Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

Abstract

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.

Uma arquitetura de rede neural convolucional para detecção de alvos de desmatamento na Amazônia através de imagens SAR ICEYE

“…Já em [Kuck et al 2021], foram testados diferentes modelos pré-treinados de redes neurais convolucionais para detecção de mudanças associadas à degradação florestal em produtos bitemporais obtidos a partir de um par de imagens SAR COSMO-SkyMed adquiridas antes e depois da extração na Floresta Nacional do Jamari. Os resultados indicaram que os dados SAR multitemporais da banda X têm potencial para monitorar a extração seletiva em florestas tropicais, especialmente em combinação com as redes neurais convolucionais.…”

Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks

“…In this methodology, a set of statistical parameters of backscatter coefficients are computed and used as input to the network. Similarly, Neitzel et al [40] evaluate several pretrained DL architectures to detect selective logging using different scores of contextual and textural information from X-Band SAR data and compose an RGB composition. Wahab et al [41] also used a Convolutional Neural Network (CNN) to map deforestation using Sentinel-1 data.…”