Forest and Forest Change Mapping with C- and L-band SAR in Liwale, Tanzania
As part of a Tanzanian-Norwegian cooperation project on Monitoring Reporting and Verification (MRV) for REDD+, 2007-2011 Cand L-band synthetic aperture radar (SAR) backscatter data from Envisat ASAR and ALOS Palsar, respectively, have been processed, analysed and used for forest and forest change ma...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/391/2015/isprsarchives-XL-7-W3-391-2015.pdf |
| Summary: | As part of a Tanzanian-Norwegian cooperation project on Monitoring Reporting and Verification (MRV) for REDD+, 2007-2011 Cand
L-band synthetic aperture radar (SAR) backscatter data from Envisat ASAR and ALOS Palsar, respectively, have been
processed, analysed and used for forest and forest change mapping over a study side in Liwale District in Lindi Region, Tanzania.
Land cover observations from forest inventory plots of the National Forestry Resources Monitoring and Assessment (NAFORMA)
project have been used for training Gaussian Mixture Models and k-means classifier that have been combined in order to map the
study region into forest, woodland and non-forest areas. Maximum forest and woodland extension masks have been extracted by
classifying maximum backscatter mosaics in HH and HV polarizations from the 2007-2011 ALOS Palsar coverage and could be
used to map efficiently inter-annual forest change by filtering out changes in non-forest areas. Envisat ASAR APS (alternate
polarization mode) have also been analysed with the aim to improve the forest/woodland/non-forest classification based on ALOS
Palsar. Clearly, the combination of C-band SAR and L-band SAR provides useful information in order to smooth the classification
and especially increase the woodland class, but an overall improvement for the wall-to-wall land type classification has yet to be
confirmed. The quality assessment and validation of the results is done with very high resolution optical data from WorldView,
Ikonos and RapidEye, and NAFORMA field observations. |
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| ISSN: | 1682-1750 2194-9034 |