A Review of Wetland Remote Sensing
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 dra...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/4/777 |
| _version_ | 1828137854780309504 |
|---|---|
| author | Meng Guo Jing Li Chunlei Sheng Jiawei Xu Li Wu |
| author_facet | Meng Guo Jing Li Chunlei Sheng Jiawei Xu Li Wu |
| author_sort | Meng Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:26:32Z |
| format | Article |
| id | doaj.art-8081c38967404c3083e32f0016279c32 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:26:32Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8081c38967404c3083e32f0016279c322022-12-22T04:09:36ZengMDPI AGSensors1424-82202017-04-0117477710.3390/s17040777s17040777A Review of Wetland Remote SensingMeng Guo0Jing Li1Chunlei Sheng2Jiawei Xu3Li Wu4School of Geographical Science, Northeast Normal University, Changchun 130024, ChinaNortheast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun 130102, ChinaNortheast Institute of Geography and Agricultural Ecology, Chinese Academy of Science, Changchun 130102, ChinaSchool of Geographical Science, Northeast Normal University, Changchun 130024, ChinaRemote Sensing Technique Centre, Heilongjiang Academy of Agricultural Science, Harbin 150086, ChinaWetlands 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.http://www.mdpi.com/1424-8220/17/4/777wetlandremote sensingoptical sensorradarLiDAR |
| spellingShingle | Meng Guo Jing Li Chunlei Sheng Jiawei Xu Li Wu A Review of Wetland Remote Sensing Sensors wetland remote sensing optical sensor radar LiDAR |
| title | A Review of Wetland Remote Sensing |
| title_full | A Review of Wetland Remote Sensing |
| title_fullStr | A Review of Wetland Remote Sensing |
| title_full_unstemmed | A Review of Wetland Remote Sensing |
| title_short | A Review of Wetland Remote Sensing |
| title_sort | review of wetland remote sensing |
| topic | wetland remote sensing optical sensor radar LiDAR |
| url | http://www.mdpi.com/1424-8220/17/4/777 |
| work_keys_str_mv | AT mengguo areviewofwetlandremotesensing AT jingli areviewofwetlandremotesensing AT chunleisheng areviewofwetlandremotesensing AT jiaweixu areviewofwetlandremotesensing AT liwu areviewofwetlandremotesensing AT mengguo reviewofwetlandremotesensing AT jingli reviewofwetlandremotesensing AT chunleisheng reviewofwetlandremotesensing AT jiaweixu reviewofwetlandremotesensing AT liwu reviewofwetlandremotesensing |