High-resolution mapping of floodplain topography from space: A case study in the Amazon


Terrain elevation is essential for land management, navigation, and earth science applications. Remote sensing advancements have led to an increase in the availability of a range of digital elevation models with global to quasi-global land coverage. However, the generation of these models in water bodies requires specialized approaches, such as the delimitation of the shorelines (isobaths) of lakes over time. Therefore, the processing costs are high in complex areas with many lakes. Currently, there is no systematic topographic mapping of lakes and channels in large and complex floodplains using remote sensing data. We present here the first high-resolution topographic mapping (30 m) of the non-forested portion of the middle-lower Amazon floodplain using a new method based on in-situ Amazon river water levels and a flood-frequency map derived from the Landsat Global Surface Water Dataset. Validation using locally derived bathymetry showed a root mean square error (RMSE) of 0.89 m for floodplain elevation and a good representation of spatial patterns with Pearson’s correlation coefficient of 0.77. Our approach for improving topographic representation in open water areas is an alternative to SRTM3 DEM or MERIT DEM, which represents these areas as a flat surface. We also generated the Amazon River bathymetry using nautical charts from the Brazilian Navy (average RMSE of 7.5 m and bias of 5 m), and floodplain depths maps corresponding to the high- and low-water periods of the river flood wave. The results show that the storage volume in the open-water floodplain varies 104.3 km3 on average each year (from 11.9 km3 in low-water to 116.2 km3 in high-water). The method can be applied to any temporarily flooded area to provide the often missing underwater digital topographic data required for hydrological, ecological, and geomorphological studies. The data set developed in this study can be found at https://doi-org.ez61.periodicos.capes.gov.br/10.17632/vn599y9szb.1.

Remote Sensing of Environment