4.7 Article

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

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SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-021-97784-5

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  1. Coordination for the Improvement of Higher Education Personnel (CAPES)
  2. Federal University of Ceara (UFC)
  3. Brazilian Agricultural Research Corporation (EMBRAPA)

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This study utilized remote sensing to analyze spectral responses and land cover change in fragments of Caatinga biome, showing higher SRB in infrared band and lower SRB in red and blue bands in Ouricuri. The classification model had high accuracy in detecting land cover changes in Ouricuri and Taua, indicating its effectiveness in identifying areas most affected by degradation and generating political support for conservation efforts in the Caatinga Biome.
This study aimed to analyze fragments of rangelands through spectral responses and land cover change by livestock in regions of the Caatinga biome through remote sensing. For spectral behavior, the surface reflectance bidirectional (SRB) and spectral indexes of vegetation were used to verify the ragelands seasonality. Land cover change detection of Ouricuri and Taua through Landsat-8 images with a 16-day revisit interval, were processed in the Google Earth Engine platform (GEE) and software Quantum GIS version 2.18 (QGIS). In the GEE platform, annual mosaics and stacking of the spectral bands were generated for the classification of images, and in sequence the production of thematic maps in QGIS. The analysis of land cover change considered the classes: thinned Caatinga, conserved Caatinga, herbaceous vegetation, bare soil, water and others. The analysis of the spectral responses showed that the vegetation monitored in Ouricuri presented higher SRB in the infrared band and lower SRB in the red and blue bands, and that caused the pasture to produce higher vegetation indexes than the other locations. Through validation, it was observed that in Taua, there was an overall accuracy of 91% and Kappa index of 89%, and in Ouricuri there was an overall accuracy of 90% and Kappa index of 86%, indicating excellent correctness of the classification model. The classification model proved to be effective in verifying the temporal and spatial land cover change, making it possible to identify places with the vegetation that was most affected and susceptible to degradation and generation of political support to minimize damage to the Caatinga Biome.

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