David Weekley The University of Kansas
Xingong Li The University of Kansas

22E

Water distribution, both spatially and temporally, are critical aspects of the environment with dramatic effects on ecology, economy, and human welfare. While water presence and surface area are often easy to detect using standard remote sensing techniques, water volume calculations require additional information such as water surface elevation traditionally acquired using in-situ gauge monitoring stations or space-based altimetry satellites, both of which are generally limited to larger lakes and reservoirs. This research uses Google Earth Engine to estimate long-term lake dynamics (surface elevation, surface area, volume, and volume change) for multiple Kansas reservoirs using Landsat imagery combined with Lidar and bathymetry data products. The accuracy of water surface elevation estimates was analyzed using a variety of water indices (NDWI, MNDWI, AWEI), segmentation thresholds, water boundaries, and statistics. Additionally, image contamination, such as cloud cover, shadow, snow, and ice, are identified in each image via the Pixel Quality Assurance band in the Landsat Surface Reflectance data product which serves to increase water surface elevation accuracy as well as provide increased temporal resolution by allowing the analysis of “imperfect” imagery. Water surface area, volume, and volume change were then calculated using elevation/surface area/volume relationships derived from the combined Lidar/bathymetry data providing 40+ years of lake dynamic data applicable to a wide-variety of fields and interests.

11:15 Estimating Long-Term Lake Dynamics with Landsat Imagery and Topography Datasets, David Weekley