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

Haarpaintner, J.; Davids, C.; Hindberg, H.; Zahabu, E.; Malimbwi, R. E.

dc.creator	Haarpaintner, J.
dc.creator	Davids, C.
dc.creator	Hindberg, H.
dc.creator	Zahabu, E.
dc.creator	Malimbwi, R. E.
dc.date	2017-04-05T06:08:26Z
dc.date	2017-04-05T06:08:26Z
dc.date	2015
dc.date.accessioned	2022-10-25T08:50:29Z
dc.date.available	2022-10-25T08:50:29Z
dc.identifier	https://www.suaire.sua.ac.tz/handle/123456789/1380
dc.identifier.uri	http://hdl.handle.net/123456789/90265
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
dc.description	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.
dc.format	application/pdf
dc.language	en
dc.publisher	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
dc.relation	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,;XL-7/W3, 2015
dc.subject	REDD+
dc.subject	Forest
dc.subject	Forest Change
dc.subject	SAR
dc.subject	Liwale
dc.subject	Tanzania
dc.title	Forest and forest change mapping with C- and L-Band Sar in Liwale, Tanzania
dc.type	Article