Mapping pervasive selective logging in the south-west Brazilian Amazon 2000-2019

Hethcoat, M. G., Carreiras, J. M. B., Edwards, D. P., Bryant, R. G., Peres, C. A. ORCID: and Quegan, S. (2020) Mapping pervasive selective logging in the south-west Brazilian Amazon 2000-2019. Environmental Research Letters, 15 (9). ISSN 1748-9318

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Tropical forests harbour the highest biodiversity on the planet and are essential to human livelihoods and the global economy. However continued loss and degradation of forested landscapes, coupled with a rapidly rising global population, is placing incredible pressure on forests globally. The United Nations has developed the Reducing Emissions from Deforestation and forest Degradation (REDD +) programme in response to the challenges facing tropical forests and in recognition of the role they can play in climate mitigation. REDD + requires consistent and reliable monitoring of forests, however, national-level methodologies for measuring degradation are often bespoke and, because of an inability to track degradation effectively, the majority of countries combine reporting for deforestation and forest degradation into a single value. Here, we extend a recent analysis that enabled the detection of selective logging at the scale of a logging concession to a regional-scale estimation of selective logging activities. We utilized logging records from across Brazil to train a supervised classification algorithm for detecting logged pixels in Landsat imagery then predicted the extent of logging over a 20 year period throughout Rondônia, Brazil. Approximately one-quarter of the forested lands in Rondônia were cleared between 2000 and 2019. We estimate that 11.0% of the forest area present in 2000 had been selectively logged by 2019, comprising >11 500 km2 of forest. In general, rates of selective logging were twice as high in the first decade relative to the last decade of the period. Our approach is a considerable advance in developing an operationalized selective logging monitoring system capable of detecting subtle forest disturbances over large spatial scales.

Item Type: Article
Uncontrolled Keywords: renewable energy, sustainability and the environment,environmental science(all),public health, environmental and occupational health,sdg 3 - good health and well-being,sdg 7 - affordable and clean energy,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/2100/2105
Faculty \ School: Faculty of Science > School of Environmental Sciences
University of East Anglia Research Groups/Centres > Theme - ClimateUEA
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 02 Oct 2020 23:55
Last Modified: 20 Mar 2023 14:49
DOI: 10.1088/1748-9326/aba3a4


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