ANALYSIS AND CLASSIFICATION OF THE CABRUCA AGROFORESTRY SYSTEM WITH MACHINE LEARNING IN THE ATLANTIC FOREST BIOME – BRAZIL - Silva, Vinícius de Amorim; Carvalho, Hercules da Silva; Vila Nova Souza, Paulo Sérgio (Ra'e Ga: o Espaço Geográfico em Análise, 2025)
The absence of labeling and spectral similarity of cabruca systems in the Atlantic Forest biome for the Almada, Cachoeira, and Una River basins in the databases hinders the proper classification of these areas. Thus, this study aims to analyze the classification of cabruca areas in the mentioned basins through the use of images of different resolutions and machine learning. The methodology includes: determining land cover and land use classes, acquiring satellite images, processing the images with algorithms in Python, and evaluating supervised classification. The results show that cropping algorithms are effective in processing satellite images, preserving essential geographic information. Furthermore, the Neural Network model achieved an accuracy of 92%, regardless of the diversity of spatial resolutions. However, it is recommended to use the classification results of cabruca areas as estimates, due to the difficulty of the algorithms in distinguishing these areas from Dense Ombrophilous Forest.