Selective Logging Detection in the Brazilian Amazon

Abstract

Selective logging activities are commonly observed in the Brazilian Amazon and are responsible for high forest impact. In this study, selective logging detection techniques and the spatiotemporal extension of forests impacted by logging activities between 2003 and 2014 in portions of the states of Mato Grosso, Pará, and Rondônia were assessed using remotely sensing data. Based on results obtained, it was estimated that the overall accuracies are greater than 91% for techniques applied to detect forests impacted by selective logging in the study areas. Forests impacted by selective logging increased in Western state of Mato Grosso and Northern state of Rondônia, which indicates a stage high forest activity in these regions. In contrast, in Eastern state of Pará, a decrease in forests impacted by logging activities was observed, which indicates collapsed stage of logging activities resulting from deforestation and predatory logging in that region.

A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

“…The machine learning techniques considered in this study showed good results in comparison with previous studies on the automatic detection of selective logging based on high-resolution optical or SAR images, semiautomated methods, and low temporal frequency [26,81,82]. The advantages over previous studies are as follows: (1) after training the algorithms, the method is fully automatic, reducing errors associated with human interpretation; (2) the possibility of constant monitoring even under adverse weather conditions; (3) high revisit capability of X-band COSMO-SkyMed constellation satellites; (4) high generalization capacity of pretrained networks, allowing the use of the same training set if the SAR images are acquired under the same image acquisition modes.…”

Long-term forest degradation surpasses deforestation in the Brazilian Amazon

“…We mapped six types of forest disturbance: (i) deforestation, (ii) selective logging, (iii) understory fires in intact forests, (iv) fires on logged sites, (v) forest edge effects adjacent to deforested areas, and (vi) isolated forest fragments created by deforestation. The method (29) uses a visual digital object analysis framework, digital spectral analysis (canopy texture from spectral radiance variation and canopy density from spectral mixture analysis), and then iterative calibration using field data (26)(27)(28). A dataset was constructed from more than 1200 Landsat satellite digital images covering the entire BA forest area, which were then digitally analyzed for seven observation years (OYs): 1992(OYs): , 1996(OYs): , 1999(OYs): , 2003(OYs): , 2006(OYs): , 2010, and 2014.…”

Dinâmica espaço-temporal da extração seletiva de madeiras no estado de Mato Grosso entre 1992 e 2016

“…As imagens foram coletadas nos meses de junho a agosto, período de encerramento das atividades de exploração de madeira. O presente estudo incluiu a utilização de duas bases de dados preparadas por Matricardi et al (2013) e Costa et al (2019). A detecção das florestas exploradas seletivamente em 2016 foi conduzida no presente estudo.…”

“…Na última década, a exploração seletiva de florestas nativas está se expandindo para as últimas fronteiras da Amazônia (COSTA et al, 2019). Apesar disso, a maior parte dos artigos científicos sobre a extração seletiva de madeiras na Amazônia estão limitados a estudos de casos em pequenas áreas comparados com a dimensão da região como um todo (GRECCHI et al, 2017) e, os espacialmente mais abrangentes, incluíram séries temporais limitadas (ASNER et al, 2005;MATRICARDI et al, 2013).…”