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

被引:1
|
作者
Sun, Weiwei [1 ]
Yang, Gang [1 ,2 ]
Huang, Yuling [3 ]
Mao, Dehua [4 ]
Huang, Ke [5 ]
Zhu, Lin [1 ]
Meng, Xiangchao [5 ]
Feng, Tian [1 ]
Chen, Chao [6 ]
Ge, Yong [2 ]
机构
[1] Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China
[2] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
[3] Nat Resources & Planning Bur, Shanghai 200100, Peoples R China
[4] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China
[5] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China
[6] Suzhou Univ Sci & Technol, Sch Geog Sci & Geomat Engn, Suzhou 215009, Peoples R China
基金
中国国家自然科学基金;
关键词
Wetland mapping; Google Earth Engine; Sentinel imagery; South America; DIFFERENCE WATER INDEX; NDWI; RED; SUITABILITY; VALIDATION; RESERVOIRS; CHINA;
D O I
10.1007/s11430-023-1366-8
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页码:3252 / 3269
页数:18
相关论文
共 50 条
  • [1] First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data
    Weiwei SUN
    Gang YANG
    Yuling HUANG
    Dehua MAO
    Ke HUANG
    Lin ZHU
    Xiangchao MENG
    Tian FENG
    Chao CHEN
    Yong GE
    ScienceChinaEarthSciences, 2024, 67 (10) : 3252 - 3269
  • [2] Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform
    Ji, Hanyu
    Li, Xing
    Wei, Xinchun
    Liu, Wei
    Zhang, Lianpeng
    Wang, Lijuan
    REMOTE SENSING, 2020, 12 (17) : 1 - 23
  • [3] Wetlands mapping in typical regions of South America with multi-source and multi-feature integration
    Huang Y.
    Yang G.
    Sun W.
    Zhu L.
    Huang K.
    Meng X.
    National Remote Sensing Bulletin, 2023, 27 (06) : 6 - 25
  • [4] Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection
    He, Jinjie
    Wang, Chang
    Han, Ying
    Zhang, Wen
    Wang, Xu
    Li, Yuxiang
    Guo, Li
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (18) : 6624 - 6650
  • [5] Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine
    Li, Qingyu
    Qiu, Chunping
    Ma, Lei
    Schmitt, Michael
    Zhu, Xiao Xiang
    REMOTE SENSING, 2020, 12 (04)
  • [6] Wetland mapping in the Balqash Lake Basin Using Multi-source Remote Sensing Data and Topographic features Synergic Retrieval
    Zhu, Changming
    Luo, Jiancheng
    Shen, Zhanfeng
    Huang, Chudong
    2011 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY ESIAT 2011, VOL 10, PT C, 2011, 10 : 2718 - 2724
  • [7] MAPPING AERODYNAMIC ROUGHNESS LENGTH WITH MULTI-SOURCE REMOTE SENSING DATA
    Hu, Deyong
    Cao, Shisong
    Chen, Shanshan
    Feng, Nan
    2016 4rth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 2016,
  • [8] Integrating multi-source remote sensing data for soil mapping in Victoria
    Abuzar, M
    Ryan, S
    IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 2495 - 2497
  • [9] The high spatial resolution remote sensing image classification based on SVM with the multi-source data
    Zhang, JS
    Pan, YZ
    He, CY
    Li, J
    IGARSS 2005: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, PROCEEDINGS, 2005, : 3818 - 3821
  • [10] Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing
    Wang, Nan
    Chen, Songchao
    Huang, Jingyi
    Frappart, Frederic
    Taghizadeh, Ruhollah
    Zhang, Xianglin
    Wigneron, Jean-Pierre
    Xue, Jie
    Xiao, Yi
    Peng, Jie
    Shi, Zhou
    JOURNAL OF REMOTE SENSING, 2024, 4