High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

Margaret Kalacska, Oliver Lucanus, Leandro Sousa and J. Pablo Arroyo-Mora
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Margaret Kalacska: Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
Oliver Lucanus: Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
Leandro Sousa: Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira, PA 68372-040, Brazil
J. Pablo Arroyo-Mora: Flight Research Lab, National Research Council, Ottawa, ON K1A-0R6, Canada

Data, 2020, vol. 5, issue 3, 1-12

Abstract: We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.

Keywords: Altamira; endemic; freshwater fish; land cover change; PlanetScope; RapidEye; reservoir; Worldview 1; Hypancistrus zebra; dam (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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