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  5. Spatiotemporal-based automated inundation mapping of Ramsar wetlands using Google Earth Engine

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Article
English
2023

Spatiotemporal-based automated inundation mapping of Ramsar wetlands using Google Earth Engine

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English
2023
Scientific Reports
Vol 13 (1)
DOI: 10.1038/s41598-023-43910-4

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Manish Kumar Goyal
Manish Kumar Goyal

Indian Institute Of Technology Indorethe Institution

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Manish Kumar Goyal
Shivukumar Rakkasagi
Soumya Shaga
+3 more

Abstract

Wetlands are one of the most critical components of an ecosystem, supporting many ecological niches and a rich diversity of flora and fauna. The ecological significance of these sites makes it imperative to study the changes in their inundation extent and propose necessary measures for their conservation. This study analyzes all 64 Ramsar sites in China based on their inundation patterns using Landsat imagery from 1991 to 2020. Annual composites were generated using the short-wave infrared thresholding technique from June to September to create inundation maps. The analysis was carried out on each Ramsar site individually to account for its typical behavior due to regional geographical and climatic conditions. The results of the inundation analysis for each site were subjected to the Mann-Kendall test to determine their trends. The analysis showed that 8 sites exhibited a significantly decreasing trend, while 14 sites displayed a significantly increasing trend. The accuracy of the analysis ranged from a minimum of 72.0% for Hubei Wang Lake to a maximum of 98.0% for Zhangye Heihe Wetland National Nature Reserve. The average overall accuracy of the sites was found to be 90.0%. The findings emphasize the necessity for conservation strategies and policies for Ramsar sites.

How to cite this publication

Manish Kumar Goyal, Shivukumar Rakkasagi, Soumya Shaga, Tian C. Zhang, Xianhui Li, Saket Dubey (2023). Spatiotemporal-based automated inundation mapping of Ramsar wetlands using Google Earth Engine. Scientific Reports, 13(1), DOI: 10.1038/s41598-023-43910-4.

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Publication Details

Type

Article

Year

2023

Authors

6

Datasets

0

Total Files

0

Language

English

Journal

Scientific Reports

DOI

10.1038/s41598-023-43910-4

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