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Mapping and monitoring Tapajós National Forest (PA) using multisensor data


Currently with the facility for accessing a number of data from the new instruments of surface observation, providing information in different levels of acquisition (aerial and orbital) and of resolutions (spatial, spectral, radiometric and temporal), an analysis exploring the main advantages of each remote sensor can be employed and evaluated quantitatively. The multitemporal data of multisensors make possible a more precisely targets classification, considering that the information is independent and uncorrelated. The objective of this Project is to evaluate the contribution of different sensors (MODIS and MISR/Terra, WFI, CCD and HRC/CBERS-2B, TM/Landsat, IKONOS, Videography, Radarsat-2, ALOS/PALSAR, LIDAR), with different resolutions (spatial, spectral, radiometric and temporal), for mapping and monitoring the vegetation cover and land use and estimation of biophysical parameters of Tapajós National Forest (PA). For this: (1) it will be integrated the information coming from different sensors in a unique GIS (Geographical Information System); (2) it will be tested the feasibility of using new sensors with high temporal resolution (MODIS/Terra and WFI/CBERS), in global scales for Amazon environment; (3) it will be mapped the vegetation cover and the land use of Tapajós National Forest; (4) it will be performed a relational analysis between data from several sensors for estimating biophysical parameters; and (5) phenology data and spectral characterization of different types of land cover will be collected and analyzed. The expected results of this Project are: (1) a methodology of integrating remote sensing data with different spatial, spectral, and temporal resolutions; (2) a methodology of relational analysis between remote sensors and biophysical parameters; (3) generation of maps of vegetation cover distribution and land use in several scales; (4) the estimate of biophysical parameters; and (5) characterization of phenology of different types of land cover from different sensors data. (AU)