This document discusses the assessment of forest dynamics and carbon biomass in tropical peatlands of Indonesia, emphasizing the use of multi-remote sensing approaches. It highlights the significant role of peatlands in contributing to CO2 emissions through deforestation, peat decomposition, and peat fires. The study aims to improve classification accuracy using SAR and optical data to support national reporting to the UNFCCC.

1. 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 Emissions
Technical Session 3: State of the Art Technology for Carbon Stock
Assessment and Monitoring
Jakarta, 3 – 5 March 2015

2. 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
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3. 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
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4. 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
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5. CO2 Emissions from Deforestation, Peat
Drainage and Peat Fires in Indonesia

6. Contributions of CO2 Emissions by Islands

7. Land Cover Classification System
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Landuse/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 Forest
2 Secondary Upland Forest/Logged Forest
3 Primary Swamp Forest
4 Secondary Swamp Forest/Logged Area
5 Primary Mangrove Forest
6 Secondary Mangrove Forest/Logged
7 Crop Forest
8 Oil Palm and Estate Crops
9 Bushes/Shrubland
10 Swampy Bush
11 Savanna
12 Upland Farming
No Classification
13 Upland Farming Mixed with
Bush
14 Rice field
15 Cultured Fisheries/Fishpond
16 Settlement/Developed Land
17 Transmigration
18 Open Land
19 Mining/mines
20 Water Body
21 Swamp
22 Cloud
23 Airport/Harbor

8.  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
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National Forest Degradation Mapping

9. Deforestation Drivers Analysis
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What about drivers of forest degradation?

10. Saatchi biomass map

11. Baccini biomass map

12. 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

13. Carbon density by landcover type
Forest classes carbon (ton/ha) SD (ton/ha)
Primary dry forest (PF 2001) 179.9 16.9
Secondary dry/logged over forest (SF 2002) 173.7 15.2
Primary Swamp Forest (PSF 2005) 155.5 19.2
Secondary swamp forest(SSF 20051) 143.8 19.7
Primary mangrove forest (PMF 2004) 87.4 13.4
Secondary mangrove forest (SMF 20041) 62.6 8.9
Crop 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 19.9
Bushes/Shrubland (B2007) 123.9 13.7
Swampy bush (SB 20071) 77.6 14.1
Savanna (S 3000) 63.1 11.3
Upland farming (UF 20091) 79.9 14.5
Upland farming mixed with bushes (Pc 20092) 115.2 17.2
Rice field (Sw 20093) 62.8 12.0

14. Carbon stocks change 2000 - 2009
Based on Multiply and Stratify approach. The figure shows only C stocks above ground.
2000

15. Carbon stocks change 2000 - 2009
2009

16. Landcover and carbon density
Landcover 2000 Landcover 2009
(a) (b)
(c) (d)

17. Degradation Mapping Exercise
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18. 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
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19. Peatlands under study
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Class label Peat types Peat
thickness
Proportions
(%)
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: 0.23
Fibrists: 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: 0.23
Fibrists: 0.13
Mineral: 0.32
Primary
forest
Very shallow
peat in sparse
forest (PVSp)
Hermists/mineral
(H1i)
<0.5m (very
shallow)
20/80 Hermists: 0.23
Mineral: 0.32
Sparse
forest
Shallow peat
in secondary
swamp forest
(PSS)
Hermists/fibrists/
mineral (H1b)
0.5–1m
(shallow)
50/30/20 Hermists: 0.23
Fibrists: 0.13
Mineral: 0.32
Secondary
forest

20. SAR Data Decomposition
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21. SAR Backscatter Responses
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22. Alpha Entropy Plane
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23. Land Cover Map
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24. PLR SAR Features
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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)

25. Alpha Entropy Plane
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1
2
3
4
5
6
7
8
9

26. Initial SAR Classification
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27. 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
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