Peatland Definition and Peatland Mapping Methodology Assessment

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ICCC Peatland Definition and Peatland Mapping Methodology Assessment Report

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Suggested Citation: ICCC, 2014. ICCC Peatland Definition and Peatland Mapping Methodology Assessment Report. Indonesia Climate Change Center. Jakarta, Indonesia.

This report was prepared by Mazars Starling Resources (MSR) for the Peatland Definition and Peatland Mapping Methodology Assessment commissioned by Indonesia Climate Change Center (ICCC) and funded by United States Forest Service (USFS).

ICCC Primary Contact:

Badan Pengkajian dan Penerapan Teknologi I (BPPT) building, 16th FloorJalan M.H.Thamrin 8, Jakarta 10340, IndonesiaEmail: [email protected]

USFS Primary Contact:

United States Forest Service International Programs1 Thomas CircleWashington, DC. 2009Tel: +1 (202) 644-4571

Mazars Starling Resources Primary Contact:

Mazars Starling ResourcesIkat Plaza Building, 3rd Floor,Jl. Bypass Ngurah Rai No. 505Denpasar, Bali 80361, IndonesiaTel: +62-(0)361-847-3141

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Acknowledgement

The Research Report of Peatland Definition and Peatland Mapping Methodology Assessment is developed based on research conducted from April to September 2013 in collaboration with several universities, agencies and organizations.

ICCC would like to thank all contributors, researchers as well as administrative, field survey and technical staff, local communities of Teluk Meranti and Pangkalan Kapau villages, Pelalawan District, and local communities of Tewang Kampung and Kampung Melayu villages, Katingan District, for their support. We also would like to thank to all project team members: Mazars Starling Resources, Hokkaido University, Japan Space Systems, Bandung Institute of Technology, University of Riau, University of Palangkaraya, Yayasan Puter Indonesia, and Institute Social and Economic Change.

*All images Indonesia Climate Change Center

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Table of Contents

Acknowledgement List of Acronyms ForewordSummary

ICCC Peatland Definition and Peatland Mapping Methodology Assessment

Section A: Peatland Mapping A1. Introduction A2. Methodology

A2.1. Theoretical Background of the Shimada Model A2.2. Limitations and Assumptions of the Shimada Model A2.3. Methodological Procedures

A3. Results and Discussions A3.1. Results of Spatial Analysis A3.2. Results of Peat Depth Measurement and Sampling A3.3. Results of Peatland Mapping

Section B: Historical Peatland Management Practices and Implications for Land-Use Land-Cover Changes B1. Introduction B2. Methodology

B2.1. Remote-Sensing Analysis B2.2. Focus Group Discussions and Social Baseline Surveys

B3. Results and Discussions B3.1. Historical Overview of LULCC Patterns B3.2. Remote Sensing Analysis of LULCC in Pelalawan District B3.3. Remote Sensing Analysis of LULCC in Katingan District

Section C: Climate Change and Its Potential Impacts on Peatland C1. Introduction C2. Methodology

C2.1. Data Acquisition C2.2. Methods C2.3. Limitations

C3. Results and Discussions C3.1. Historical Observation of Climatic Variations in Pelalawan District C3.2. Historical Observation of Climatic Variations in Katingan District C3.3. Climate Projection for Pelalawan District C3.4. Climate Projection for Katingan District C3.5. Climate Extremes for Pelalawan District C3.6. Climate Extremes for Katingan District C3.7. Potential Impacts of Climate Change on Peatland

4 7 9 10 29

34 34 35 35 36 3645 455156

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6060606162626670

7373747475767777798284868789

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Section D: Use of Satellite-Based Ground Water Table (GWT) Data for Estimating Net CO2 Emissions From Peatland D1. Introduction D2. Methodology

D2.1. Theoretical Background of The Estimation of CO2 Emissions From Satellite- Based GWT Data D2.2. Methodological Steps D2.3. Limitations

D3. Results and Discussions D3.1. Empirical GWT Measurement D3.2. Estimation of CO2 Emissions Based on The Satellite-Based GWT Modeling D3.3. Estimation of The Volume of Peat Drainage

Section E: Toward Sustainable Peatland Management in Indonesia E1. Implications of Sustainable Peatland Management for Climate Change Mitigation E2. Recommendations for Sustainable Peatland Management in Indonesia

E2.1. One Accurate Peatland Map and Spatial Planning E2.2. Protection of The Remaining Peatlands E2.3. Peatland Best Management Practices E2.4. Prevention of Peatland Fires E2.5. Peatland Ecosystem Restoration

References Annexes Annex 1. New Peatland Map of Pelalawan District Annex 2. New Peatland Map of Katingan District Annex 3. Land-Use, Land-Cover Change Analysis of Peatland Area in Pelalawan District Annex 4. Land-Use, Land-Cover Change Analysis of Peatland Area in Katingan District

91 91 9191

9399100100 102103

106106108108109110111112

114119120121122123

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List of Acronyms

AR4 Fourth Assessment Report (IPCC)AVHRR Advanced Very High Resolution RadiometerBIG Badan Informasi GeospasialBMKG Badan Meteorologi, Klimatologi dan GeofisikaCO2 Carbon dioxideCDF Cumulative Density FunctionCRU Climate Research Unit (University of East Anglia)DB Drained Burnt PeatlandDBH Diameter at breast height (1.3 meters)DEM Digital Elevation ModelDF Drained ForestDNPI Dewan Nasional Perubahan Iklim (National Council on Climate Change)ENSO El Nio Southern OscillationERC Ecosystem Restoration ConcessionFGD Focus Group DiscussionGCM Global Circulation ModelGHG Greenhouse GasGIS Geographic Information SystemGPP Gross Primary Production (ecosystem photosynthesis)GPS Global Positioning SystemGWL Ground Water LevelGWP Global Warming PotentialGWT Ground Water TableHa HectareHCV High Conservation ValueHPH Hak Pengusahaan Hutan (forest concession license)HP Hutan Produksi (production forest) HTI Hutan Tanaman Industri (industrial timber plantation)HV Horizontal transmitting, Vertical receiving (PALSAR)ICCC Intergovernmental Panel on Climate ChangeIPCC Intergovernmental Panel on Climate ChangeITB Institut Teknologi BandungITCZ Inter-Tropical Convergence ZoneJICA Japan International Cooperation AgencyLULC Land Use and Land CoverLULCC Land Use and Land Cover ChangeLULUCF Land Use, Land-Use Change and Forestrym Metermm MillimeterMoA Ministry of Agriculture IndonesiaMODIS Moderate Resolution Imaging SpectroradiometerMoE Ministry of the EnvironmentMoF Ministry of Forestry IndonesiaMSR Mazars Starling ResourcesMT Metric tonneNDVI Normalized Difference Vegetation indexNEE Net Ecosystem ExchangeNGO Non-governmental OrganizationNOAA National Oceanic and Atmospheric Administration (US Government)PALSAR Phased Array type L-band Synthetic Aperture Radar

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PET Potential EvapotranspirationPDF Probability Density FunctionPDO Pacific Decadal OscillationPSF Plant-Soil FeedbackRE Ecosystem RespirationRePPProt Regional Physical Planning Program for TransmigrationRGB Red/Green/Blue (satellite imagery)RSNI Rancangan Standarisasi Nasional Indonesia (Indonesia National Standardization Program)SAR Strategic Assessment Report (IPCC)SRES Special Report on Emissions ScenariosSRTM Shutter Radar Topography MissiontC Tonnes of CarbontCO2e Metric tonne of Carbon Dioxide equivalentTM Landsat Thematic MapperTS Time SeriesUF Undrained ForestUNEP United Nations Environment ProgrammeUSGS United States Geological Survey VV Vertical transmitting, Vertical receiving (PALSAR)WI Wetlands International

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Foreword

Indonesia Climate Change Center (ICCC) through several Expert Meetings has formulated a consensus Peatland Definition and Peatland Mapping Methodology. As a follow up, to provide information on scientific analyses supporting policy and actions dealing with greenhouse gas (GHG) emissions from peatland, and to accommodate the robust policy making, ICCC has conducted a Peatland Definition and Peatland Mapping Methodology Assessment in Pelalawan District, Riau Province, and Katingan District, Central Kalimantan Province. This study was conducted to test the Peatland Definition and Peatland Mapping Methodology developed by ICCC.

This assessment has been carried out in close consultation with Experts from universities and agencies. The assessment by ICCC includes all the activities of: 1) Testing the Peatland Definition; 2) Testing the Peatland Mapping Methodology; 3) Collecting time series data and information related to socio-economic activity, demographic, disaster (fires, droughts, and floods) and other information related to the interaction of communities with peatland. With this result, ICCC is targeted to support the policy makers in developing the robust policies towards sustainable peatland management. From assessment in those two pilot sites, ICCC provides: 1) Data and analysis results based on scientific; 2) Key findings; and 3) Recommendations for further work to develop robust peatland assessments.

