共 190 条
Remote Sensing and Machine Learning Tools to Support Wetland Monitoring: A Meta-Analysis of Three Decades of Research
被引:19
作者:

Jafarzadeh, Hamid
论文数: 0 引用数: 0
h-index: 0
机构:
Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada

Mahdianpari, Masoud
论文数: 0 引用数: 0
h-index: 0
机构:
Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada
C CORE, St John, NL A1B 3X5, Canada Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada

Gill, Eric W.
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h-index: 0
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Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada

Brisco, Brian
论文数: 0 引用数: 0
h-index: 0
机构:
Canada Ctr Mapping & Earth Observat, Ottawa, ON K1S 5K2, Canada Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada

Mohammadimanesh, Fariba
论文数: 0 引用数: 0
h-index: 0
机构:
C CORE, St John, NL A1B 3X5, Canada Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada
机构:
[1] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NL A1B 3X5, Canada
[2] C CORE, St John, NL A1B 3X5, Canada
[3] Canada Ctr Mapping & Earth Observat, Ottawa, ON K1S 5K2, Canada
基金:
加拿大自然科学与工程研究理事会;
关键词:
wetlands;
remote sensing;
machine learning;
meta-analysis;
systematic review;
GOOGLE EARTH ENGINE;
RANDOM FOREST CLASSIFICATION;
LAND-COVER CLASSIFICATION;
MAPPING WETLANDS;
TIME-SERIES;
IMAGE CLASSIFICATION;
INUNDATION DYNAMICS;
COASTAL WETLANDS;
MULTIPLE SOURCES;
NEURAL-NETWORKS;
D O I:
10.3390/rs14236104
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation.
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