Towards High-Resolution Lake Bathymetry: an Algorithm tested using Data Collected by the ICESat-2 Airborne Simulator over Lake Mead
Abstract
Precise lake bathymetry is essential for various studies involving many aspects of hydrological cycles, biogeochemical processes, and water resource management. A novel algorithm was developed for deriving Lake Mead's bathymetry by combining Landsat-based water classifications with ICESat-2 (Ice, Cloud & Land Elevation Satellite 2) prototype airborne-observed elevation. First, an unsupervised classification method, ISODATA (Iterative Self-Organizing Data Analysis Techniques Algorithm), was adopted to extract the lake area from 410 Landsat images during the period from 1982 to 2017, and then a water occurrence percentile image was generated. Next, the occurrence percentile image was paired with airborne-observed elevation values to establish an Area-Elevation (A-E) relationship, which in turn was applied to the classification contour map to obtain the bathymetry. Finally, the Lake Mead bathymetry image was embedded onto the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to replace the constant values of waters. Validations against survey LiDAR elevation (with average R2 of 0.95 and average RMSE of 1.86 m) and storage values (with R2 of 1 and RMSE of 0.10 km3) indicate that the bathymetry derived from this study is reliable. This algorithm has the potential for generating a global dataset of lake bathymetry values when ICESat-2 data become available.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H43G2508L
- Keywords:
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- 1817 Extreme events;
- HYDROLOGYDE: 1855 Remote sensing;
- HYDROLOGYDE: 1880 Water management;
- HYDROLOGYDE: 1895 Instruments and techniques: monitoring;
- HYDROLOGY