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Sökning: WFRF:(Mahdavi Mohammad) > (2022) > Forty Years of Wetl...

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

Amani, Meisam (författare)
Wood Environment & Infrastructure Solutions Canada Limited
Kakooei, Mohammad, 1988 (författare)
Chalmers University of Technology
Ghorbanian, Arsalan (författare)
Lund University,Lunds universitet,Institutionen för teknik och samhälle,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Technology and Society,Departments at LTH,Faculty of Engineering, LTH,K. N. Toosi University of Technology
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Warren, Rebecca (författare)
Mahdavi, Sahel (författare)
Brisco, Brian (författare)
Moghimi, Armin (författare)
University of Hanover, Germany,Leibniz University of Hannover
Bourgeau-Chavez, Laura (författare)
Michigan Technological University
Toure, Souleymane (författare)
Environment and Climate Change Canada,Environment Canada
Paudel, Ambika (författare)
Environment and Climate Change Canada,Environment Canada
Sulaiman, Ablajan (författare)
Environment and Climate Change Canada,Environment Canada
Post, Richard (författare)
Environment and Climate Change Canada,Environment Canada
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 (creator_code:org_t)
2022-08-06
2022
Engelska.
Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:15
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)
NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

Nyckelord

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

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