It is our pleasure to share this assessment results with you.

We thank all partners who have contributed to this effort and join us in finding solution for climate change issues, particularly on peatland.

Farhan Helmy

Manager Indonesia Climate Change Center (ICCC)

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Summary

INTRODUCTION

An objective of Peatland and Peatland Mapping Cluster (PPMC) of the Indonesia Climate Change Center (ICCC) is to disseminate information on scientific analyses supporting policy and actions dealing with greenhouse gas (GHG) emissions from peatland. Accordingly, ICCC needs to present reliable scientific information to policy makers to facilitate renewed policy on peatland management in support of Government of Indonesias GHG emission reduction target. To accommodate robust science-based peatland policy development, PPMC has developed a Peatland Definition and Peatland Mapping Methodology to assist in improving existing peatland maps. The next step, which is critical to the PPMC objective, is to conduct analyses based on field data collected from identified sites to evaluate the PPMC Peatland Definition and Peatland Mapping Methodology based on the existing Wetlands International and Ministry of Agriculture maps in selected sample areas. Recognizing the need for an ecosystem-based sustainable approach, PPMC aims to: 1) Assess the applicability of the PPMC Peatland Definition; 2) Demonstrate and assess the PPMC Peatland Mapping Methodology; and 3) Collect time series data and information related to socio-economic activity, demographics, disasters (fires, droughts, and floods), water management, agriculture in peatland, and information related to community-based peatland interactions in the project areas. Supported by reliable data, these activities will enhance ICCC efforts to support science-based policy towards sustainable peatland management in Indonesia.

In order to achieve the program objectives, ICCC carry out a science-based assessment of peatland methodologies with the following objectives:

To assess peatland mapping methodologies;

To develop new accurate peatland maps for the target sites;

To recommend a sustainable peatland management model by considering the balance between GHG emission reductions and socio-economic needs; and

To build collaborative partnerships with national and international universities, NGOs, and communities to exchange knowledge and facilitate capacity building.

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STUDY SITE

The peatland mapping assessment was conducted for Pelalawan District, Riau Province, and Katingan District, Central Kalimantan Province, Indonesia (Figure 1).

Figure 1: Study site in Pelalawan District, Riau Province and Katingan District, Central Kalimantan Province

Pelalawan District encompasses an area of 1,392,494 hectare (ha) or 14.73% of the total size of Riau Province. It is bordering with Siak District in the north, Indragiri Hulu and Indragiri Hilir Districts in the south, Kampar and Indragiri Hulu Districts in the west, and Karimun, Kepri and Bengkalis Districts in the east. Approximately 536,000 ha or 40% of the district covers peatland. Among 12 sub-districts in Pelalawan District, Kerumutan, Teluk Meranti, Pelalawan and Kuala Kampar sub-districts are situated on peatland. Pelalawan District falls within a tropical climate, with average annual precipitation of 219.65 millimeters (mm) and the average air temperature of 27.22 degrees Celsius (C) in 2012. The total population on peatland areas in 2011 was 44,469.

Katingan District encompasses an area of 1,750,000 ha, or 11.4% of the total size of Central Kalimantan Province, within which there are 13 districts, one municipality, 131 sub-districts, and 1,339 villages. It is bordering with East and West Kalimantan Districts in the north, Java Sea in the south, West Kalimantan District in the west, and East and South Kalimantan Districts in the east. Approximately 643,800 ha or 38% of the district covers peatland. Among 13 sub-districts in Katingan District, Tasik Payawan, Kamipang, Mendawai and Katingan Kuala sub-districts are, in part, situated on and/or around peatland. The district falls within a tropical climate, with average annual precipitation of 329 mm and the average air temperature of 26.9 C in 2012. The total population on peatland areas in 2011 was 69,330.

Pelalawan District

(1,392,494 ha)

Katingan District (1,750,000 ha)

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Section A: PEATLAND MAPPING EXERCISE IN PELALAWAN AND KATINGAN

DISTRICTS

BACKGROUND

Peatland maps developed by each source have indicated different and inconsistent results. This is because different data, definitions, methods and precision levels were adopted in the process of analyzing peatland distribution and depths. Such inconsistencies have hindered the effective implementation of policies, regulations, and spatial planning and zoning at the district, provincial and national levels. Creating one accurate, integrated and consistent peatland map based on a well-developed methodology throughout Indonesia is essential for sustainable peatland management. To accommodate the accuracy improvement of existing peatland map, Indonesia Climate Change Center (ICCC) has conducted a peatland mapping exercise in Pelalawan District, Riau Province and Katingan District, Central Kalimantan Province. The aims of the exercise are as follows,

A quick review of existing peatland maps (i.e., MoA 2012 and WI 2004) and identification of gaps for target study sites;

Testsoftheproposedmethodologyinthefield;

Calibrationofpeatdepthsandpeatlandboundarybasedonfielddataandspatialmodeling (i.e., the Shimada Model) integrated with satellite data such as Landsat and Palsar; and

Peatlandmappingwithmoreaccurateestimationofpeatdistributionanddepths.

FINDINGS

The results suggested different peat depths from WI peatland maps

The peat depths or peatland areas identified during the survey showed different results from Wetlands International (WI) peatland maps (Figure 2a). Several sampling points in Pelalawan District, which were classified as non-peatland or not-counted areas by the WI maps, were found to be peatland. Similarly, in Katingan, some areas categorized as peatland with the thickness between 0.5 to 2 meters fell outside of peatland (Figure 2b). Such sampling data, especially those that occur around boundaries of peatland and non-peatland areas, are very important for more accurate delineation of peatland.

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The deepest peat classified by WI and MoA maps were limited up to 8 meters in Pelalawan, meanwhile based on the field survey by ICCC, the areas of peat depths deeper than 8 m represented a large fraction of the data for the region. These areas were widely distributed around the northern part of Pelalawan. The study also estimated a larger extent of peatland distribution to the east of Kampar River compared with the existing maps.

The new peatland map in Pelalawan and Katingan Districts identified considerably large differences in peatland area compared to the existing WI and MoA maps.

Figure 2b: Peat depth data from the field survey overlaid with WI peatland map in

Pelalawan

Figure 2a: Peat depth data from the field survey overlaid with WI peatland map in

Pelalawan

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Key differences found in the Shimada Model based peatland map compared with

Color indication

Wetlands International

peatland map (ha)

Ministry of Agriculture

peatland map (ha)

Pelalawan District1. Area with deeper peat deposits 313,489 351,9982. Area with no noticeable difference 300,574 267,2033. Area with shallower peat deposits 18,975 15,1214. Area identified as non-peatland in this study but as peatland by WI and/or MoA

46,571 42,859

5. Area identified as peatland in this study but as non-peatland by WI and/or MoA

99,713 97,226

Katingan District1. Area with deeper peat deposits 205,526 224,9212. Area with no noticeable difference 138,958 141,8843. Area with shallower peat deposits 141,336 140,3404. Area identified as non-peatland in this study but as peatland by WI and/or MoA

25,213 28,886

5. Area identified as peatland in this study but as non-peatland by WI and/or MoA

98,455 79,580

For Pelalawan, WI map indicates a total peatland area larger than that of the MoA map by 2,693 ha. These differences are insignificant, and such discrepancies were probably caused in the process of data processing. On the other hand, for Katingan, the MoA map indicates a total peatland area larger than that of the WI map by as much as 24,696 ha. This is a significant difference, and further investigation is considered necessary. The new maps developed under this study identified considerably large differences in both peatland distribution and peat depths compared to the existing maps. Table 1 presents gaps between WI map and the new peatland map based on the Shimada Model.

The new maps developed under this study identified considerably large differences in peatland distribution compared to the existing WI and MoA maps.

Figure 3a and 3b present gaps identified for Pelalawan District, and Figure 4 a and 4b for Katingan District. Indicated in blue are areas in which the existing WI and MoA maps showed as non-peatland or no-data area but this study identified as peatland. For Pelalawan District, such areas are estimated to be as large as 99,713 ha (compared with the WI map) and 97,226 ha (compared with the MoA map). For Katingan District, these areas are estimated to be approximately 98,455 ha (compared with the WI map) and 79,580 ha (compared with the MoA map). This implies that the extent of peatland distribution indicated by WI and MoA maps may be underestimated, and that both Pelalawan and Katingan districts are likely to cover larger areas of peatland as suggested by this study.

Table 1: Gaps between the new peatland map compared with WI and MoA maps

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Areas shown in pink indicate areas where the WI and MoA maps identified as peatland, but were assumed otherwise by this study. Some of these areas previously considered as peatland were reclassified as non-peatland based on the findings from the field sampling data. Other areas which lack field data need to be surveyed in order to verify the results.

