LAND USE PREDICTION ACCURACY OF DIFFERENT SUPERVISED CLASSIFIERS OVER AGRICULTURE AND LIVESTOCK ECONOMY-BASED MUNICIPALITY IN BRAZIL

The dynamics of land use and land cover (LULC) are of great importance for the management of natural resources, sustainable development and urban planning over geographic space, and this condition is sometimes supported by geoprocessing and remote sensing techniques. The decision for which classification typology presents the best results is related to the application and considering the LULC prediction as input to a cellular automata (CA) network, the performances of Classification and Regression Tree (CART), Random Forest (RF) and Minimum Distance (MID) for predicting land use and occupation in Sinop, Brazil were assessed. Using the median of the reference years 2013 and 2015 to create a transition potential modelling (TPM) neural network, and then predict a scenario in 2017. With the highest performance, the RF typology reached the best performance in an area of mostly agricultural occupation, separated into four classes (native forest, urban area, water and bare soil/agricultural activity).