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
相关论文
共 190 条
  • [21] Mapping boreal peatland ecosystem types from multitemporal radar and optical satellite imagery
    Bourgeau-Chavez, L. L.
    Endres, S.
    Powell, R.
    Battaglia, M. J.
    Benscoter, B.
    Turetsky, M.
    Kasischke, E. S.
    Banda, E.
    [J]. CANADIAN JOURNAL OF FOREST RESEARCH, 2017, 47 (04) : 545 - 559
  • [22] Mapping and classification of peatland on the Isle of Lewis using Landsat ETM
    Brown, E.
    Aitkenhead, M.
    Wright, R.
    Aalders, I. H.
    [J]. SCOTTISH GEOGRAPHICAL JOURNAL, 2007, 123 (03) : 173 - 192
  • [23] Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices
    Bwangoy, Jean-Robert B.
    Hansen, Matthew C.
    Roy, David P.
    De Grandi, Gianfranco
    Justice, Christopher O.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2010, 114 (01) : 73 - 86
  • [24] A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States
    Byrd, Kristin B.
    Ballanti, Laurel
    Thomas, Nathan
    Dung Nguyen
    Holmquist, James R.
    Simard, Marc
    Windham-Myers, Lisamarie
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 139 : 255 - 271
  • [25] Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data
    Cai, Yaotong
    Li, Xinyu
    Zhang, Meng
    Lin, Hui
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2020, 92
  • [26] Salt Marsh Monitoring in Jamaica Bay, New York from 2003 to 2013: A Decade of Change from Restoration to Hurricane Sandy
    Campbell, Anthony
    Wang, Yeqiao
    Christiano, Mark
    Stevens, Sara
    [J]. REMOTE SENSING, 2017, 9 (02):
  • [27] Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
    Campbell, Anthony Daniel
    Wang, Yeqiao
    [J]. PLOS ONE, 2020, 15 (02):
  • [28] Cao MC, 2007, INT GEOSCI REMOTE SE, P4585
  • [29] A decision-tree classification for low-lying complex land cover types within the zone of discontinuous permafrost
    Chasmer, L.
    Hopkinson, C.
    Veness, T.
    Quinton, W.
    Baltzer, J.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2014, 143 : 73 - 84
  • [30] Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change
    Chasmer, Laura
    Mahoney, Craig
    Millard, Koreen
    Nelson, Kailyn
    Peters, Daniel
    Merchant, Michael
    Hopkinson, Chris
    Brisco, Brian
    Niemann, Olaf
    Montgomery, Joshua
    Devito, Kevin
    Cobbaert, Danielle
    [J]. REMOTE SENSING, 2020, 12 (08)