Correction of Interferometric and Vegetation Biases in the SRTMGL1 Spaceborne DEM with Hydrological Conditioning towards Improved Hydrodynamics Modeling in the Amazon Basin

Correction of Interferometric and Vegetation Biases in the SRTMGL1 Spaceborne DEM with Hydrological Conditioning towards Improved Hydrodynamics Modeling in the Amazon Basin

In the Amazon basin, the recently released SRTM Global 1 arc-second (SRTMGL1) remains the best topographic information for hydrological and hydrodynamic modeling purposes. However, its accuracy is hindered by errors, partly due to vegetation, leading to erroneous simulations. Previous efforts to rem...

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Bibliographic Details
Main Authors: Sebastien Pinel, Marie-Paule Bonnet, Joecila Santos Da Silva, Daniel Moreira, Stephane Calmant, Fredéric Satgé, Fredérique Seyler
Format: Article
Language:English
Published: MDPI AG 2015-12-01
Series:Remote Sensing
Subjects:
DEM
SRTMGL1
Amazon
vegetation
altimetry
floodplain
remote sensing
lake
bathymetry
Online Access:http://www.mdpi.com/2072-4292/7/12/15822

Internet

http://www.mdpi.com/2072-4292/7/12/15822

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