Modelagem da dinâmica da paisagem na bacia hidrográfica do rio Caeté, Pará - Rabelo, Francisco Davy Braz (2021)
This thesis aimed to evaluate changes in land use forms in the Caeté river basin over five decades. The methodology was based on the use of digital image processing techniques with remote sensing data, generating a multitemporal analysis covering the period of five decades (1973 to 2020). Images of passive orbital platforms with the lowest possible cloud cover were obtained through research in the Earth Explorer Geological Survey (USGS) repositories and in the Image Catalog of the National Institute for Space Research in Brazil (INPE), a process followed by assembling of geographic data and the color composition of images from diferente sensors with image classification with the IsoCluster algorithm. The verification of the samples in the field, and the validation of the classification products and analysis of the obtained data were carried out through the quantification of the areas and representation of the results. For the Caeté Bay sector, the Digital Shoreline Analysis System (DSAS) tool was used, which consists of defining linear transects taken from a single baseline applied to the assessment of changes in the coastline through image analysis. Different areas with sedimentation and erosion were calculated, identified and mapped, pointing to variations related to sedimentary losses in the intertidal zone and sedimentary gains (progradation) caused by material transport through syzygy tides. As a result, it was possible to quantify the classes defined during the years 1973, 1985, 1995, 2005, 2015 and 2018. Allowing a spatiotemporal spatialization of the changes that occurred in the Caeté River Basin, identifying the main transition trends between the classes of use. Correlating periods of drought and changes in the forms of use and occupation in the Caeté river basin, in a perspective of dynamic modeling of the landscape, in addition to the implementation of methodologies that allow the scenario and prediction of possible future scenarios with current rates of change.