Brown and light green colored areas are classified as peatland by all maps, but with different peat depths. Brown areas show areas where peat depths were considered to be deeper by this study than the same peat areas estimated by WI and MoA. Light green areas, on the other hand, show areas in which this study estimated shallower peat deposits than those of WI and MoA. Areas indicated in gray showed no noticeable differences among WI, MoA and this study, and were considered as peatland with a similar peat depth distribution.

Figure 3b: Differences between the new Pelalawan peatland map with MoA

peatland map

Figure 3a: Differences between the new Pelalawan peatland map with WI

peatland map

Figure 4b: Differences between the new Katingan peatland map with WI

peatland map

Figure 4a: Differences between the new Katingan peatland map with

WI peatland map

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FUTURE WORKS

This study examined the applicability of a new peatland mapping methodology for Pelalawan and Katingan districts based on the Shimada Model method combined with manual delineation by using remote sensing images, field sampling data, and Kriging extrapolation method. It also improved the accuracy of existing peatland maps which have been developed by Wetlands International (WI) and Ministry of Agriculture Indonesia (MoA). The new peatland maps were overlaid with BIG topographic map of Pelalawan and Katingan Districts, and were adjusted to the 1:50,000 scale. This methodology can be applied to other peatland areas at minimum technical complexity and costs. However, to result the methodology which can be applied by national level, combining the Shimada Model and existing methodology is necessary. Improving the Shimada Model (or combined use of different models) by including peat depth estimation for non-forest areas is important.

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Section B: HISTORICAL PEATLAND MANAGEMENT AND IMPLICATIONS FOR

LAND-USE LAND-COVER CHANGES

BACKGROUND

Land-use and land-cover changes (LULCC) in tropical regions are largely linked to shifting economic opportunities as well as socio-political and infrastructural needs (Hecht, 1985). In fact, increasing global demand for agricultural crops, palm oil and timber, combined with population growth and economic development, is the key driving factor for LULCC in Pelalawan and Katingan Districts.

While Indonesias LULCC information is available for certain locations, little is known about the extent to which peatland is converted into different land-uses. This is partly because of the inadequate knowledge and research about tropical peatland itself and the lack of underlying data.

A good understanding of the complexity and dynamics of LULCC is a key to proper planning and utilization of natural resources, and also to strategizing Indonesias land-use and climate change policies in the future. In this study, a detailed spatial analysis of land-use and land-cover on peatland in Pelalawan and Katingan Districts was conducted based on satellite imagery, field sampling data, and information from secondary sources.

FINDINGS

Agricultural frontiers in Pelalawan and Katingan Districts have been progressively moving toward deep peatland due to the limitation in land availability

Since the late 1980s, local farmers have changed agricultural practices from shifting cultivation to land-based farming methods. In general, farmers prefer to develop agricultural fields on mineral soil or shallow peat. This is because they are more fertile, easier to maintain, and therefore, less costly than deep peatland, in which heavy irrigation and maintenance are required. Despite this, agricultural frontiers in the study area have been progressively moving toward deep peatland due to limitations in land availability.

Land use changes in peatland drive peat degradation and subsidence

Because of the need for community lands, a considerably large area of peat swamp forests was converted into smallholder rubber and oil palm plantation. As a result, new canals and ditches were built, causing peat degradation. Peat subsidence and the lowering of the water table have been observed in many community lands, and the incidence of peat fires and haze has been mounting during the dry season.

The remote sensing based LULCC analysis for Pelalawan and Katingan Districts has witnessed large scale forest degradation and conversion over the past 20 years

In 1990, a large part of peatland in Pelalawan District was covered by dense swamp forest. Approximately 390,397 ha or 47% of the peatland was still intact, and 325,052 ha or 40% was secondary forest. The remaining primary peat swamp forest covers about 135,562 ha 65% of intact forest has already disappeared since 1990; only 180,215 ha of secondary peat swamp forest remain today. Figure 5 shows the summary of key LULCC on peatland in Pelalawan District, which were observed from 1990 to 2013.

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Figure 5: Summary of key LULCC (in hectare) on peatland in Pelalawan District from 1990 to 2013

In 1990, 598,281 ha or more than 98% of peatland in Katingan District was still covered with swamp forest in which approximately 46% consisted of primary peat swamp forest and 52% consisted of secondary forest. Between 1973 and 2002, the timber industry was the primary economic driver in the area, and massive logging concessions (HPH) occupied the landscape. Illegal logging was also rampant during this period, and accelerated the rate of deforestation and degradation on peatland. Between 1990 and 1995, approximately 7% of primary peat swamp forest and 12% of secondary forest were converted into shrub and grassland areas. As a result, shrub and grassland areas increased by 599%, encompassing 66,956 ha of peatland during this period. Such land cover changes are more evident on the eastern side of the Katingan River. Figure 6 shows the summary of key LULCC on peatland in Katingan District, which were observed from 1990 to 2013.

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Figure 6: Summary of key LULCC (in hectare) on peatland in Katingan District from 1990 to 2013

FUTURE WORKS

In addition to the prevention of peatland conversion, drainage and fires, it is important to restore already degraded peatland in order to reverse trends of deforestation and a rapid loss of peatland ecosystems. Restoration activities are likely to pose socio-economic impacts on people living in surrounding areas, and thus should be planned and implemented through participatory approaches.

For both Pelalawan and Katingan Districts, to support the Sustainable Peatland Management, the awareness and capacity development on the importance of peatland through continuous research and knowledge building, and the development of silviculture techniques and assisted natural regeneration of native species on peatland is need to be addressed.

In Pelalawan, the development of buffer zones around industrial plantations to be collaboratively managed with local communities is necessary, and the effective zoning, conservation and monitoring of high conservation value (HCV) forest and species in plantations is important. Meanwhile, in Katingan, some recommendations for achieving Sustainable Peatland Management are as follows, 1) Peat rewetting and implementation of effective water management; 2) Reforestation in non-forest areas and enrichment planting in degraded areas with native species; 3) Development of buffer zones to be managed as community forest; and 4) Protection and assisted regeneration of high conservation value (HCV) species.

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Section C: CLIMATE CHANGE AND ITS POTENTIAL IMPACTS ON PEATLAND

BACKGROUND

Climate change is often associated with increasing extreme weather events. IPCC defines that [a] changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of extreme weather and climate events, and can result in unprecedented extreme weather and climate events (IPCC, 2000). Information on climate extremes is essential to understand implications of climate change and assess social and environmental risks. Peatland ecosystems are of most vulnerable and exposed to climate stress. Furthermore, natural hazards pose considerable impacts on people living on and around peatland in Indonesia. When peat is dry, especially during the dry season, it can easily spread fires which can continue to burn down to the water table for an extended period of time. Peat degradation also deteriorates the water retention ability of soil and often causes unseasonal floods. Unpredictable weather patterns also pose adverse impacts on economic activities such as farming and fishing, and crop productivity.

A science-based understanding of climate change, climate projection and its potential impacts has important implications for society and sustainable development. It will allow policy makers to determine climate change mitigation strategies, while also supporting appropriate interventions to disaster and disaster risk management. It is also key to developing sustainable peatland management approaches, in which GHG emission reduction objectives and socioeconomic needs are balanced. Therefore, ICCC initiated study in Pelalawan and Katingan District which aiming to provide the historical climate variations and provide climate change projections up to 2050 based on seven Global Circulation Models. In this study, it will be discussed about the occurrence and projection of climate extremes by conducting statistical probably analysis.

FINDINGS

An average air temperature has shown an uprising trend of 1.33 C in the period of 1950-2011 in Pelalawan District

The historical observation of temperature and precipitation trends for Pelalawan District has shown large variability in local climate patterns since 1950. Average air temperature has shown an uprising trend of 1.33 C and rainfall patterns have indicated an increasing trend of anomalies (i.e., more frequent extremes such as drought and flood events) over the period of 1950-2011. The occurrence of extreme weather in the study area has been more frequent with increasing intensity. Figure 7 shows the historical trend of temperatures and precipitations in Pelalawan.

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Figure 7: Historical trend of temperatures (left) and precipitations (right) from 1950 to 2012 in Pelalawan

Pelalawan District is particularly prone to climate related natural hazards such as floods, droughts and forest/peat fires. An increasing trend of temperatures was found to be more evident and drastic since the 1990s. The recent trend of hotter temperatures was also mentioned and validated during focus group discussions in sample villages. Some years also indicated extreme rainfalls and floods, showing obvious anomalies in rainfall patterns. Furthermore, there have been mounting cases of forest and peat fires intensified by prolonged dry seasons every few years, and severe floods and storms during rainy seasons. Many farmers have experienced peat fires in their plantations and gardens especially during El Nio years, and have lost considerable harvests for the season.

