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

Remote Sensing

Sebastien Pinel¹  Marie-Paule Bonnet²  Joecila Santos Da Silva¹  Daniel Moreira⁴  Stephane Calmant²  Fredéric Satgé²  Fredérique Seyler²  Guy J-P. Schumann³  Magaly Koch³ 

[1] RHASA/ State of Amazonas University (UEA), Av. Darcy Vargas, 1200, Parque 10, 69050-020 Manaus, Brazil;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;;RHASA/ State of Amazonas University (UEA), Av. Darcy Vargas, 1200, Parque 10, 69050-020 Manaus, BrazilGeological Survey of Brazil (CPRM), Av. Pasteur, 404, Urca, 22290-040 Rio de Janeiro, Brazil;

关键词: DEM;    SRTMGL1;    Amazon;    vegetation;    altimetry;    floodplain;    remote sensing;    lake;    bathymetry;   

DOI  :  10.3390/rs71215822

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.