Depth Retrieval from A Reservoir Using A Conditional-Based Model

• Autor(a) principal: Nunes, Melina Brunelli [UNESP]
• Data de Publicação: 2020
• Outros Autores: Poz, Aluir Porfirio Dal [UNESP], Alcantara, Enner [UNESP], Curtarelli, Marcelo
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/LAGIRS48042.2020.9165636; http://hdl.handle.net/11449/221571
• topic: accuracy; Amazonian region; bathymetry; dam; Landsat-8; Lyzega; multispectral sensor

Water depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70's, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about -0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model.