This document summarizes research using remote sensing to map tropical peatlands in Indonesia. It tested various classification approaches combining synthetic aperture radar (SAR) and optical sensor data to improve land cover mapping. Initial results found that combining SAR polarimetric features like alpha angle, entropy and anisotropy with reflectance from optical data improved classification of peatland types including primary swamp forest, secondary swamp forest and sparse forest. Further research is needed to upgrade technical capacity in Indonesia and integrate remote sensing methods with national forest monitoring systems.

1. Joint GFOI/GOFC-GOLD Expert Workshop 2:
Approaches to monitoring forest degradation for REDD+
Characterizing Forest Degradation and Carbon
Biomass Assessment in 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
Wageningen, The Netherlands
1-3 October, 2014

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
3

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. Carbon emission from Peatlands
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Source: MOF and CIFOR analysis

6. National Forest Degradation Mapping
• Most likely based on national land cover
change map from 1990 – 2013
• 23 land cover types – primary and secondary
forests
• Degradation is change of primary to
secondary forests – upland, mangrove, peat
swamp ecosystems
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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
21

21. SAR Backscatter Responses
22

22. Alpha Entropy Plane
23

23. Land Cover Map
26

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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28. R&D Challenge/Opportunity
• More difficult to accept modeling approach
• Proposed methods should fit in with existing
national capacity
• Multi-stakeholders discussion and
involvement in various meetings
• Inform country about international
guidelines/standards/conventions which can
be applied
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