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A Novel Method for Mapping Lake Bottom Topography Using the GSW Dataset and Measured Water Level.

Authors :
Li, Yuanxi
Yang, Wei
Li, Junjie
Zhang, Zhen
Meng, Lingkui
Source :
Remote Sensing; Mar2022, Vol. 14 Issue 6, p1423, 21p
Publication Year :
2022

Abstract

Lake bottom topography is a basic parameter that reflects the elevation of all lake bottom geographical locations. In this study, a novel method was proposed for mapping lake bottom topography by combining the water occurrence map from the Global Surface Water (GSW) dataset with long-term measured water levels. This method took advantage of the following feature: the rapid change in water level of a lake's dynamic inundation area leads to a different water occurrence frequency and, therefore, put forward the concept of lake water level frequency, which refers to the frequency at which the water level is higher than or equal to a specified elevation. As water occurs more frequently in lake bottoms with lower elevations and less frequently in lake bottoms with higher elevations, we assume that lake water level frequency is identical to the water occurrence frequency over a long time. The water level frequency curve of all the measured water level data was generated through the P-III distribution function, and the elevation values from the water level frequency curve were assigned to pixels with the same frequency in the water occurrence map in order to generate the lake bottom topographic map. A case study was conducted on Poyang Lake in China to demonstrate the performance of the method. The derived bottom topographic map of Poyang Lake was verified by four measured sections. The results showed that the proposed method was feasible and could well reflect the bottom topography of Poyang Lake. The absolute error was mostly less than 0.5 m, the mean relative error was 7.4%, and the root mean square error was 0.99 m. The proposed method enriches the mapping means of lake bottom topography and has the potential to become a useful tool with a broad application prospect. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
6
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
156094655
Full Text :
https://doi.org/10.3390/rs14061423