High frequency of changes in surface water levels in rivers and tributaries in Pelalawan District

Local communities are also aware of a high frequency of changes in surface water levels in rivers and tributaries. Salt water intrusion has been observed by several community members in the area as a result of lower water levels and potential changes in rainfall regimes. This potentially poses a significant threat to the areas peatland ecosystems. It could damage trees and aquatic organisms, and agricultural productivity and crop yields could also decrease drastically due to the salinity of peatland.

An average air temperature has shown an uprising trend of 0.58 C in the period of 1950-2011 in Katingan District

In Katingan, on the other hand, an increasing trend of average temperatures was recorded at 0.58 C during the overall period from 1950 to 2011, but a decreasing trend in recent years during the period between 1997 and 2012. The pattern of temperature variations in Katingan is different from that of the Pelalawan region, which indicated a discernible ascending trend over the past two decades. Figure 8 shows the historical trend of temperatures and precipitations in Katingan.

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Figure 8: Historical trend of temperatures (left) and precipitations (right) from 1950 to 2012 in Katingan

Similar to Pelalawan, Katingan District is particularly prone to climate related natural hazards such as floods, droughts and forest/peat fires. There have been mounting cases of forest and peat fires intensified by prolonged dry seasons every few years, and severe floods and storms during rainy seasons. Hotter air temperatures and changing surface water levels in rivers and tributaries are also evident in the area.

Temperature trends over a 40 year period demonstrated a continuous increase at the degree of 1.37 C for Pelalawan and 1.28 C for Katingan

The projection of climate extremes was analyzed by using monthly GCM outputs. Temperature trends over a 40 year period demonstrated a continuous increase at the degree of 1.37 C for Pelalawan and 1.28 C for Katingan. The projection of precipitation from 2011 to 2050, on the other hand, showed no significant uprising trend for Pelalawan, and a gentle decreasing trend for Katingan. Nevertheless, the analysis of PDF and CDF patterns indicated that the frequency of the highest precipitation events and the intensity of rainfalls are likely to increase in the future for both regions. This is likely to cause more frequent flood events in the future.

FUTURE WORKS

Peatland is prone to climate related natural hazards such as floods, droughts and forest/peat fires. In Pelalawan District, there have been mounting cases of forest and peat fires intensified by prolonged dry seasons, heat waves, and severe floods and storms during rainy seasons. Similarly, Katingan District is expected to experience raising temperatures but lower precipitation rates over the next few decades. Reduced rainfall and higher temperatures may also increase the risk of peat fires and other climate hazards.

Climate change is likely to cause serious environmental, economic and social impacts on communities living on or around peatland in Pelalawan and Katingan Districts. Unpredictable seasonality and extreme weather events put heavy burdens on local farmers. Floods and storms can also damage their agricultural crops and cause economic losses. Peat fires also pose direct economic and health risks to local communities as well as neighboring regions and countries. Furthermore, haze pollution has been witnessed in several peat fire cases, including the ones that occurred across multiple provinces in Sumatra and Kalimantan islands in 1997-1998, and more recently in Riau Province in June 2013.

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Section D: USE OF SATELLITE-BASED GROUND WATER TABLE DATA FOR ESTIMAT-

ING NET CO2 EMISSIONS FROM PEATLAND

BACKGROUND

Tropical peatland is one of the largest terrestrial carbon stores, and plays a major role in global hydrological cycles and atmospheric circulation. Although the decomposition of organic matters occurs naturally over time, in Indonesia, it has been extensively and rapidly caused by the degradation of peat compounds due to anthropogenic activities. Peatland degradation in Indonesia is often associated with forest conversion, deforestation and peat fires. Peat drainage due to the construction of canals and ditches for irrigation and transportation purposes lowers ground water table (GWT) depths, resulting in the loss of hydrological integrity, peat oxidation and subsidence. This results in greenhouse gases being released into the atmosphere in mass quantities, and consequently leads to climate change.

While there are a number of researches which have studied the effect of drainage on CO2 emissions based on subsidence data and chamber methods, the magnitude of ecosystem-scale carbon balance on tropical peatland is still unknown (Hirano, et. al., 2012). GWT is one of the key parameters to understand carbon cycling on peatland, and the monitoring and recording of GWT fluctuations is crucial to quantify their net CO2 emissions. Thus, this section of the report presents key steps and preliminary results from the assessment of a satellite-based net CO2 estimation methodology based on the eddy covariance technique and empirical GWT measurements on peatland in Pelalawan and Katingan Districts.

Moreover, this study aimed to assess the methodology for the estimation of the volume of water released from peatland into canals at maximum and minimum scenarios. Although the preliminary results from the hydrological drainage model could not be integrated into the overall estimation of CO2 emissions from peatland during this assignment (due to time and resource constraints), this model is instrumental to analyze the potential effect of canal and irrigation trench development on peatland in future research.

FINDINGS

This study found that ground water table (GWT) is one of the key parameters to understand carbon cycling on peatland, and the monitoring and recording of GWT fluctuations is crucial to quantify their net CO2 emissions. Based on the eddy covariance technique and empirical GWT measurements, we examined relationships between net ecosystem exchange (NEE) in the atmosphere and GTW levels to estimate net CO2 emissions from three distinctive sample sites Undrained peat swamp forest (UF), Drained peat swamp forest (DF), and Drained and burned peatland (DB). At all sites, net CO2 emissions increased as GWT lowered. On the UF sample sites, net CO2 emissions (positive NEE) occurred when the GWT lowered by 6 cm from the surface. On the DF sample sites, net CO2 emissions occurred when the GWT lowered by 31 cm. On the DB sample sites, we found consistent net CO2 emissions at all GWT levels, even the surface was inundated.

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Net CO2 emissions from each peatland ecosystem types were estimated based on the daily mean NEE values (gC/m2) of September 1, 2013. The total sample size of UF, DF and DB pixels for Pelalawan District were 90, 101 and 16, and 80, 277 and 43 for Katingan District, respectively. Figure 9 shows the result of the estimation of net CO2 emissions from the study sites: a) Pelalawan District; and b) Katingan District. A linear relationship between estimated average net CO2 emissions (expressed in NEE values) and GWT fluctuations from peatland at different disturbance levels are clearly shown. In all sample sites, net CO2 emissions increased as GWL lowered.

Figure 9a: Relationships between estimated daily mean net CO2 emissions (NEE gC/m2) and GWL (cm) from peatland at different disturbance levels in Pelalawan District

Figure 9b: Relationships between estimated daily mean net CO2 emissions (NEE gC/m2) and GWL (cm) from peatland at different disturbance levels in Katingan District

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FUTURE WORKS

The study by ICCC in Pelalawan and Katingan Districts suggested some future works as follows,

The development of fire hot spot mapping methodology based on satellite-based GWT data and a satellite sensor such as Moderate Resolution Imaging Spectroradiometer (MODIS), overlaid with an accurate peatland map, for early warning and reporting systems;

Monitoring of water table levels in fire prone areas especially during the dry season;

A study on effective alternative land clearing methods for agriculture;

Awareness and capacity building on the prevention, control and impacts of peat fires among smallholder farmers;

For Pelalawan District: 1) Improve law enforcement to regulate the use of fire in plantation development; and 2) Develop collaboration with industrial plantation concessionaires to control drainage at an optimal level;

For Katingan District, the small-scale canal blocking to prevent drainage and maintain GWT levels high in the Sebangau National park and the proposed ecosystem restoration concession site is important.

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Section E: TOWARD SUSTAINABLE PEATLAND MANAGEMENT IN INDONESIA

BACKGROUND

This study provided science-based methodological, empirical and conceptual approaches to climate change mitigation potentials and needs for peatland in Pelalawan and Katingan Districts. While the degree of climate change impacts varies across regions, it is important to incorporate scientifically rigorous methodologies and practical mitigation strategies into climate policies at national, provincial and district levels. Both general and region specific recommendations are provided below to conclude this report.

FINDINGS AND RECOMMENDATIONS

Based on the study results by ICCC, the components should be considered to achieve the Sustainable Peatland Management are as follows,

1. One accurate peatland map and spatial planning

Creating one integrated, transparent, consistent and collaboratively developed peatland map throughout Indonesia is essential for effectively implementing policies, regulations, and sustainable peatland management strategies. The map should be developed upon a standardized and scientifically rigorous methodology, and serve as the basis for low emission land-use planning and zoning at the district, provincial and national levels.

2. Protection of the remaining peatland

The foremost threat to the areas peatland ecosystems is the conversion of peatland and peat swamp forest into other land uses such as oil palm plantations, pulpwood plantations (e.g., acacia), non-food crop plantations (e.g., rubber), and/or agricultural lands. In order to reduce ecological pressures and GHG emissions from peatland conversion, the government must protect the remaining peatland through effective policies and multi-stakeholder engagement.

3. Peatland best management practices

In addition to the protection of remaining peatland through various legal measures as presented above, peatland best management practices must be developed and communicated among stakeholders in order to reduce GHG emissions and other socio-ecological pressures. Best practices should be science-based, socio-culturally acceptable, environmentally appropriate and financially feasible, and draw on the experience of experts and local communities.

