<title>Combination of Landsat TM and ERS-1 satellite data to discriminate forest cover types in Brazil</title>

Abstract

The purpose of this study was to analyze the synergy between optical (Landsat TM) and microwave (ERS-1 satellite SAR) data sets, used singly and in combination, to discriminate and identify Eucalyptus stands and other cover types. The study site of interest is located in the vale do Rio Doce, State of Minas Gerais, Brazil. Reference data used in this study included detailed map and stand information (forest inventory data and management records). From the individual stand records, critical information such as cutting and planting dates were determined which show the age of the stands at the time both Landsat TM and ERS-1 radar data were obtained. The data sets were geometrically corrected, registered, and resampled to a 30 meter grid using cubic convolution interpolation. A maximum likelihood classifier was used in this study. The evaluation of the classifications was both qualitative and quantitative. Results indicated that the Landsat TM used alone was the most effective sensor system to classify Eucalyptus stands (accuracy of 94.8%), followed by the combined data set (accuracy of 85.9%). The combination of TM and ERS-1 data was generally not as effective as the use of Landsat alone, but did enable areas under clouds to be classified. The ERS-1 C-band radar data alone did not provide satisfactory results, with a classification accuracy of only 35.7%. This low accuracy was due, in part, to topographic effects. The use of ERS-1 SAR data appears to be severely limited in areas of rolling terrain, due to the variations in backscatter caused by the topographic effects.