Characterizing Forest Dynamics and Carbon Biomass Assessment over Tropical Peatlands using Multi Remote Sensing Approaches

Arief Wijaya

Center for International Forestry Research (CIFOR), Indonesia

Contributors: Ari Susanti, Oka Karyanto, Wahyu Wardhana, Lou Verchot, Daniel Murdiyarso, Richard Gloaguen, Martin Herold, Ruandha Sugardiman, Budiharto,

Anna Tosiani, Prashanth Reddy Marpu and Veraldo Liesenberg

International Workshop on Forest Carbon EmissionsTechnical Session 3: State of the Art Technology for Carbon Stock

Assessment and MonitoringJakarta, 3 – 5 March 2015

Project Background

This work is part of CIFOR projects – Global Comparative Study on REDD+ (GCS REDD) – work in 6

countries

– Sustainable Wetlands Adaptation and Mitigation Project (SWAMP) – work in > 20 countries

CIFOR is an international research organization working based on three pillars – research, capacity building and media outreach

Background

The presentation focuses on mapping of tropical peatlands in Indonesia using SAR and optical sensors

Tested various classification approaches and SAR features combined with reflectance of optical data to improve image classification

Importance of Peatlands Ecosystem

The GoI is preparing FREL submission to UNFCCC – emissions from deforestation, peat decomposition and peat fires

Indonesia covers >80% (~20 Mha in 1990 out of 24 Mha) of tropical peatlands in SE Asia

1.1 Mha of intact peat swamp forests and 6.8 Mha of secondary peatlands forest were deforested from 1990 – 2012

CO2 Emissions from Deforestation, Peat Drainage and Peat Fires in Indonesia

Contributions of CO2 Emissions by Islands

Land Cover Classification SystemLanduse/cover classification of Indonesia for the years 1990, 1996, 2000, 2003, 2006, 2009, 2011, 2012 and 2013. Data source: LANDSAT satellite data (30 m resolution) (MOF, 2014)

No Classification

1 Primary Upland Forest2 Secondary Upland Forest/Logged Forest3 Primary Swamp Forest4 Secondary Swamp Forest/Logged Area5 Primary Mangrove Forest6 Secondary Mangrove Forest/Logged7 Crop Forest8 Oil Palm and Estate Crops9 Bushes/Shrubland10 Swampy Bush11 Savanna12 Upland Farming

No Classification

13 Upland Farming Mixed with Bush

14 Rice field15 Cultured Fisheries/Fishpond16 Settlement/Developed Land17 Transmigration18 Open Land19 Mining/mines20 Water Body21 Swamp22 Cloud 23 Airport/Harbor

Characteristics: maps based on visual interpretation of Landsat data, MMU 6.25 ha, need to assess the consistency

Not yet included in any national reporting – FREL submissions to UNFCCC during COP in Lima – issues of FD definition, REDD activity degradation/carbon stock enhancement

National Forest Degradation Mapping

Deforestation Drivers Analysis

What about drivers of forest degradation?

Saatchi biomass map

Baccini biomass map

Adjusted RS biomass measurement

Biomass map based on study by Baccini et al. (2012) including LIDAR shots data obtained during Biomass mapping training at BIG

Carbon density by landcover type

Forest classes carbon (ton/ha) SD (ton/ha)Primary dry forest (PF 2001) 179.9 16.9Secondary dry/logged over forest (SF 2002) 173.7 15.2Primary Swamp Forest (PSF 2005) 155.5 19.2Secondary swamp forest(SSF 20051) 143.8 19.7Primary mangrove forest (PMF 2004) 87.4 13.4Secondary mangrove forest (SMF 20041) 62.6 8.9Crop forest (CF 2006) 111.4 17.0

Non-forest classes (vegetated) carbon (ton/ha) SD (ton/ha)Oil Palm and estate crops (PG 2010) 95.6 19.9Bushes/Shrubland (B2007) 123.9 13.7Swampy bush (SB 20071) 77.6 14.1Savanna (S 3000) 63.1 11.3Upland farming (UF 20091) 79.9 14.5Upland farming mixed with bushes (Pc 20092) 115.2 17.2Rice field (Sw 20093) 62.8 12.0

Carbon stocks change 2000 - 2009

Based on Multiply and Stratify approach. The figure shows only C stocks above ground.

2000

Carbon stocks change 2000 - 2009

2009

Landcover and carbon density

Landcover 2000 Landcover 2009

(a) (b)

(c) (d)

Degradation Mapping Exercise

Data

Dual-polarimetry TerraSAR X data (2008)

PLR data of ALOS Palsar (2007-2009)

Landsat data

Peatland maps from Wetland International

Land use/land cover map from MoF

Peatlands under studyClass label Peat types Peat

thicknessProportions

(%)Bulk density

(gram/cc)Carbon

contents (%)Land

cover type

Mangrove forest (MF)

- - - - - Mangrove forest

Deep peat in primary swamp forest (PDP)

Hermists/fibrists (H3a)

2 – 4m (deep) 60/40 Hermists: 0.23Fibrists: 0.13

Hermists: 36%Fibrists: 43%Mineral: 31%

Primary forest

Shallow peat in primary swamp forest (PSP)

Hermists/fibrists/mineral (H1b)

0.5 – 1m (shallow)

50/30/20 Hermists: 0.23Fibrists: 0.13Mineral: 0.32

Primary forest

Very shallow peat in sparse forest (PVSp)

Hermists/mineral (H1i)

<0.5m (very shallow)

20/80 Hermists: 0.23Mineral: 0.32

Sparse forest

Shallow peat in secondary swamp forest (PSS)

Hermists/fibrists/mineral (H1b)

0.5–1m (shallow)

50/30/20 Hermists: 0.23Fibrists: 0.13Mineral: 0.32

Secondary forest

SAR Data Decomposition

SAR Backscatter Responses

Alpha Entropy Plane

Land Cover Map

PLR SAR Features

Polarimetric features: alpha angle (a), entropy (b) and anisotropy (c). Two additional polarimetric features were also calculated, PolSAR random volume over ground volume ratio (RVOG_mv) based on polarimetric data inversion and accumulation of polarimetric backscatter (span in decibel / span_db)

Alpha Entropy Plane

1

2

3

4

5

6

7

8

9

Initial SAR Classification

Technical Challenges/Opportunities

Needs to upgrade technical competence in the country – ground station is available

Access to data might not be major concern – various donors/bilateral cooperations continuously comes – JICA, EU, USAID, Norway

Methods for merging SAR and optical need good knowledge of RS data pre-processing

Relatively good IT infrastructure and facilities