Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Seasonality of net radiation in two sub-basins of Paracatu by the use of modis sensor products

The net radiation (Rn) represents the main source of energy for physical and chemical processes that occur in the surface-atmosphere interface, and it is used for air and soil heating, water transfer, in the form of vapor from the surface to the atmosphere, and for the metabolism of plants, especially photosynthesis. If there is no record of net radiation in certain areas, the use of information is important to help determine it. Among them we can highlight those provided by remote sensing. In this context, this work aims to estimate the net radiation, with the use of products of MODIS sensor, in the sub-basins of Entre Ribeiros creek and Preto River, located between the Brazilian states of Goiás and Minas Gerais. The SEBAL (Surface Energy Balance Algorithm for Land) was used to obtain the Rn in four different days in the period of July to October, 2007. The Rn results obtained were consistent with others cited in the literature and are important because the orbital information can help determine the Rn in areas where there are not automatic weather stations to record the net radiation.