Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada
Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water fi...
Published in: | Journal of Environmental Management |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Elsevier
2020
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Online Access: | https://doi.org/10.1016/j.jenvman.2020.111676 https://nrc-publications.canada.ca/eng/view/object/?id=51471463-2621-4423-b651-38b005d0d714 https://nrc-publications.canada.ca/fra/voir/objet/?id=51471463-2621-4423-b651-38b005d0d714 |
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ftnrccanada:oai:cisti-icist.nrc-cnrc.ca:cistinparc:51471463-2621-4423-b651-38b005d0d714 2023-06-11T04:14:11+02:00 Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada Mahdianpari, Masoud Granger, Jean Elizabeth Mohammadimanesh, Fariba Warren, Sherry Puestow, Thomas Salehi, Bahram Brisco, Brian 2020-11-24 text https://doi.org/10.1016/j.jenvman.2020.111676 https://nrc-publications.canada.ca/eng/view/object/?id=51471463-2621-4423-b651-38b005d0d714 https://nrc-publications.canada.ca/fra/voir/objet/?id=51471463-2621-4423-b651-38b005d0d714 eng eng Elsevier issn:03014797 Journal of Environmental Management, Volume: 280, Issue: C, Publication date: 2020-11-24 doi:10.1016/j.jenvman.2020.111676 pii:S0301479720316017 wetland city remote sensing vhr imagery lidarimage classification object-based random forest article 2020 ftnrccanada https://doi.org/10.1016/j.jenvman.2020.111676 2023-04-29T23:01:34Z Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using time- and cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's. Peer reviewed: Yes NRC publication: Yes Article in Journal/Newspaper Newfoundland National Research Council Canada: NRC Publications Archive Canada Newfoundland Journal of Environmental Management 280 111676 |
institution |
Open Polar |
collection |
National Research Council Canada: NRC Publications Archive |
op_collection_id |
ftnrccanada |
language |
English |
topic |
wetland city remote sensing vhr imagery lidarimage classification object-based random forest |
spellingShingle |
wetland city remote sensing vhr imagery lidarimage classification object-based random forest Mahdianpari, Masoud Granger, Jean Elizabeth Mohammadimanesh, Fariba Warren, Sherry Puestow, Thomas Salehi, Bahram Brisco, Brian Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
topic_facet |
wetland city remote sensing vhr imagery lidarimage classification object-based random forest |
description |
Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using time- and cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's. Peer reviewed: Yes NRC publication: Yes |
format |
Article in Journal/Newspaper |
author |
Mahdianpari, Masoud Granger, Jean Elizabeth Mohammadimanesh, Fariba Warren, Sherry Puestow, Thomas Salehi, Bahram Brisco, Brian |
author_facet |
Mahdianpari, Masoud Granger, Jean Elizabeth Mohammadimanesh, Fariba Warren, Sherry Puestow, Thomas Salehi, Bahram Brisco, Brian |
author_sort |
Mahdianpari, Masoud |
title |
Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
title_short |
Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
title_full |
Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
title_fullStr |
Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
title_full_unstemmed |
Smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada |
title_sort |
smart solutions for smart cities: urban wetland mapping using very-high resolution satellite imagery and airborne lidar data in the city of st. john's, nl, canada |
publisher |
Elsevier |
publishDate |
2020 |
url |
https://doi.org/10.1016/j.jenvman.2020.111676 https://nrc-publications.canada.ca/eng/view/object/?id=51471463-2621-4423-b651-38b005d0d714 https://nrc-publications.canada.ca/fra/voir/objet/?id=51471463-2621-4423-b651-38b005d0d714 |
geographic |
Canada Newfoundland |
geographic_facet |
Canada Newfoundland |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_relation |
issn:03014797 Journal of Environmental Management, Volume: 280, Issue: C, Publication date: 2020-11-24 doi:10.1016/j.jenvman.2020.111676 pii:S0301479720316017 |
op_doi |
https://doi.org/10.1016/j.jenvman.2020.111676 |
container_title |
Journal of Environmental Management |
container_volume |
280 |
container_start_page |
111676 |
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1768392020248756224 |