Mapping deforestation and land use in amazon rainforest by using SIR-C imagery

Abstract

In this paper, the potential use of spaceborne polarimetric synthetic aperture radar (SAR) data in mapping landcover types and monitoring deforestation in tropics is studied. Here, the emphasis is placed on several clearing practices and forest regeneration that can be characterized by using the sensitivity of SAR channels to vegetation biomass and canopy structure. A supervised Bayesian classifier designed for SAR signal statistics is employed to separate five classes: primary forest, secondary forest, pasture-crops, quebradao, and disturbed forest. The L- and C-band polarimetric SAR data acquired during the shuttle imaging radar-C (SIR-C)/X-SAR space-shuttle mission in 1994 are used as input data to the classifier. The results are verified by field observation and comparison with the Landsat data acquired in August of 1994. The SAR data can delineate these five classes with approximately 72% accuracy. The confusion arises when separating old secondary forests from primary forest and the young ones from pasture-crops. It is shown that Landsat and SAR data carry complementary information about the vegetation structure that, when used in synergism, may increase the classification accuracy over secondary forest regrowth. When the number of land-cover types was reduced to three classes including primary forest, pasture-crops, and regrowth-disturbed forest, the accuracy of classification increased to 87%. A dimensionality analysis of the classifier showed that the accuracy can be further improved to 92% by reducing the feature space to L-band HH and HV channels. Comparison of SIR-C data acquired in April (wet period) and October (dry period) indicates that multi-temporal data can be used for monitoring deforestation; however, the data acquired during the wet season are not suitable for accurate land-cover classification.