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Sensors 2008, 8(9), 6055-6076; doi:10.3390/s8096055

An Evaluation of Radarsat-1 and ASTER Data for Mapping Veredas (Palm Swamps)

Departamento de Cartografia, Universidade Federal de Minas Gerais - UFMG, Belo Horizonte, Minas Gerais, Brazil
Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada
Author to whom correspondence should be addressed.
Received: 6 August 2008 / Revised: 10 September 2008 / Accepted: 22 September 2008 / Published: 26 September 2008
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR))
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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 are mainly unknown and their cartography is an essential issue for their protection. This research article evaluates some of the fine resolution satellite data both in the radar (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 images 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 Radarsat 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 angle of incidence systematically yielded better results. For the classification of the main vegetation types, better results (overall success of 78.8%) were obtained 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 using solely the Radarsat data from two different angle of incidence and two different dates, the classification results were low (50.8%) but remained powerful for delineating the permanently moist riparian forest portion of the veredas with an accuracy better than 75% in most cases. These results are considered good given the width of some types often less than 50 m wide compared with the resolution of the images (12.5 - 15 m). Comparing the classification results with the Radarsat-generated delineation allows an understanding of the relation between synthetic aperture radar (SAR) backscattering and vegetation types of the veredas. View Full-Text
Keywords: Radarsat; Unsupervised Classification; Markov Random Fields; Wetlands; Palm swamps; ASTER; Supervised Classification; Vegetation types Radarsat; Unsupervised Classification; Markov Random Fields; Wetlands; Palm swamps; ASTER; Supervised Classification; Vegetation types

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Maillard, P.; Alencar-Silva, T.; Clausi, D.A. An Evaluation of Radarsat-1 and ASTER Data for Mapping Veredas (Palm Swamps). Sensors 2008, 8, 6055-6076.

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