Abstract
We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points.
Highlights
We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil
Ongoing deforestation in the tropics is a well-known threat to biodiversity [1,2,3]
The Xingu River basin encompasses the fourth largest catchment area of the Amazon. It is known for its high fish biodiversity, with an estimated 600 species, 10% of them endemic to this river basin with many remaining to be described [11,12,13,14]
Summary
Ongoing deforestation in the tropics is a well-known threat to biodiversity [1,2,3]. Species with restricted ranges are at risk of extinction from habitat loss [4]. Global- and regional-scale maps developed to analyze large-scale trends (e.g., [5,6,7,8]) do not necessarily capture the small-scale changes experienced by restricted range species These areas require the development of detailed land cover datasets that allow for a better understanding of historical and current environmental threats (e.g., deforestation, mining, etc.). In order to assess the rapid changes that the Xingu River basin has experienced over the last four decades, we developed a forest/non-forest classification system for four time periods from 1989 to 2018 as a case study applicable elsewhere in the tropics These datasets were produced from cloud free surface reflectance mosaics of Landsat. Data 2019, 4, x FOR PEER REVIEW reflectance mosaics of Landsat TM5 and Landsat 8-OLI images, classified with a classification and regression tree (CART) in Google Earth Engine
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