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4. Prevention of peatland fires

Peatland fires usually occur because of unsustainable land-use practices, and are one of the main causes of massive deforestation and peatland degradation, and pose negative environmental and social impacts. Fires almost always occur on non-forest and degraded peatland during the dry season, often caused by land clearing for farming and by accident (e.g., cigarettes and cooking fires) on drained peatland. The prevention of peatland fires is critical for sustainable peatland management and mitigation of GHG emissions.

5. Peatland ecosystem restoration

In addition to the prevention of peatland conversion, drainage and fires, it is important to restore already degraded peatland in order to reverse trends of deforestation and a rapid loss of peatland ecosystems. Restoration activities are likely to pose socio-economic impacts on people living in surrounding areas, and thus should be planned and implemented through participatory approaches.

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INTRODUCTION

1. Background and objectives

Climate change and greenhouse gas (GHG) emissions resulting from land-use, land-use change and forestry (LULUCF) is increasingly relevant on the global stage. Indonesia is among the top five countries for GHG emissions, and has been the worlds third largest source of emissions forsome years, including 2005 and 2009 (UNEP, 2012). In 2005, as much as 85% of the total emissions in Indonesia resulted from LULUCF and peatland, 41% of which were emissions from carbon-rich peatland (DNPI, 2010).

Despite such ecological functions, there is an increasing interest in peatland conversion for agricultural development in Indonesia. Although the decomposition of organic matter occurs naturally over time, it can also be caused by the degradation of peat compounds. Peatland degradation in Indonesia is often associated with forest conversion, deforestation and peat fires. Peat drainage due to the construction of canals and ditches for irrigation and transportation purposes lowers water table depths resulting in the loss of hydrological integrity, peat oxidation, and lowering of ground surface levels or subsidence. This results in greenhouse gases being released into the atmosphere in mass quantities, and consequently leads to climate change.

Recognizing that scientific analyses are critical to supporting policy and actions related to GHG emissions from peatland, the Indonesia Climate Change Center (ICCC) seeks to make science-based contributions to policy development in two key areas: 1) Recommended regulations of water management systems for land use activities on peatland, and 2) Recommended low-emission land use management practices on peatland across multiple sectors. ICCCs target sites encompass two districts Pelalawan District, Riau Province, and Katingan District, Central Kalimantan Province.

In order to achieve the program objectives, ICCC commissioned Mazars Starling Resources (MSR) to carry out a science-based assessment of peatland methodologies with the following objectives:

To assess peatland mapping methodologies;

To develop new accurate peatland maps for the target sites;

To recommend a sustainable peatland management model by considering the balance between GHG emission reductions andsocio-economic needs; and

To build collaborative partnerships with national and international universities, NGOs, and communities to exchange knowledge and facilitate capacity building.

ICCC PEATLAND DEFINITION AND PEATLAND MAPPING METHODOLOGY ASSESSMENT

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2. Study sites

The peatland mapping assessment was conducted for Pelalawan District, Riau Province, and Katingan District, Central Kalimantan Province, Indonesia (Figure 1).

Figure 1: Location of study sites and size of districts

2.1. Background of Pelalawan District

Pelalawan District encompasses an area of 1,392,494 hectares (ha) or 14.73% of the total size of Riau Province. It I borders with Siak District in the north, Indragiri Hulu and Indragiri Hilir Districts in the south, Kampar and Indragiri Hulu Districts in the west, and Karimun, Kepri and Bengkalis Districts in the east. Peatland covers approximately 536,000 ha or 40% of the district. Among 12 sub-districts in Pelalawan District, Kerumutan, Teluk Meranti, Pelalawan and Kuala Kampar Sub-districts are situated on peatland. Pelalawan District falls within a tropical climate zone, with average annual precipitation of 219.65 millimeters (mm) and the average air temperature of 27.22 degrees Celsius (C) in 2012. The total population on peatland areas in 2011 was 44,469 (Figure 2).

Figure 2: Population map of peatland area in Pelalawan District

Pelalawan District (1,392,494 ha)

Katingan District (1,750,000 ha)

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2.2. Background of Katingan Districts

Katingan District encompasses an area of 1,750,000 ha, or 11.4% of the total size of Central Kalimantan Province, within which there are 13 districts, one municipality, 131 sub-districts, and 1,339 villages. It is borders with East and West Kalimantan Districts in the north, Java Sea in the south, West Kalimantan District in the west, and East and South Kalimantan Districts in the east. Approximately 643,800 ha or 38% of the district is situated onpeatland. Among 13 sub-districts in Katingan District, Tasik Payawan, Kamipang, Mendawai and Katingan Kuala Sub-districts are, in part, situated on and/or around peatlands. The district falls within a tropical climate zone, with average annual precipitation of 329 mm and the average air temperature of 26.9 C in 2012. The total population on peatland areas in 2011 was 69,330 (Figure 3).

3. Definition and classifications

3.1. Definitions

In this study, the following definitions were adopted.

Peatland is defined as an area with accumulation of partly decomposed organic matter with ash content equal to or less than 35%, peat depth equal to or deeper than 50 cm, and organic carbon content (by weight) of at least 12% (ICCC, 2012).

Peat thickness or depth is a vertical distance from the ground surface until the substratum layer (mineral soil layer) underneath the peat layer.

Land-use refers to types of management and activities people undertake within a land-cover class.

Land-cover refers to the observed biophysical surface cover on the earth (FAO, 2000).

Figure 3: Population map of peatland area in Katingan District

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3.2. Classifications

Table 1 presents LULC classifications and descriptions, and Table 2 shows peat depth categories adopted in this study. Peat depth categories used in this study are intended to provide detailed classifications for the purpose of climate change mitigation, and do not necessarily address the current demarcation of 3 m threshold peat deeper than 3 m is protected from any kind of development by Indonesian law.

Table 1: Land-use and land-cover classification

No Land cover Description

1 Primary Peat Swamp Forest

Intact peat swamp forest which occurs on peatland with no traces of logging tracks, drainage network or the history of forest fires. It is a tall forest with uneven canopy, and consists of mixed plant species.

2 Secondary Peat Swamp Forest

Logged-over peat swamp forest which occurs on peatland with a history of logging tracks and/or drainage network and trenches. It is a disturbed forest with mixed plant species, and few trees reach DBH 50 cm in this forest type.

3 Peat swamp shrub and grassland

An open area with low woody vegetation and herbaceous plants which occurs on peatland with a history of drainage network, trenches and/or forest fires. This type of peatland is typically occupied with ferns, kelakai grasses and shrubs.

4 Plantation forest Monoculture timber and/or pulpwood (acacia) plantations with the existence of tracks and drainage network. This type of forest is typically managed by concessions

5 Oil palm plantation Monoculture oil palm plantations managed by concessions and/or smallholders with the existence of tracks and drainage network.

6 Crop plantation Mixed tree crop agroforestry and monoculture croplands occupied with permanent crops, not under a rotation system. This type of cropland is typically managed by smallholders with or without small-scale drainage network.

7 Agriculture Agricultural cropland area with or without small-scale drainage systems. This type of land includes rice paddies, vegetables and other food crops.

8 Bare land Natural and non-built-up land surface with little or no vegetation cover including burned areas, bare soils and abandoned agricultural lands.

9 Settlement House, building and other types of infrastructure

10 Water Body River, stream, canal, lake, etc.

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Table 2: Peat depth categories

Category Peat depth (m) Category Peat depth (m)PEATY 0-0.5 D3 6.0-8.0

D0 0.5-2.0 D4 8.0-10.0D1 2.0-4.0 D5 > 10.0D2 4.0-6.0

4. Outline of this report

This report is organized into five sections. Each section is composed of a brief introduction, methodology and results.

Section A. Peatland mapping Section B. Historical peatland management and implications for land-use land-cover changes Section C. Climate change and its potential impacts on peatland Section D. Use of satellite-based ground water table data for estimating net CO2 emissions from peatland Section E. Toward sustainable peatland management

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Section A:

PEATLAND MAPPING A1. Introduction

Indonesia has several peatland maps developed by various authors and organizations including the Ministry of Agriculture (MoA), Regional Physical Planning Program for Transmigration (RePPProT), Wetlands International (WI) and others (Table A1). Among these, peatland maps published by WI and MoA are the most often cited maps in Indonesia today.

Table A1: Estimates of peatland area and distribution in Indonesia by various authors and sources (Source: Najiyati et al., 2005)

*This figure does not include peatland associated with saline lands and floodplains, which are estimated to cover 2.46 million ha in Indonesia.

