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 [1 ]
Mahdianpari, Masoud [1 ,2 ]
Gill, Eric W. [1 ]
Brisco, Brian [3 ]
Mohammadimanesh, Fariba [2 ]
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.
引用
收藏
页数:38
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  • [41] The RADARSAT Constellation Mission Core Applications: First Results
    Dabboor, Mohammed
    Olthof, Ian
    Mahdianpari, Masoud
    Mohammadimanesh, Fariba
    Shokr, Mohammed
    Brisco, Brian
    Homayouni, Saeid
    [J]. REMOTE SENSING, 2022, 14 (02)
  • [42] Wetland monitoring using classification trees and SPOT-5 seasonal time series
    Davranche, Aurelie
    Lefebvre, Gaetan
    Poulin, Brigitte
    [J]. REMOTE SENSING OF ENVIRONMENT, 2010, 114 (03) : 552 - 562
  • [43] Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada
    DeLancey, Evan R.
    Simms, John F.
    Mahdianpari, Masoud
    Brisco, Brian
    Mahoney, Craig
    Kariyeva, Jahan
    [J]. REMOTE SENSING, 2020, 12 (01)
  • [44] Monitoring Hydro Temporal Variability in Alberta, Canada with Multi-Temporal Sentinel-1 SAR Data
    DeLancey, Evan R.
    Kariyeva, Jahan
    Cranston, Jerome
    Brisco, Brian
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2018, 44 (01) : 1 - 10
  • [45] Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning
    Delancey, Evan Ross
    Kariyeva, Jahan
    Bried, Jason T.
    Hird, Jennifer N.
    [J]. PLOS ONE, 2019, 14 (06):
  • [46] Tropical Forest and Wetland Losses and the Role of Protected Areas in Northwestern Belize, Revealed from Landsat and Machine Learning
    Doyle, Colin
    Beach, Timothy
    Luzzadder-Beach, Sheryl
    [J]. REMOTE SENSING, 2021, 13 (03) : 1 - 22
  • [47] Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China
    Dronova, Iryna
    Gong, Peng
    Wang, Lin
    [J]. REMOTE SENSING OF ENVIRONMENT, 2011, 115 (12) : 3220 - 3236
  • [48] Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data
    Dubeau, Pierre
    King, Douglas J.
    Unbushe, Dikaso Gojamme
    Rebelo, Lisa-Maria
    [J]. REMOTE SENSING, 2017, 9 (10)
  • [49] Monitoring of wetland changes affected by drought using four Landsat satellite data and Fuzzy ARTMAP classification method (case study Hamoun wetland, Iran)
    Amir Houshang Ehsani
    Mojtaba Shakeryari
    [J]. Arabian Journal of Geosciences, 2021, 14 (14)
  • [50] Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine
    Farda, N. M.
    [J]. 5TH GEOINFORMATION SCIENCE SYMPOSIUM 2017 (GSS 2017), 2017, 98