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Delimitation of water areas using remote sensing in Brazil’s semiarid region

Débora Natália Oliveira de Almeida,D. Araújo,Débora Rodrigues Soares,Francisco Marcelo de Alencar Maia,S. Montenegro,S. M. Santos,L. M. Oliveira

Published 2023 in Revista Brasileira de Ciências Ambientais

ABSTRACT

Remote sensing techniques are of fundamental importance to investigate the changes occurred in the terrestrial mosaic over the years and contribute to the decision-making by increasing efficient environmental and water management. This article aimed to detect, demarcate and quantify the hydric area of Poço da Cruz reservoir, located in Ibimirim, Pernambuco, semiarid region of Brazil, with modeling based on Landsat 8/OLI satellite multispectral images from 2015 to 2020, and to relate it with data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellites average rainfall. For this purpose, the Modified Normalized Difference Water Index (MNDWI) was modeled, being produced georeferenced theme maps and extracted only the pixels represented by positive spectral values, which represent water targets. The open-access software Quantum Geographic Information System (QGIS, version 2.18.16) was used for all stages of digital image processing and connection with complementary databases on the theme maps elaboration. In the results, changes in the spatial distribution of Poço da Cruz were evidenced and analyzed using precipitation data from the CHIRPS product, allowing a better understanding of the rainfall behavior in the region and its influence. The MNDWI was lined with the CHIRPS product, in which the spatial correlation between the rainy event and the water area’s delimitation is documented, especially in October 2017 (minimum values) and October 2020 (maximum values).

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Revista Brasileira de Ciências Ambientais

  • Publication date

    Unknown publication date

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.5327/z2176-94781524
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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