However, as Table A1 shows, peatland maps developed by each source have indicated different and inconsistent results. This is because different data, definitions, methods and precision levels were adopted in the process of analyzing peatland distribution and depths. Such inconsistencies have hindered the effective implementation of policies, regulations, and spatial planning and zoning at the district, provincial and national levels. Creating one accurate, integrated and consistent peatland map based on a well-developed methodology throughout Indonesia is essential for sustainable peatland management. As such, in assessing a scientifically rigorous peatland mapping methodology, this section of the report presents results of:

A quick review of existing peatland maps (i.e., MoA 2012 and WI 2004) and identification of gaps for target study sites;

Satellite analyses to estimate various parameters including elevation, peat depths, hydrological network and peat domes;

Tests of the proposed methodology in the field;

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Calibration of peat depths and peatland boundary based on field data and spatial modeling (i.e., the Shimada Model) integrated with satellite data such as Landsat and Palsar; and

Peatland mapping with more accurate estimation of peat distribution and depths.

A2. Methodology

A2.1. Theoretical background of the Shimada Model

In tropical peat swamp forests of Sumatra and Borneo,a gradual change in forest types can be observed from shallow peat on the periphery of floodplains to thick deposits on the center of peat domes (Bruenig, 1990; Shepherd et al., 1997). In tropical peat swamp forests, the type of forest stand and its phenology correspond to peat depths and seasonal groundwater level fluctuations, from which spatial trends in seasonal vegetation activity can be obtained (Shimada et al., 2004). Therefore, it is possible to extrapolate the distribution of peatland and peat depths by analyzing the relationship between forest types and peat layer thickness (Figure A1).

Peat thickness

Figure A1: Example of relationships between forest types and peat depths

In order to examine this relationship, various phenology types of peat swamp forest were classified by using multi-temporal remote sensing data (Shimada, et al., 2004). This classification method, the Shimada Model, assumes that vegetation undergoes various growth stages during different seasonal periods, and extracts phenological variables from NDVI (Normalized Difference Vegetation index).

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A2.2. Limitations and assumptions of the Shimada Model

The Shimada Model method requires that the season of study areas be clearly divided into dry and wet periods. However, Pelalawan District did not show a distinctive dry period, and precipitation patterns indicated variations with two peaks in April and November. On the other hand, precipitation patterns in Katingan District indicated only one rainfall peak in a year period, observed between the months of December and April (Figures C4b and d; C7b and d). For this reason, there are several difficulties in directly adopting the Shimada Model for our study area in Pelalawan.

With this methodology, peat depth classification accuracy is strongly dependent on the quantity and quality of the field survey dataset. Although there exists a relatively large activity database for Riau and Central Kalimantan, the density of peat drilling locations is still low. Furthermore, peat sampling locations are spatially biased due to limited accessibility and logistical challenges. The maximum likelihood method used for the classification of peat depths (see Section 2.2.5.) required at least two to three times more activity data for each satellite image (1-km grid cells) than was available (Oki et al., 2005). For this reason, some of the peat depth classification categories(i.e., 0 0.5 m and 0.5 1 m for Pelalawan, and 8 10 m and above for Katingan) were restricted in the Shimada Model, and alternatively, we used the Ordinary Kriging method to re-classify these particular categories. More uniformly distributed sampling locations would improve the classification results.

Furthermore, because this methodology is based on vegetation activities in response to hydro-periods, the Shimada Model is limited to swamp vegetation covers, and currently is not valid for non-forest areas such as bare ground or grasslands.

A2.3. Methodological procedures

A2.3.1. Spatial analysis

Cloud- and haze-free remote sensing images are limited for the tropical regions. In order to reduce data gaps and improve interpretation,initial spatial analysis was conducted by using a combination of medium-resolution optical satellite images and Synthetic Aperture Radar (SAR) data (see Table A2) as well as empirical field data. SAR data, a microwave imaging system that is able to penetrate cloud cover regardless of day or night, provided interpretation on structural characteristics of different forest and land cover types. Optical satellite sensors, on the other hand, use visible, near and short-wave infrared sensors, and detect solar radiation reflected or scattered from the earth, forming images similar to photographs.

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Table A2: Satellite sensors used in this study

Satellite sensor Data capability Year Number of scenes acquiredSpatial

resolutionLandsat TM4, 5, 7 and 8

Medium resolution optical sensor

1990, 1995, 2000, 2005, 2010, 2013

Multiple imageries for path rows 126-60

30 m for multispectral bands 1-7

ALOS PALSAR SAR active microwave sensor

2010 and 2011 14 scenes for full polarimetry mode HH+HV+VH+VV

12.5 m for full polarimetry mode

Other data used for this study include:

Geospatial Information Agency (BIG) topography maps of 1:50,000 scale;

Shutter Radar Topography Mission (SRTM) digital elevation model (DEM) data;

Peatland maps (ESRI shape file format) developed by Wetlands International (WI, 2004) and Ministry of Agriculture Indonesia (MoA, 2012); and

National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data.

Landsat TM4, 5, 7 and 8 were used as the primary satellite imagery data source. Initial data processing (pre-processing) of radiometric and geometric correction was carried out before data interpretation. In order to enhance the visual interpretation of satellite imagery, Landsat composite image colors of RGB 543 and RGB 453 were selected.

Alos Palsar satellite data was also used as a complementary data source to clarify and validate uncertain delineation and cloud-covered areas on Landsat imagery. Backscattering coefficient analysis, interferometry processing and polarimetric data processing were conducted to obtain a combination of vector information.

SRTM DEM data was used to determine geomorphological features such as peat dome structures and hydrological network for the study areas.

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A2.3.2. Sample data collection

Sampling locations were proposed beforehand based on existing WI and MoA peatland maps, satellite images, digital elevation model (DEM) data, estimated topography and streamlines, and the availability of existing peat depth data1.

1) This database, a compila-tion of peat depth data from various sources, is provided as an appendix to this report under the file name, 2013-09-15 ICCC Peatland Survey Database_FINAL.

Figure A3a: Sampling points in Pelalawan overlaid with WI peatland map

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The location of sampling points was determined according to the following criteria:

Identified gaps and/or uncertainties from WI and MoA peatland maps;

Areas which were assumed to be peat boundary or showed abrupt changes in peat depth based on DEM and satellite images;

Different existing land-use and land-cover (LULC);

Accessibility to the proposed sampling areas; and

Legal accessibility (i.e., permits) to the proposed sampling areas.

Figure A3b: Sampling points in Katingan overlaid with WI peatland map

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Above criteria were reviewed and determined by a remote sensing analysis prior to conducting field surveys. Further, field surveyors conducted a rapid observation to the surrounding areas. Based on findings from the remote sensing analysis and field observation, sampling points were determined and, whenever necessary, adjusted in the field. Total 50 sampling points were surveyed in Pelalawan District, and 51 plots in Katingan District (Figure A3a and A3b).

In this study, three types of field survey were conducted peat thickness measurement, peat core sampling, and ground water table (GWT) measurement. Each survey was carried out in close collaboration with local people and universities.

A2.3.2.1. Peat thickness measurement

A field survey of peat depth measurement followed the peatland mapping procedure described in the National Standardization Agencys Indonesia National Standardization Program (Standar Nasional Indonesia 7925: 2013). A peat sampler auger, Eijkelkamp2, was used to measure peat depths at each sampling point. Peat thickness was measured by drilling the auger into the peat soil manually, and by taking the peat corein 50 cm segments until reaching the substratum layer (Figure A4).

Figure A4: Peat thickness measurement process in the field

A2.3.2.2. Peat core sampling

Peat core samples were collected to analyze the carbon content, ash content and bulk density as peat is defined based on the percentage of these parameters found in peat samples. Sampling was conducted at about half of the peat thickness measurement points by using the Eijelkamp auger.

We collected peat cores from the peat surface down to 3 meters (i.e., 0 0.5 m, 0.5 1 m, 2.5 3 m) in 50 cm segment each, and also at the bottom part of the peat layer the transition layer to the mineral soil boundary. Peat samples were then taken at the length of 5 cm from the undisturbed part of core parts. At the transition layer,samples were collected directly above the peat-mineral soil boundary. Roots and less decomposed peat materials were carefully removed with a knife. Peat samples were placed into aluminum cups (8 cm by 7 cm),and immediately measured in the field (wet weight). Each aluminum cup was tightly sealed with aluminum foil and stored into Whirl-Pak sample bags. The samples collected during the field survey

2) The stainless steel peat sampler: http://en.eijkelkamp.com/products/soil/soil-and-sediment-sampling/semi-disturbed-sediment-sampling/peat-sampler.htm

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were sent to Biotrop3 in Bogor, West Java, for a laboratory analysis of ash contents, bulk density and carbon contents in peat samples.

