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Monitoring of artificial water reservoirs in the Southern Brazilian Amazon with remote sensing data

Damien Arvor 1 Felipe Daher 1 Thomas Corpetti 1 Marianne Laslier 1 Vincent Dubreuil 1
1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
Abstract : The agricultural expansion in the Southern Brazilian Amazon has long been pointed out due to its severe impacts on tropical forests. But the last decade has been marked by a rapid agricultural transition which enabled to reduce pressure on forests through (i) the adoption of intensive agricultural practices and (ii) the diversification of activities. However, we suggest that this new agricultural model implies new pressures on environment and especially on water resources since many artificial water reservoirs have been built to ensure crop irrigation, generate energy, farm fishes, enable access to water for cattle or just for leisure. In this paper, we implemented a method to automatically map artificial water reservoirs based on time series of Landsat images. The method was tested in the county of Sorriso (State of Mato Grosso, Brazil) where we identified 521 water reservoirs by visual inspection on very high resolution images. 68 Landsat-8 images covering 4 scenes in 2015 were pre-classified and a final class (Terrestrial or Aquatic) was determined for each pixel based on a Dempster-Shafer fusion approach. Results confirmed the potential of the methodology to automatically and efficiently detect water reservoirs in the study area (overall accuracy = 0.952 and Kappa index = 0.904) although the methodology underestimates the total area in water bodies because of the spatial resolution of Landsat images. In the case of Sorriso, we mapped 19.4 km 2 of the 20.8 km 2 of water reservoirs initially delimited by visual interpretation, i.e. we underestimated the area by 5.9%.
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Damien Arvor, Felipe Daher, Thomas Corpetti, Marianne Laslier, Vincent Dubreuil. Monitoring of artificial water reservoirs in the Southern Brazilian Amazon with remote sensing data. SPIE 2016, Sep 2016, Edimbourg, United Kingdom. ⟨10.1117/12.2241905⟩. ⟨halshs-01518623⟩



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