Sokoine University of Agriculture

Forest and forest change mapping with C- and L-Band Sar in Liwale, Tanzania

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dc.contributor.author Haarpaintner, J.
dc.contributor.author Davids, C.
dc.contributor.author Hindberg, H.
dc.contributor.author Zahabu, E.
dc.contributor.author Malimbwi, R. E.
dc.date.accessioned 2017-04-05T06:08:26Z
dc.date.available 2017-04-05T06:08:26Z
dc.date.issued 2015
dc.identifier.uri http://www.suaire.suanet.ac.tz:8080/xmlui/handle/123456789/1380
dc.description The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany en_US
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, en_US
dc.relation.ispartofseries The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,;XL-7/W3, 2015
dc.subject REDD+ en_US
dc.subject Forest en_US
dc.subject Forest Change en_US
dc.subject SAR en_US
dc.subject Liwale en_US
dc.subject Tanzania en_US
dc.title Forest and forest change mapping with C- and L-Band Sar in Liwale, Tanzania en_US
dc.type Article en_US
dc.url http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/391/2015/isprsarchives-XL-7-W3-391-2015.pdf en_US


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