A2.3.3. Vegetation activity index

A simple supervised classification method based on previous research and the existing peat depth data4 were applied to estimate spatial trends of peat depths by swamp forest types. In this classification, multi-temporal normalized difference vegetation index (NDVI) values derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation activities. In order to estimate accurate peatland depths and distribution in the study areas, the analysis was based on the land cover of the early 1990s, when peatland was relatively undisturbed and the original condition could be assessed. The following expression applies:

(1)

Where: NIR = near-infrared wavelength (Channel 2 of AVHRR) RED = the visible red wavelength (Channel 1 of AVHRR)

2.3.4. Data processing procedure

Data processing procedure is shown in Figure A6 and explained in more details below.

2.3.4.1. Acquisition of NOAA-AVHHR images

NOAA-AVHRR images were obtained from the USGS Global Land 1-km AVHHR Project5. The period of the available data is from April 1992 to January 1996, all of which were used in this study. The acquired data were pre-processed as 10-day maximum composites.

2.3.4.2. Pre-processing of NOAA-AVHHR data

First, monthly maximum composites of the NDVI data were computed. Second, noise and cloud-covered monthly datasets were eliminated to avoid no-data values in the calculation. This elimination was conducted using the Band 1 of the NOAA AVHHR dataset. The monthly composite data ultimately selected for the analysis included

NDVI = (NIR-RED)(NIR+RED)

Figure A6: Satellite data processing procedure

3) SEAMO BIOTROP Ser-vices Laboratory: http://www.biotrop.org

4) 2013-09-15 ICCC Peatland Survey Database_FINAL

5) http://edc2.usgs.gov/1KM/comp10d.php

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17 months for Pelalawan and 22 months for Katingan Districts. Finally, monthly composite raster data were stacked (assembled) as multi-temporal raster files for classification.

2.3.4.3. Delineation of the extent of peatland

Peat swamp vegetation areas were extracted from the land-cover map of 1990 to represent the potential extent of peatland. In 1990, most of the peatland areas were still covered by dense peat swamp forest, and peatland conversion or large-scale degradation had not yet occurred. This implies that swamp vegetation areas at the time represent a relatively accurate extent of peatland. In addition, activity data obtained from direct peat sampling points were added to land-cover maps. Peat swamp forest areas were further manually calibrated with reference to Landsat TM images6 with the band order 5(R), 4(G) and 3(B).

For the manual delineation of peatland, slope raster data with a 1-km resolution were compiled based on the elevation point data from the BIG 1:50,000-scale topographic map. 95% of the sampling points that occurred on peatland intersected with slope raster data, which indicated slope angles of less than or equal to 0.2. These areas were defined as gentle slope areas (Figure A7).Manual delineation was conducted with reference to these gentle slope areas. However, whenever valley morphology was identified within an area of interest, the area was eliminated even if it was located within a gentle slope area. Similarly, riparian forests on flood plains were separated from peatland and also eliminated during this process. The delineation of peatland for the isolated eastern island of Pelalawan District, where no sampling data or BIG slope raster data were available, was solely based on the Landsat images.

These areas, determined as peatland based on the data processing explained above, were integrated and used to mask the data for supervised classification results.

6) Scene IDs: lt51260601990154bkt00, lt51250601989256bkt00

Figure A7: Distribution of Peat sampling points and Gentle slope areas (left: Pelalawan, right: Katingan)

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2.3.4.4. Selection of training data

Because the Shimada Model classifies peat depths based on the vegetation activity index, the training data must be extracted from forested areas. Therefore, only sampling points that intersect with peat swamp forest areas were extracted as training data, and other points that occurred outside forested areas were omitted in this analysis. In order to have a sufficient sample size for classification, we used existing data sets from various sources7as well as the new survey data collected during this study. Landsat images were used for visual interpretation to determine whether these sampling points were located within forested areas.

All of the activity data points were re-classified into 1-km grid cells to make them correspond to the resolution of the NOAA-AVHRR data. Based on the Shimada Model, peat depth data for Pelalawan District were initially categorized into 5 classes and Katingan District into 6 classes (Table A3). Such inconsistencies in peat depth classification occurred because the shallower peat strata in Pelalawan and the deeper strata in Katingan (red circled classes) were under-sampled. Inclusion of small sample data would result in statistical biases and uncertainties in the estimation of peat depths based on the Shimada Model, and thus these categories had to be bundled.

Table A3: Peat depth category for Shimada Model

Category Peat depth (m) Pelalawan Peat depth (m) KatinganPEATY

0-4.00-0.5

D0 0.5-2.0D1 2.0-4.0D2 4.0-6.0 4.0-6.0D3 6.0-8.0 6.0-8.0D4 8.0-10.0

>8.0D5 > 10.0

2.3.4.5. Supervised classification of peat depths

Supervised classification was conducted using stacked (assembled) multi-temporal NOAA AVHRR data and selected training datasets. The classification procedure is shown in Figure A8. We adopted a maximum likelihood discriminant analysis, a method that is most commonly used in supervised classification of satellite imagery. In this method, the Mahalanobis distance, a unit less relative measure of similarity of an unknown sample data point to a known one, was used to assign each pixel of the satellite image to one of the peat depth categories. Two-dimensional cases (e.g., satellite images from two months) were considered to measure the distance8. The initial output of this classification is in raster format written in GeoTIFF.

The Mahalanobis distance and likelihood are inversely proportional. The method of maximum likelihood discrimination selects a set of values for the model parameters which maximize this likelihood in multiple dimensions (multi-satellite images). The maximum likelihood analysis is based on the Gaussian function, and a small number of training data would cause a low-quality estimation of the likelihood distribution, since the arrangement of the pre-classified training data is too sparse within the space. Thus, a large amount of training data is required for an accurate estimation of classification likelihoods.

7) 2013-09-15 ICCC Peatland Survey Database_FINAL

8) The Euclidian distance from x to the centroid of category PD2 is equal to that from x to PD1. However, the probability that x belongs to category PD1 is almost zero, since the probability density of PD1 concentrates around the centroid. Conversely, the probability that x belongs to category PD2 is greater than zero, since category PD2 has a broader density function. Thus, x is classified into category PD2.

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Figure A8: Supervised classification procedures

2.3.4.6. Conversion to vector data

For visualization purposes, the initial raster results were simplified and vectorized. First, classified raster polygons smaller than a specified threshold (4 pixels) were removed and replaced with the pixel value of the largest neighboring polygon. Second, the simplified and classified raster data were converted to vector point data (in the ESRI shapefile format). Third, Thiessen polygons were created and boundaries were smoothed using a peak algorithm with a 4000-m threshold. Finally, the smoothed Thiessen polygons were clipped using the peat swamp forest mask.

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2.3.4.7. Re-classification into detailed peat depth categories

Further to develop peatland maps, classifications used by the Shimada Model (5 for Pelalawan; 6 for Katingan) were divided into more detailed and consistent peat depth categories. An interpolation technique, known as Ordinary Kriging, was adopted in this process. This method allows the prediction of values for unmeasured locations based on the spatial analysis of the distance between measured points and their relationships to the distance. Thus, by combining two techniques, the Shimada Model and Kriging, this study ultimately classified peat depths into 7 categories for both Pelalawan and Katingan districts (Table A4).

Table A4: Peat depth category by a combination of Shimada Model and Kriging adopted in this study

Category Peat depth (m) Category Peat depth (m)PEATY 0-0.5 D3 6.0-8.0

D0 0.5-2.0 D4 8.0-10.0D1 2.0-4.0 D5 > 10.0D2 4.0-6.0

2.3.5. Software

For satellite data processing and analysis, Grass GIS 6.4 RC3 (GRASS Development Team, 2013), Quantum GIS 1.8 (Quantum GIS Development Team, 2013), and R 2.14.2 were used.

A3. Results and Discussions

A3.1. Results of spatial analysis

A3.1.1. Review of existing peatland maps

A quick review of existing peatland maps developed by the Ministry of Agriculture (MoA, 2012) and Wetlands International (WI, 2004) identified gaps between them. Figure A9a and A9b present areas of key differences between the existing peatland maps of Pelalawan and Katingan Districts, respectively.

The peatland maps published by both WI and MoA were developed by the same expert team based on the methodologies as follows (Wahyunto, et. al, 2008):

Compilation of existing sampling data and peat maps

Classification of peat depth categories (Sumatra: < 50cm, 50100 cm, 100200 cm, 200400 cm, >400 cm; Kalimantan: < 50cm, 50100 cm, 100200 cm, 200400 cm, 400800 cm, 8001200 cm)

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Satellite image analysis (Sumatra: Landsat TM7 2000 2002; Kalimantan: Landsat TM7 2001 2002) to interpret spectral signatures based on tone, texture and image patterns(e.g., crop cover type similarities and moisture) to delineate peatland areas

Extrapolation to detect peat depths from sampling and satellite data

Groundtruthing to validate the maps

Figure A9a: Pelalawan peatland map by Wetlands International (left) and MoA (right)

Figure A9b: Katingan peatland map by Wetlands International (left) and MoA (right)

For Pelalawan, the WI map indicates a total peatland area larger than that of the MoA map by 2,693 ha. These differences are insignificant, and such discrepancies were probably caused in the process of data processing. On the other hand, for Katingan, the MoA map indicates a total peatland area larger than that of the WI map by as much as 24,696 ha. This is a significant difference, and further investigation is considered necessary.

