This presentation, presented at the 36th International Symposium on Remote Sensing of Environment, explains the importance of peatlands to Indonesia as well as their contribution to carbon emissions. ALOS PALSAR data and above ground biomass assessments are used to map peatlands.
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Characterizing Forest Degradation and Carbon Biomass Assessment in Tropical Peatlands using Multi-Temporal and Multi-Polarizations SAR Data
1. Characterizing Forest Degradation and
Carbon Biomass Assessment in Tropical
Peatlands using Multi-Temporal and Multi-
Polarizations SAR Data
Contributors: Arief Wijaya, Ari Susanti, Oka Karyanto, Wahyu Wardhana, Lou
Verchot and Veraldo Liesenberg
36th International Symposium on Remote Sensing of Environment
Berlin, 10 – 15 May 2015
2. Project Background
This work is part larger CIFOR projects
Global Comparative Study on REDD+ (GCS REDD+) –
work in 6 countries
CIFOR is an international research organization working
based on three pillars – research, capacity building and
media outreach
This study is CIFOR research portfolio to address climate
change
2
3. Outline of Presentation
Importance of peatlands for Indonesia
CO2 emissions from peatlands
Test site descriptions
Data and methods
Results
– Peatlands mapping using ALOS PALSAR data
– Above ground biomass assessment
4. Importance of Peatlands Ecosystem
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
degraded peatlands forests were lost between 1990 – 2012
5. CO2 Emissions from Deforestation, Peat
Drainage and Peat Fires in Indonesia
Peatlands contribute to 30 – 50% of total carbon emissions from
forestry sector between 1990 and 2012
8. Available Satellite Data
Quad polarizations ALOS Palsar (May 2010) – forest
degradation discrimination
Quad polarizations ALOS Palsar (April 2007, May 2007,
April 2009, April 2010) - biomass modeling
Landsat 5 image (February 2010)
Peatland maps from Wetlands International
Land use/land cover map from Ministry of Forestry (2000 –
2012)
9. Field Data Collection
Land use
Volume
(m3/ha)
AGB
(Mg/ha)
Carbon stocks
(MgC/ha)
Tree
height
(m)
Basal area
(m2)
LAI
Peat swamp
forest
592.35 228.25 114.12 16.92 33.19 2.96
Pioneer species
(Macaranga sp.)
32.63 7.47 3.74 11.22 9.71 3.09
Mangrove forest 182.16 105.65 52.82 9.53 11.79 2.38
Plantation forest 196.33 130.95 65.48 12.57 13.22 1.32
Rubber
plantation
125.73 113.44 56.72 14.95 51.02 2.06
- In total, 83 sampling plots were collected during dry season in 2010
- Circular nested sampling plots within 60 x 60 m2
- Size of each sub plot 0.04 ha
15. Above ground biomass modeling results
ID
Acquisition
date
Predictor(s) Biomass model coefficients R2 SEE F Sig.
7001 20/5/2007
HH, HV, VH,
VV, Alpha,
Entropy,
Anisotropy
y = 157.38HH - 467.09HV + 405.08VH + 23.4VV -
41.6Alpha + 4913.7Entropy - 1096.4Anisotropy +
175.656
0.278 202.217 0.441 0.851
2001 9/4/2009
HH, HV, VH,
VV, Alpha,
Entropy,
Anisotropy
y = 171.07HH - 298.26HV + 264.46VH + 96.1VV +
50.89Alpha + 714.9Entropy + 2189.26Anisotropy -
825.3
0.416 181.981 0.813 0.601
1001 12/4/2010
HH, HV, VH,
VV, Alpha,
Entropy,
Anisotropy
y = 191.57HH - 30.7HV + 104.46VH + 31.69VV -
25.19Alpha + 1150.47Entropy + 2598.39Anisotropy +
3248.58
0.670 99.83 5.356 0.015
1001 12/4/2010 HH y = 238.164HH + 2987.913 0.744 91.014 40.733 0.000
Features selection is apparently important for biomass modeling
16. Seasonal Variability or Land Use Change?
0
50
100
150
200
250
300
350
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Monthlyprecipitation(mm)
Month
mean =263 mm
Source: Indonesian National Metereological Agency
ALOS Palsar acquisition (2007, 2009, 2010)
Field work (2010)
Coverage of ALOS and Landsat data is national/global, TerraSAR X more on sub-national coverage
Temporal resolution reolution of Landsat 16-18 days, ALOS Palsar has gone but replaced with ALOS 2
Source of ground data – we use high spatial resolution for study in Kalimantan, and additional field data and national land cover map for study in Sumatera
Conventional Confusion matrices approach is used to validate the resulted maps
This method, in terms of R&D needs good competence in RS data analysis, especially to handle preprocessing of SAR data which is normally not straight forward as the optical data
The approach will complement national estimate on forest degradation with more accurate result. Jurisdictional approach of REDD project should find better and more accurate methods to map forest cover change and/or forest degradation and eventually come up with better predictions of carbon emissions
Description:
One scene of quad-polarimetric ALOS PALSAR data acquired during the end of rain season on May 2010 and Landsat 5 Thematic Mapper (TM) from February 2010 are used to characterize forest degradation event. Calibration of radar backscatter and corrections to other topographic distortions were conducted. Noise filtering to reduce speckle effects is applied using Enhanced-Lee filter. Polarimetric features, i.e. entropy, anisotropy and alpha angle, were generated from the coherency matrices of polarimetric SAR data and plotted against training observation data over degraded peatlands, pristine peatland forest and other land uses to characterize the profiles of radar backscatter in discriminating different peat forests conditions.
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).