During the last decades, the science of remote sensing of the Earth's surface has produced an enormous amount of data. In parallel, with the increase in computational capacity, several classification methods have ...
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During the last decades, the science of remote sensing of the Earth's surface has produced an enormous amount of data. In parallel, with the increase in computational capacity, several classification methods have been applied to the satellite retrievals. This timely combination allows recovering more accurate knowledge about the land cover maps of past times. Therefore, the main goal of this work was to develop a land cover product for the year 1985 in the Upper Parana River Basin (UPRB-1985), one of the largest and most economically important river basins in the world. The land cover map was developed using a supervised classifier - SVM (Support Vector Machine) applied to data from Landsat TM (Thematic Mapper) sensor. The classification process was carried out based on 52 scenes collected during 1985 and a total of 17,040 training samples across the basin. Pixel and object-based methods were used to classify Landsat scenes. The generated mapping accuracy was assessed using statistical criteria adopted in the literature - Global Accuracy and Kappa Index. The McNemar's test result showed no significant differences (at the 5% level) between the Pixel-based and object-based classifications, even with the object-based classification accuracy was slightly higher (Global Accuracy of 79.8%). However, some relationship between the relief and the classification approach was observed. In sub-basins with high slopes, the mean overall accuracy values of the Pixel-basedclassification approach were 13.1% higher than the object-based approach. By mapping past land cover, this work is strategic information to understand ongoing processes, as well as to assess changes in land cover that have occurred over time and evaluate to what extent they explain the variability in the hydrology of the region.
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