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Open AccessArticle

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

1
RHASA/ State of Amazonas University (UEA), Av. Darcy Vargas, 1200, Parque 10, 69050-020 Manaus, Brazil
2
Mixed Laboratory International, Observatory for Environmental Change (LMI-OCE), Institute of Research for Development(IRD)/University of Brasilia (UnB), Campus Darcy Ribeiro, 70910-900 Brasília, Brazil
3
UMR 5563 GET/Institute of Research for Development (IRD), 14 avenue Edouard Belin, 31400 Toulouse, France
4
Geological Survey of Brazil (CPRM), Av. Pasteur, 404, Urca, 22290-040 Rio de Janeiro, Brazil
5
UMR 5566 LEGOS/Institute of Research for Development (IRD), 14 avenue Edouard Belin, 31400 Toulouse, France
6
Institute of Geosciences (LAGEQ), University of Brasília (UnB), Campus Darcy Ribeiro, 70910-900 Brasília, Brazil
7
UMR 228 ESPACE-DEV/Institute of Research for Development (IRD), 500 rue JF Breton, 34093 Montpellier, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Guy J-P. Schumann, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2015, 7(12), 16108-16130; https://doi.org/10.3390/rs71215822
Received: 19 August 2015 / Revised: 13 November 2015 / Accepted: 19 November 2015 / Published: 2 December 2015
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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 remove the vegetation signal either did not account for its spatial variability or relied on a single assumed percentage of penetration of the SRTM signal. Here, we propose a systematic approach over an Amazonian floodplain to remove the vegetation signal, addressing its heterogeneity by combining estimates of vegetation height and a land cover map. We improve this approach by interpolating the first results with drainage network, field and altimetry data to obtain a hydrological conditioned DEM. The averaged interferometric and vegetation biases over the forest zone were found to be −2.0 m and 7.4 m, respectively. Comparing the original and corrected DEM, vertical validation against Ground Control Points shows a RMSE reduction of 64%. Flood extent accuracy, controlled against Landsat and JERS-1 images, stresses improvements in low and high water periods (+24% and +18%, respectively). This study also highlights that a ground truth drainage network, as a unique input during the interpolation, achieves reasonable results in terms of flood extent and hydrological characteristics. View Full-Text
Keywords: DEM; SRTMGL1; Amazon; vegetation; altimetry; floodplain; remote sensing; lake; bathymetry DEM; SRTMGL1; Amazon; vegetation; altimetry; floodplain; remote sensing; lake; bathymetry
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MDPI and ACS Style

Pinel, S.; Bonnet, M.-P.; Santos Da Silva, J.; Moreira, D.; Calmant, S.; Satgé, F.; Seyler, F. Correction of Interferometric and Vegetation Biases in the SRTMGL1 Spaceborne DEM with Hydrological Conditioning towards Improved Hydrodynamics Modeling in the Amazon Basin. Remote Sens. 2015, 7, 16108-16130.

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