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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 LU ; Warren, Rebecca ; Mahdavi, Sahel ; Brisco, Brian ; Moghimi, Armin ; Bourgeau-Chavez, Laura ; Toure, Souleymane and Paudel, Ambika , et al. (2022) In Remote Sensing 14(15).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
big data, change detection, GEE, remote sensing, wetlands
in
Remote Sensing
volume
14
issue
15
article number
3778
publisher
MDPI AG
external identifiers
  • scopus:85137074835
ISSN
2072-4292
DOI
10.3390/rs14153778
language
English
LU publication?
yes
id
e5ccf2ef-df0c-4437-b884-ae41afda3580
date added to LUP
2022-11-09 11:56:22
date last changed
2023-02-12 05:56:16
@article{e5ccf2ef-df0c-4437-b884-ae41afda3580,
  abstract     = {{<p>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.</p>}},
  author       = {{Amani, Meisam and Kakooei, Mohammad and Ghorbanian, Arsalan and Warren, Rebecca and Mahdavi, Sahel and Brisco, Brian and Moghimi, Armin and Bourgeau-Chavez, Laura and Toure, Souleymane and Paudel, Ambika and Sulaiman, Ablajan and Post, Richard}},
  issn         = {{2072-4292}},
  keywords     = {{big data; change detection; GEE; remote sensing; wetlands}},
  language     = {{eng}},
  number       = {{15}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine}},
  url          = {{http://dx.doi.org/10.3390/rs14153778}},
  doi          = {{10.3390/rs14153778}},
  volume       = {{14}},
  year         = {{2022}},
}