title: A Review of Wetland Remote Sensing.
abstract: 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.
authors.affiliation: School of Geographical Science, Northeast Normal University, Changchun 130024, China. guom521@nenu.edu.cn.
authors.forename: Meng
authors.initials: M
authors.lastname: Guo
authors.affiliation: Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun 130102, China. lijingsara@iga.ac.cn.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun 130102, China. shengchunlei@iga.ac.cn.
authors.forename: Chunlei
authors.initials: C
authors.lastname: Sheng
authors.affiliation: School of Geographical Science, Northeast Normal University, Changchun 130024, China. xujw643@nenu.edu.cn.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Xu
authors.affiliation: Remote Sensing Technique Centre, Heilongjiang Academy of Agricultural Science, Harbin 150086, China. aromawu@163.com.
authors.forename: Li
authors.initials: L
authors.lastname: Wu
mesh_terms: D058998:Remote Sensing Technology; D053833:Wetlands
keywords: LiDAR; optical sensor; radar; remote sensing; wetland