteristics of clouds: -

High brightness in the visible range of the electromagnetic spectrum Cold temperature and therefore low emittance in the thermal range of the electromagnetic spectrum

This has also been demonstrated by Martinuzzi et al. (2006) who created cloud masks in Puerto Rico by the use of these bands. The RSS routine combines the information of the two bands in order to be able to generate the cloud mask with only one threshold to be adjusted. A customized solution was implemented in the eCognition Architect interface which follows the workflow shown in Figure 27. Figure 28: Workflow of the RSS cloud masking routine.

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 53

The solution ingests the input image, which has gone through the pre-processing steps described in Sections 4.2.1 and 4.2.2. The first processing step is the image segmentation by the multiresolution segmentation. The first interactive step is the adjustment of the detection threshold on the cloud index by the interpreter, which is the basis for the cloud classification. Next, the interpreter has to adjust the threshold for the detection of cloud shadows that is based in the reflectance in the NIR infrared band, and executes the cloud shadow classification. In order not to confuse cloud shadows with other shadows (i.e. terrain shading), only objects are considered which are located in the proximity of previously detected clouds. The first result is the initial Cloud / Cloud shadow mask. This initial mask is grown pixel based until it covers also the mixed pixels in the adjacency of the clouds. Then, image objects completely enclosed by either cloud or cloud shadow objects are also included into the mask. The last interactive step is the visual quality check of the cloud mask, and a manual adjustment where necessary. The final cloud mask is then exported from eCognition and stored in raster format. Finally, it is applied to the input image in order to remove all cloud and cloud-affected areas.

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 54

9.2 Land cover maps Figure 29: 1991 Landsat based land cover classification for the Kapuas District

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 55

Figure 30: 1997 Landsat based land cover classification for the Kapuas District

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 56

Figure 31: 2000 Landsat based land cover classification for the Kapuas District

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 57

Figure 32: 2005 Landsat based land cover classification for the Kapuas District

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 58

Figure 33: 2009 Landsat based land cover classification for the Kapuas District

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 59

Figure 34: 1991 Landsat based land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 60

Figure 35: 1997 Landsat based land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 61

Figure 36: 2000 Landsat based land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 62

Figure 37: 2005 Landsat based land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 63

Figure 38: 2009 Landsat based land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 64

Figure 39: 1991 Landsat based land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 65

Figure 40: 1997 Landsat based land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 66

Figure 41: 2000 Landsat based land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 67

Figure 42: 2005 Landsat based land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 68

Figure 43: 2009 Landsat based land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 69

Figure 44: 1991 Landsat based MOFor-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 70

Figure 45: 1997 Landsat based MOFor-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 71

Figure 46: 2000 Landsat based MOFor-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 72

Figure 47: 2005 Landsat based MOFor-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 73

Figure 48: 2009 Landsat based MOFor-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 74

Figure 49: 1991 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 75

Figure 50: 1997 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 76

Figure 51: 2000 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 77

Figure 52: 2005 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 78

Figure 53: 2009 Landsat based MOFor-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 79

Figure 54: 1991 Landsat based MOFor-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 80

Figure 55: 1997 Landsat based MOFor-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 81

Figure 56: 2000 Landsat based MOFor-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 82

Figure 57: 2005 Landsat based MOFor-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 83

Figure 58: 2009 Landsat based MOFor-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 84

Figure 59: 1991 Landsat based UKP4-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 85

Figure 60: 1997 Landsat based UKP4-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 86

Figure 61: 2000 Landsat based UKP4-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 87

Figure 62: 2005 Landsat based UKP4-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 88

Figure 63: 2009 Landsat based UKP4-conform land cover classification for the Kapuas district

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 89

Figure 64: 1991 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 90

Figure 65: 1997 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 91

Figure 66: 2000 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 92

Figure 67: 2005 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 93

Figure 68: 2009 Landsat based UKP4-conform land cover classification for the GoI Kapuas Area

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 94

Figure 69: 1991 Landsat based UKP4-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 95

Figure 70: 1997 Landsat based UKP4-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 96

Figure 71: 2000 Landsat based UKP4-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 97

Figure 72: 2005 Landsat based UKP4-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 98

Figure 73: 2009 Landsat based UKP4-conform land cover classification for the KFCP site

Source: Remote Sensing Solutions GmbH

Historical Land Cover Classifications and Land Cover Change in the Kalimantan Forests and Climate Partnership (KFCP) site and the Kapuas District

Page 99