A3.1.2. Analysis of geomorphological features

Figure A10a shows a SRTM DEM image of 90m spatial resolution for Pelalawan District, and Figure A10b for Katingan District. Light color indicates higher elevations,

47


whereas dark color corresponds to lower elevations. Streamlines and depression areas are shown in dark gray. Ridgelines and forests covered with tall trees are shown in bright gray. Typical peat dome structures were also identified by SRTM DEM. Peat domes were found to be 5 to 10m higher elevations than those of the surrounding areas as shown in brighter gray color. The Y axis indicates elevation, and the X axis shows the distance.

Figure A10: Geomorphological features of a)Pelalawan District (left) and b) Katingan District (right)

The hydrological network was also analyzed by using the SRTM DEM and other available satellite images. Each pixel of the SRTM DEM data included average elevations representing 90m by 90m areas, and calculated streamlines automatically. They were further adjusted with optical and radar sensor images. Figure A11 shows estimated streamlines for Pelalawan and Katingan Districts.

Figure A11: Estimated stream line extracted from SRTM DEM for a) Pelalawan (left) and b) Katingan (right)

A

B

A B

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A3.1.3. Satellite data analysis

A combined use of Alos Palsar and Landsat imagery provided a precise land cover classification analysis (Figure A12 and A13). In this analysis, it was estimated that Z2 areas represented a volume scattering of dense forest and Z6 areas reflected a surface scattering of coarse surface or low vegetation such as agricultural cultivation areas (Figure A14). Furthermore, Z2 area was divided into three intensity (I) classes, namely, Low-I, Medium-I, and High-I. Intensity is influenced by the size of plant leaves and tree trunks, as well as vegetation moisture levels. For example, when computing the volume of biomass in forest using PALSAR data, the estimated volume becomes large if intensity is strong. Similarly, the volume of biomass is estimated to be small, if intensity is low. This implies that different plant types and structures exist within the dense forest areas in the study areas.

Figure A12: Land cover of 2010 for Pelalawan District with Palsar data (left) and Landsat data (right)

Figure A13: Land cover of 2010 for Katingan District with Palsar data (left) and Landsat data (middle and right)

The polarimetry scattering analysis of bi-dimensional classification based on entrophy (H) and alpha angle () classified peatland in Pelalawan in 2010 as very coarse rough surface, low vegetation areas (Z6) and high dense vegetation area occupied with tree crowns (Z2). Thin vegetation areas (Z5) were also observed in some areas. Similarly, in Katingan, Z2, Z5 and Z6 were identified to be the dominant classes for the peatland

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area. Within the Z2 class are further classified into two different forest types with light green as low density forest and dark green high density forest.

Figure A14: Classification of Alpha Angle and Entropy

Source: Yamaguchi, Y., Radar Polarimetry From Basics to Applications, Page 182, IEICE, Tokyo, Japan, 2007

Palsar and Landsat image data were compared to obtain the distinction of ground surface covers. Examples of satellite image comparison are provided below (Figure A15-17).

Figure A15: Comparison of Palsar and Landsat land cover classification results in Box A (from Fig. A13)

Palsar (left), Landsat 5 (middle and right)

As Figure A15 shows, the distinctive land cover pattern (red solid circle) identified by Landsat is also clearly extracted by Palsar. Boundaries of the land cover are not clear in some areas (red dotted circles) with Landsat images, and in such a case, delineation

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of land cover is heavily dependent on individual perceptions and the level of skills. By combining the classification results obtained from Palsar data with LANDSAT, land cover boundaries can be determined more accurately.

Figure A16: Comparison of Palsar and Landsat land cover classification results in Box B (from Fig. A13)


Differences of land cover types in the Landsat images are also recognized by the PALSAR image (Figure A16). However, differences in vegetation types shown in the red solid circle area detected by the Landsat data were not identified by Palsar. This may be because different tree species and/or vegetation cover with similar structural features were present in the area. On the other hand, vegetation along the river indicated by the red dotted circle appears to be homogeneous in the Landsat images,whereas the same area was classified into different types with the Palsar image. This suggests that tree species in the focal area are relatively homogeneous,but structural features such as density are different or ground surface is covered by water. Land cover information obtained with satellite images varies depending on the instruments used in the analysis.

As shown in Figure A17, in the Landsat images,several black areas appeared, indicating low reflection areas such as rivers, lakes and swamps. It is sometimes difficult to differentiate swamp areas from lakes and river tributaries only with Landsat data. Nevertheless, Palsar polarimetry data can classify swamp areas by interpreting back scattered signal intensity from the water surface. Back scattered signals from the water surface appear as low intensity, while signals from inundated swamp forest indicates high intensity. Thus the combined utilization of Landsat and PALSAR data served as the most powerful tool for swamp forest classification in this study.

Figure A17. Comparison of Palsar and Landsat land cover classification results in Box C (from Fig. A13)


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A3.2. Results of peat depth measurement and sampling

A3.2.1. Field verification of peat depths

According to the findings from a total of 101 peat depth survey points, 51 in Katingan District and 50 in Pelalawan District, peat depths of the study sites were found to vary extensively within thepeat depth categories of D1(0-0.5m) up to D7 (>10m). The summary of peat depth measurement is shown in table A5.

As Table A5 indicates, more than half of the sampling points occurred on peatland with depths between 2 to 6 meters. On average, peat deposits in sampling locations in Pelalawan District were found to be relatively deeper than those in Katingan District. Some locations were deemed to be very deep peatland with the thickness over 10 meters. However, this could not be verified, since the Eijelkamp augerpolesused in this study only extended up to 10 meters long.

Another important finding from the field verification is that peat depths or peatland areas identified during the survey showed different results from Wetlands International (WI) peatland maps (Figure A18). Several sampling points in Pelalawan District,which were classified as non-peatland or not-counted areas by the WI maps, were found to be peatland. Similarly, in Katingan, some areas categorized as peatland with the thickness between 0.5 to 2 meters fell outside of peatland. Such sampling data, especially those that occur around boundaries of peatland and non-peatland areas, are very important for more accurate delineation of peatland.

Table A5: Results of peat depth measurement points in Pelalawan and Katingan Districts

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Table A6 shows the summary of peat depth classifications in various land cover types. Various peat thicknessesoccurred even within the same land cover type. For example, in Pelalawan District, peat depths in oil palm plantationsvaried from peaty soil to D3 (i.e., 0-8 meters).Similarly, in the Katingan study area, peat layers on shrubs and grassland showed varying depths. This implies that, while anthropogenic activities have changed the original land cover types into plantations, shrubs, grassland, crops and other man-made surfaces on peatland, peatland can still be found in non-forest areas across the study areas. This is because these human-triggered land cover changes are relatively new compared tothe long history of peat formation, and peat subsidence has only been occurring gradually.

Table A6a: Peat thickness distribution in study site in Pelalawan District

Figure A18a: Peat depth data from the field survey overlaid with WI peatland map Pelalawan

Figure A18b: Peat depth data from the field survey overlaid with WI peatland map Katingan

LandcoverPelalawan

TotalPEATY D0 D1 D2 D3 D4 D5

Bare Land 1 1 1 1 4Plantation Forest 1 2 3Primary Peat Swamp Forest 1 1 2Secondary Peat Swamp Forest 6 4 6 2 18Peat Swamp Shurb and Grassland 1 3 3 1 8Oil palm plantation 2 1 2 5 2 12Crops plantation 1 1 1 3

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Table A6b: Peat thickness distribution in study site in Katingan District

LandcoverKatingan

TotalPEATY D0 D1 D2 D3 D4 D5

Agriculture 1 1Bare Land 1 5 6Secondary Peat Swamp Forest 3 6 9Peat Swamp Shurb and Grassland 10 3 9 2 6 3 1 34Crop plantation 1 1Total 12 4 17 8 6 3 1 51

A3.2.2. Analysis of peat characteristics

Peat characterization is also an important part of peatland mapping. In principal, three parameters are estimated during the peat characterization, namely: bulk density, ash content and organic carbon content.Organic carbon content and ash content values are widely used as key parameters to define peat and segregate peat from non-peat soils.

Overall, more than 190 peat samplesfrom 32 sampling points were collected during the field survey in Pelalawan and Katingan Districts. These samples included transition layers between peat and mineral soils for the comparison of characteristics. Table A7presents a statistical summary of peat sampling conducted by this study. Bulk density, organic carbon and ash content values from transition layers were excluded from the analysis, since these layers usually contain higher bulk density values and would create statistical biases. Bulk density and organic carbon content values shown in Table A6 are within the range of values p

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