A Review of Wetland Remote Sensing

被引:302
|
作者
Guo, Meng [1 ]
Li, Jing [2 ]
Sheng, Chunlei [2 ]
Xu, Jiawei [1 ]
Wu, Li [3 ]
机构
[1] Northeast Normal Univ, Sch Geog Sci, Changchun 130024, Peoples R China
[2] Chinese Acad Sci, Northeast Inst Geog & Agr Ecol, Changchun 130102, Peoples R China
[3] Heilongjiang Acad Agr Sci, Remote Sensing Tech Ctr, Harbin 150086, Peoples R China
基金
中国国家自然科学基金;
关键词
wetland; remote sensing; optical sensor; radar; LiDAR; SYNTHETIC-APERTURE RADAR; LAND-COVER CLASSIFICATION; SALT-MARSH VEGETATION; RICE PLANTING AREA; MADRE-DE-DIOS; TIME-SERIES; HYPERSPECTRAL DATA; SATELLITE DATA; SURFACE-WATER; SPATIAL-RESOLUTION;
D O I
10.3390/s17040777
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.
引用
收藏
页数:36
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