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Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Meisam Amani
  • Mohammad Kakooei
  • Arsalan Ghorbanian
  • Rebecca Warren
  • Armin Moghimi

External Research Organisations

  • Wood Environment & Infrastructure Solutions
  • Chalmers University of Technology
  • K.N. Toosi University of Technology
  • Lund University
  • Canada Center for Mapping and Earth Observation (CCMEO)
  • Michigan Technological University
  • Meteorological Service of Canada (ARQX)

Details

Original languageEnglish
Article number3778
JournalRemote sensing
Volume14
Issue number15
Publication statusPublished - 6 Aug 2022

Abstract

Wetlands provide many benefits, such as water storage, flood control, transformation and retention of chemicals, and habitat for many species of plants and animals. The ongoing degradation of wetlands in the Great Lakes basin has been caused by a number of factors, including climate change, urbanization, and agriculture. Mapping and monitoring wetlands across such large spatial and temporal scales have proved challenging; however, recent advancements in the accessibility and processing efficiency of remotely sensed imagery have facilitated these applications. In this study, the historical Landsat archive was first employed in Google Earth Engine (GEE) to classify wetlands (i.e., Bog, Fen, Swamp, Marsh) and non-wetlands (i.e., Open Water, Barren, Forest, Grassland/Shrubland, Cropland) throughout the entire Great Lakes basin over the past four decades. To this end, an object-based supervised Random Forest (RF) model was developed. All of the produced wetland maps had overall accuracies exceeding 84%, indicating the high capability of the developed classification model for wetland mapping. Changes in wetlands were subsequently assessed for 17 time intervals. It was observed that approximately 16% of the study area has changed since 1984, with the highest increase occurring in the Cropland class and the highest decrease occurring in the Forest and Marsh classes. Forest mostly transitioned to Fen, but was also observed to transition to Cropland, Marsh, and Swamp. A considerable amount of the Marsh class was also converted into Cropland.

Keywords

    big data, change detection, GEE, remote sensing, wetlands

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine. / Amani, Meisam; Kakooei, Mohammad; Ghorbanian, Arsalan et al.
In: Remote sensing, Vol. 14, No. 15, 3778, 06.08.2022.

Research output: Contribution to journalArticleResearchpeer review

Amani, M, Kakooei, M, Ghorbanian, A, Warren, R, Mahdavi, S, Brisco, B, Moghimi, A, Bourgeau-Chavez, L, Toure, S, Paudel, A, Sulaiman, A & Post, R 2022, 'Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine', Remote sensing, vol. 14, no. 15, 3778. https://doi.org/10.3390/rs14153778
Amani, M., Kakooei, M., Ghorbanian, A., Warren, R., Mahdavi, S., Brisco, B., Moghimi, A., Bourgeau-Chavez, L., Toure, S., Paudel, A., Sulaiman, A., & Post, R. (2022). Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine. Remote sensing, 14(15), Article 3778. https://doi.org/10.3390/rs14153778
Amani M, Kakooei M, Ghorbanian A, Warren R, Mahdavi S, Brisco B et al. Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine. Remote sensing. 2022 Aug 6;14(15):3778. doi: 10.3390/rs14153778
Amani, Meisam ; Kakooei, Mohammad ; Ghorbanian, Arsalan et al. / Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine. In: Remote sensing. 2022 ; Vol. 14, No. 15.
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AU - Amani, Meisam

AU - Kakooei, Mohammad

AU - Ghorbanian, Arsalan

AU - Warren, Rebecca

AU - Mahdavi, Sahel

AU - Brisco, Brian

AU - Moghimi, Armin

AU - Bourgeau-Chavez, Laura

AU - Toure, Souleymane

AU - Paudel, Ambika

AU - Sulaiman, Ablajan

AU - Post, Richard

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