首页|First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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Wetland degradation has been accelerating in recent years globally.Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management.Despite being the world's fourth largest continent,South America has limited research on wetland mapping,and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region.To address this issue,we used Sentinel-1,Sentinel-2 and SRTM data,developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features,polarization features and shape features for South American wetlands.We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE)platform.Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24％and a kappa coefficient of 0.8649,while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23％and a kappa coefficient of 0.9368.The results show that the total area of existing wetlands in South America is approximately 1,737,000 km2,which is 6.8％of the total land area.Among the ten wetland categories,shallow sea had the largest area(960,527.4 km2),while aquaculture ponds had the smallest area 1513.6 km2.Swamp had the second largest area(306,240.1 km2).Brazil,Argentina,Venezuela,Bolivia,and Colombia were found to have the largest wetland areas,with Brazil and Colombia having the most diverse wetland categories.This product can serve as baseline data for subsequent monitoring,management,and conservation of South American wetlands.

Wetland mappingGoogle Earth EngineSentinel imagerySouth America

Weiwei SUN、Gang YANG、Yuling HUANG、Dehua MAO、Ke HUANG、Lin ZHU、Xiangchao MENG、Tian FENG、Chao CHEN、Yong GE

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Department of Geography and Spatial Information Techniques,Ningbo University,Ningbo 315211,China

State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences & Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

Natural Resources and Planning Bureau,Shanghai Pudong New District,Shanghai 200100,China

Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

School of Geography Science and Geomatics Engineering,Suzhou University of Science and Technology,Suzhou 215009,China

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2024

10.1007/s11430-023-1366-8

中国科学:地球科学(英文版)
中国科学院

中国科学:地球科学(英文版)

影响因子：1.002
ISSN：1674-7313
年,卷(期)：2024.67(10)