A spatial pattern analysis of forest loss in the Madre de Dios region, Peru

Bibliographic Data

ID15551701
AuthorsAndréa Puzzi Nicolau (0000-0002-7529-2074, University of Alabama in Huntsville, corresponding author), Kelsey E Herndon (0000-0002-4771-6861, Marshall Space Flight Center), Kelsey Herndon, Africa I Flores-Anderson (0009-0008-2509-6439, University of Alabama in Huntsville), Africa Flores-Anderson, Robert Griffin (0000-0001-5665-700X, Marshall Space Flight Center)
Year2019
Volume14
Issue12
Pages124045-124045
Publication date2019-11-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab57c3
OpenAlexW2986519566
LanguageEN
Citations received6
References cited41

Over the past decades, the Peruvian Amazon has experienced a rapid change in forest cover due to the expansion of agriculture and extractive activities. This study uses spectral mixture analysis (SMA) in a cloud-computing platform to map forest loss within and outside indigenous territories, protected areas, mining concessions, and reforestation concessions within the Madre de Dios Region in Peru. The study area is focused on key areas of forest loss in the western part of the Tambopata National Reserve and surrounding the Malinowski River. Landsat 8 Operational Land Imager and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data spanning 2013–2018 were analyzed using cloud-based SMA to identify patterns of forest loss for each year. High-resolution Planet Dove (3m) and RapidEye (5m) imagery were used to validate the forest loss map and to identify the potential drivers of loss. Results show large areas of forest loss, especially within buffer zones of protected areas. Forest loss also appears in the Kotsimba Native Community within a 1 km buffer of the Malinowski River. In addition to gold mining, agriculture and pasture fields also appear to be major drivers of forest loss for our study period. This study also suggests that gold mining activity is potentially not restricted to the legal mining concession areas, with 49% of forest loss occurring outside the mining concessions. Overall accuracy obtained for the forest loss analysis was 96%. These results illustrate the applicability of a cloud-based platform not only for land use land cover change detection but also for accessing and processing large datasets; the importance of monitoring not only forest loss progression in the Madre de Dios, which has been increasing over the years, especially within buffer zones, but also its drivers; and reiterates the use of SMA as a reliable change detection classification approach

Amazon rainforest · Cloud forest · Deforestation (computer science · Forest cover · Geography · Land cover · Land use · Logging · Physical geography · Reforestation · Remote sensing · Satellite imagery · Thematic Mapper · Conservation, Biodiversity, and Resource Management · Environmental Science · Land Use and Ecosystem Services · Wildlife Ecology and Conservation · Ecology · Forestry

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