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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422050/ https://www.ncbi.nlm.nih.gov/pubmed/28379174 http://dx.doi.org/10.3390/s17040777 |
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author | Guo, Meng Li, Jing Sheng, Chunlei Xu, Jiawei Wu, Li |
author_facet | Guo, Meng Li, Jing Sheng, Chunlei Xu, Jiawei Wu, Li |
author_sort | Guo, Meng |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5422050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54220502017-05-12 A Review of Wetland Remote Sensing Guo, Meng Li, Jing Sheng, Chunlei Xu, Jiawei Wu, Li Sensors (Basel) Review 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. MDPI 2017-04-05 /pmc/articles/PMC5422050/ /pubmed/28379174 http://dx.doi.org/10.3390/s17040777 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Guo, Meng Li, Jing Sheng, Chunlei Xu, Jiawei Wu, Li A Review of Wetland Remote Sensing |
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 | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422050/ https://www.ncbi.nlm.nih.gov/pubmed/28379174 http://dx.doi.org/10.3390/s17040777 |
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