Topic: Recent Global Lake Change Patterns Revealed from Remotely Sensed Big Data
Speaker: Prof. SHENG Yongwei, University of California, Los Angeles (UCLA)
Brief Introduction to the Report:
Lakes store a major portion of liquid freshwater on the Earth’s surface, representing an irreplaceable resource to domestic, agricultural, and industrial water supplies. They are dynamic and complex aquatic ecosystems, significant sources of food and recreation, and a source of water vapor and trace gases to the atmosphere. Changes in the extent of lakes thus have significant ramifications for the hydrology, ecology, carbon cycle, exchange of energy and greenhouse gases with the atmosphere, and human use.
Understanding their distributions and change trends at regional and global scales is of crucial social and ecological importance.Remote sensing offers the only feasible approach for systematic lake mapping and inventory at the global scale. This presentation introduces their recently produced circa-2000 and circa-2015 high-resolution global lake databases from remote sensing in order to provide a comprehensive assessment of decadal lake dynamics.
Nearly nine-million lakes have been delineated using the big-data archive of Landsat scenes acquired during “lake-stable” seasons, and the mapped lake extents have gone through intensive quality assurance and quality control (QA/QC) procedures. Lake geographic and size distributions for each continent will be discussed.
The detected lake changes exhibit strongly heterogeneous spatial patterns that can be explained by regional climatic changes together with localized glacier variations and human disturbance activities. The observed 15-year lake changes provide a valuable baseline for lake dynamics studies, and a number of lake change hotspots have been identified for intensive filed observation, further investigation, and long-term monitoring.
Time: 9:30am Sep. 12, 2019
Venue: Room A0212, IGSNRR
Host: Prof. SU Fenzhen, State Key Laboratory of Resources and Environmental Information System