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

Author(s)

Kuck, Tahisa Neitzel ^{[1, 2]} ; Silva Filho, Paulo Fernando Ferreira ^[1] ; Sano, Edson Eyji ^{[3, 2]} ; Bispo, Polyanna da Conceicao ^[4] ; Shiguemori, Elcio Hideiti ^[1] ; Dalagnol, Ricardo ^{[5, 4]}

Affiliation

^[1] Inst Adv Studies IEAv, Command Control Commun Comp Intelligence Surveill, BR-12228001 Sao Jose Dos Campos - Brazil

^[2] Univ Brasilia UnB, Geosci Inst, BR-70910900 Brasilia - Brazil

^[3] Embrapa Cerrados, BR-73310970 Planaltina - Brazil

^[4] Univ Manchester, Dept Geog, Sch Environm Educ & Dev, Manchester M13 9PL, Lancs - England

^[5] Natl Inst Space Res INPE, Earth Observat & Geoinformat Div, BR-12227010 Sao Jose Dos Campos - Brazil

• Document type: Journal article
• Source: REMOTE SENSING; v. 13, n. 23 DEC 2021.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 19/21662-8 - Quantifying tree mortality with lasers: using a state-of-the-art model-data fusion approach to estimate biomass loss in tropical forests
• Grantee: Ricardo Dal'Agnol da Silva
• Support Opportunities: Scholarships in Brazil - Post-Doctoral