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TitleUse of RADARSAT SAR data for sustainable management of natural resources: A test case in the Kayapó indigenous area, Pará, Brazil
AuthorMalcolm, J R; Zimmerman, B I; Cavalcanti, R B; Ahern, F; Pietsch, R W
SourceCanadian Journal of Remote Sensing 24, 4, 1998.,
Alt SeriesEarth Sciences Sector, Contribution Series 20042879
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
Released2014 07 31
In support of research to design and implement sustainable resource use in the Kayapó Indigenous Area in southern Pará, Brazil, the utility of RADARSAT Standard and Fine Mode Synthetic Aperture Radar data were tested. The abilities of RADARSAT to accomplish two tasks were of interest: 1) the identification of small gaps in the forest canopy resulting from logging and other human disturbances, and 2) discrimination among principal ecosystem types in the area, in particular cerrado (savannah), upland forest, and floodplain forest. To this end, RADARSAT scenes were compared to maps of mahogany extraction activities in the area and a Landsat TM scene. Extraction activities (which occurred from 0.5 to 5.0 years prior to the RADARSAT recording) were not evident in the RADARSAT scenes. Although Fine Mode data distinguished canopy breaks better than did Standard Mode data, both data sets identified only those canopy openings that were at least tens of metres across and consisted of bare ground. A small (c. 60 ha) cerrado isolate was not visible in the radar data, despite only 50-75% vegetation cover. Although average backscatter differed slightly between rainforest and large cerrado regions, the noise-to-signal ratio was extremely high. Floodplain forest could be successfully distinguished from upland forest by making use of combined measures of backscatter strength and textural graininess. The relatively fine-grain structure of floodplain forest relative to upland forest may have indicated more level ground in the former, but differences in canopy architecture also may have been important. The classification of forest types based on spatial backscatter properties will be a challenge in areas that are topographically diverse.

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