4.7 Article

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

Journal

ENVIRONMENTAL RESEARCH LETTERS
Volume 15, Issue 9, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1748-9326/aba3a4

Keywords

Brazil; forest degradation; google earth engine; Landsat; random forests; selective logging; tropical forest

Funding

  1. Natural Environment Research Council [PR140015]
  2. Grantham Centre for Sustainable Futures

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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 Rondonia, Brazil. Approximately one-quarter of the forested lands in Rondonia 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 km(2)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.

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