The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

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It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.

O acelerado processo de urbanização é responsável por alterações antropogênicas significativas da paisagem. A compreensão e o monitoramento dessas alterações são fundamentais para o planejamento urbano, visando à contenção dos impactos ambientais resultantes da ocupação de novas áreas, como os causados pelas altas taxas de impermeabilização do solo. Neste contexto, o sensoriamento remoto apresenta uma série de ferramentas e produtos que permitem derivar relevantes informações em função da possibilidade de visão sinóptica e multitemporal do território. O objetivo deste trabalho foi avaliar a aplicação de um modelo de mistura baseado em múltiplas componentes (Multiple Endmember Spectral Mixture Analysis - MESMA) em imagens Landsat Thematic Mapper (TM) para a delimitação das frações de superfícies impermeáveis (que indicam a direção do crescimento urbano) da cidade de Manaus, Amazonas, Brasil, entre os anos de 1987 e 2006. No total, 20 espectros foram selecionados para os modelos de duas e três componentes e 18 espectros para os de quatro componentes, os quais modelaram 97,7% da imagem de 2006 e 99,2% da imagem de 1987 com um erro médio quadrático de 2,5%. Foram gerados mapas de frações nos quais o valor de cada pixel representa a porcentagem de determinada componente neste pixel. O resultado obtido mostrou que é viável o uso de imagens de satélite de média resolução espacial e dos modelos de mistura baseados em múltiplas componentes para análises que considerem os elementos físicos da cobertura do solo urbano.