A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland.
Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and prote...
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Online Access: | https://espace.inrs.ca/id/eprint/11209/ https://espace.inrs.ca/id/eprint/11209/1/P3829.pdf https://doi.org/10.1080/15481603.2020.1846948 |
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ftinrsquebec:oai:espace.inrs.ca:11209 2023-05-15T17:22:04+02:00 A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. Mahdianpari, Masoud Jafarzadeh, Hamid Granger, Jean Elizabeth Mohammadimanesh, Fariba Brisco, Brian Salehi, Bahram Homayouni, Saeid Weng, Qihao 2020 application/pdf https://espace.inrs.ca/id/eprint/11209/ https://espace.inrs.ca/id/eprint/11209/1/P3829.pdf https://doi.org/10.1080/15481603.2020.1846948 en eng https://espace.inrs.ca/id/eprint/11209/1/P3829.pdf Mahdianpari, Masoud, Jafarzadeh, Hamid, Granger, Jean Elizabeth, Mohammadimanesh, Fariba, Brisco, Brian, Salehi, Bahram, Homayouni, Saeid orcid:0000-0002-0214-5356 et Weng, Qihao (2020). A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. GIScience & Remote Sensing , vol. 57 , nº 8. p. 1102-1124. DOI:10.1080/15481603.2020.1846948 <https://doi.org/10.1080/15481603.2020.1846948>. doi:10.1080/15481603.2020.1846948 change detection wetlands remote sensing time series analysis geo big data Canada Article Évalué par les pairs 2020 ftinrsquebec https://doi.org/10.1080/15481603.2020.1846948 2023-02-10T11:46:32Z Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based geospatial platforms has allowed for the production of wetland maps at scales previously unfeasible due to technical limitations, yet the assessment of changes to wetlands at the level of the wetland class (bog, fen, swamp, and marsh) has yet to be implemented across Canada. Class-level change information is important when considering changes and impacts to wetland functions and services. To demonstrate this possibility, this study assessed 30 years of change to wetlands across the province of Newfoundland using Landsat imagery, spectral indices, and Random Forest classification within the Google Earth Engine (GEE) cloud-computing platform. Overall accuracies were high, ranging from 84.37% to 88.96%. In a comparison of different classifiers, Random Forest produced the highest over accuracy results and allowed for the estimation of variable importance, when compared Classification and Regression Tree (CART) and Minimum Distance (MD). The most important variables include the thermal infrared band (TIR), elevation, the difference vegetation index (DVI), the shortwave infrared bands (SWIR), and the normalized difference vegetation index (NDVI). Change detection analysis shows that bog, followed by swamp and fen, are the most common wetland classes across all time periods generally, and marsh wetlands are the least common wetland classes across all time periods respectively. The analysis also shows a general instability of wetland classes, though this is largely due to conversion from one wetland class to another. Future work may integrate RADAR data and consider weather patterns. The results of this study elucidate for the ... Article in Journal/Newspaper Newfoundland Institut national de la recherche scientifique, Québec: Espace INRS Canada GIScience & Remote Sensing 57 8 1102 1124 |
institution |
Open Polar |
collection |
Institut national de la recherche scientifique, Québec: Espace INRS |
op_collection_id |
ftinrsquebec |
language |
English |
topic |
change detection wetlands remote sensing time series analysis geo big data Canada |
spellingShingle |
change detection wetlands remote sensing time series analysis geo big data Canada Mahdianpari, Masoud Jafarzadeh, Hamid Granger, Jean Elizabeth Mohammadimanesh, Fariba Brisco, Brian Salehi, Bahram Homayouni, Saeid Weng, Qihao A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
topic_facet |
change detection wetlands remote sensing time series analysis geo big data Canada |
description |
Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based geospatial platforms has allowed for the production of wetland maps at scales previously unfeasible due to technical limitations, yet the assessment of changes to wetlands at the level of the wetland class (bog, fen, swamp, and marsh) has yet to be implemented across Canada. Class-level change information is important when considering changes and impacts to wetland functions and services. To demonstrate this possibility, this study assessed 30 years of change to wetlands across the province of Newfoundland using Landsat imagery, spectral indices, and Random Forest classification within the Google Earth Engine (GEE) cloud-computing platform. Overall accuracies were high, ranging from 84.37% to 88.96%. In a comparison of different classifiers, Random Forest produced the highest over accuracy results and allowed for the estimation of variable importance, when compared Classification and Regression Tree (CART) and Minimum Distance (MD). The most important variables include the thermal infrared band (TIR), elevation, the difference vegetation index (DVI), the shortwave infrared bands (SWIR), and the normalized difference vegetation index (NDVI). Change detection analysis shows that bog, followed by swamp and fen, are the most common wetland classes across all time periods generally, and marsh wetlands are the least common wetland classes across all time periods respectively. The analysis also shows a general instability of wetland classes, though this is largely due to conversion from one wetland class to another. Future work may integrate RADAR data and consider weather patterns. The results of this study elucidate for the ... |
format |
Article in Journal/Newspaper |
author |
Mahdianpari, Masoud Jafarzadeh, Hamid Granger, Jean Elizabeth Mohammadimanesh, Fariba Brisco, Brian Salehi, Bahram Homayouni, Saeid Weng, Qihao |
author_facet |
Mahdianpari, Masoud Jafarzadeh, Hamid Granger, Jean Elizabeth Mohammadimanesh, Fariba Brisco, Brian Salehi, Bahram Homayouni, Saeid Weng, Qihao |
author_sort |
Mahdianpari, Masoud |
title |
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
title_short |
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
title_full |
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
title_fullStr |
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
title_full_unstemmed |
A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. |
title_sort |
large-scale change monitoring of wetlands using time series landsat imagery on google earth engine: a case study in newfoundland. |
publishDate |
2020 |
url |
https://espace.inrs.ca/id/eprint/11209/ https://espace.inrs.ca/id/eprint/11209/1/P3829.pdf https://doi.org/10.1080/15481603.2020.1846948 |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_relation |
https://espace.inrs.ca/id/eprint/11209/1/P3829.pdf Mahdianpari, Masoud, Jafarzadeh, Hamid, Granger, Jean Elizabeth, Mohammadimanesh, Fariba, Brisco, Brian, Salehi, Bahram, Homayouni, Saeid orcid:0000-0002-0214-5356 et Weng, Qihao (2020). A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland. GIScience & Remote Sensing , vol. 57 , nº 8. p. 1102-1124. DOI:10.1080/15481603.2020.1846948 <https://doi.org/10.1080/15481603.2020.1846948>. doi:10.1080/15481603.2020.1846948 |
op_doi |
https://doi.org/10.1080/15481603.2020.1846948 |
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GIScience & Remote Sensing |
container_volume |
57 |
container_issue |
8 |
container_start_page |
1102 |
op_container_end_page |
1124 |
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