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An evaluation of RADARSAT-1 and ASTER data for mapping veredas (palm swamps)

TitleAn evaluation of RADARSAT-1 and ASTER data for mapping veredas (palm swamps)
Publication TypeJournal Article
Year of Publication2008
AuthorsMaillard, P., R. Alencar-Silva, and D. A. Clausi
JournalSensors
Pagination6055-6076
KeywordsASTER, Markov Random Fields, Palm swaps, Radarsat, Supervised Classification, unsupervised classification, Vegetation types, Wetlands
Abstract

Veredas (palm swamps) are wetland complexes associated with the Brazilian savanna ( cerrado ) that often represent the only available source of water for the ecosystem during the dry months. Their extent and condition a re mainly unknown and their cartography is an essential issue for their protect ion. This research article evaluates some of the fine resolution satellite data both in the rada r (Radarsat-1) and optical domain (ASTER) for the delineation and characterization of veredas . Two separate approaches are evaluated. First, given the known potential of Radarsat-1 imag es for wetland inventories, the automatic delineation of veredas is tested using only Radarsat-1 data and a Markov random fields region-based segmentation. In this case, to increase performance, processing is limited to a buffer zone around the river network. Then, characterization of their type is attempted using traditional classification methods of ASTER optical data combined with Radarsat-1 data. The automatic classification of Ra darsat data yielded results with an overall accuracy between 62 and 69%, that proved reliable enough for delineating wide and very humid veredas . Scenes from the wet season and with a smaller ang le of incidence systematically yielded better results. For the clas sification of the main vegetation types, better results (overall success of 78.8%) were obta ined by using only the visible and near infrared (VNIR) bands of the ASTER image. Radarsat data did not bring any improvement to these classification results. In fact, when usin g solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low