Characterizing Forest Degradation using Multiple SAR Approaches: Case Study of Tropical Peatland Forests in Sumatera, Indonesia
Forest degradation (FD) is an important component of carbon emissions in many developing countries. According to Cancun agreement, countries are required to develop MRV system that allows to account for FD related loss or gain of carbon stocks. This study assessed the ability of quad-polarimetric L-...
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| Main Authors: | , , , , , , , |
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| 格式: | Conference or Workshop Item NonPeerReviewed |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/274411/1/Wijaya_Characterizing%20forest%20degradation%20using%20multiple%20SAR%20approaches.pdf https://repository.ugm.ac.id/274411/ http://toc.proceedings.com/21289webtoc.pdf |
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| 總結: | Forest degradation (FD) is an important component of carbon emissions in many developing countries. According to Cancun agreement, countries are required to develop MRV system that allows to account for FD related loss or gain of carbon stocks. This study assessed the ability of quad-polarimetric L-band Synthetic Aperture Radar (SAR) data and polarimetric SAR features aiming at identifying forest degradation events on tropical peat swamp forests in SE Asia region. The selected study site is on peatland forests in Kampar Peninsula, Riau Province, Sumatera, characterized with different forest disturbance, from forest plantation and oil palm concessions. Radar backscatter data (i.e. HH, HV, VH and VV), SAR polarimetric decomposition features (i.e. alpha angle, entropy and anisotropy), ratio of volume – ground scattering amplitude and combined scattering matrix element values were used as ancillary data of the classification.
Applying maximum likelihood classification (MLC) method, the SAR classification yielded 77.8% of accuracy combining radar backscatter, polarimetric features, ratio of volume-ground scattering (RVOG_mv) and joint elements intensity (span_db). Multi-layer perceptron neural network (MLP-NN) classification outperformed the MLC method in terms of classification accuracy with 79.9% of overall accuracy using a combination of SAR backscatter and multi-spectral Landsat TM bands (Band 4,5,7) in the classification. |
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