The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous resu...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftdoajarticles:oai:doaj.org/article:4a807a95ce1b478880407f92cfc633b7 2023-05-15T15:16:41+02:00 The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi Saeid Homayouni Laura Bourgeau-Chavez 2021-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2021.3105645 https://doaj.org/article/4a807a95ce1b478880407f92cfc633b7 EN eng IEEE https://ieeexplore.ieee.org/document/9523558/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3105645 https://doaj.org/article/4a807a95ce1b478880407f92cfc633b7 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8789-8803 (2021) Canada google earth engine multisource data random forest remote sensing satellite data Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2021 ftdoajarticles https://doi.org/10.1109/JSTARS.2021.3105645 2022-12-31T10:10:46Z Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Canada IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 8789 8803 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Canada google earth engine multisource data random forest remote sensing satellite data Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Canada google earth engine multisource data random forest remote sensing satellite data Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi Saeid Homayouni Laura Bourgeau-Chavez The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
topic_facet |
Canada google earth engine multisource data random forest remote sensing satellite data Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the ... |
format |
Article in Journal/Newspaper |
author |
Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi Saeid Homayouni Laura Bourgeau-Chavez |
author_facet |
Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi Saeid Homayouni Laura Bourgeau-Chavez |
author_sort |
Masoud Mahdianpari |
title |
The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
title_short |
The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
title_full |
The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
title_fullStr |
The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
title_full_unstemmed |
The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform |
title_sort |
third generation of pan-canadian wetland map at 10 m resolution using multisource earth observation data on cloud computing platform |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doi.org/10.1109/JSTARS.2021.3105645 https://doaj.org/article/4a807a95ce1b478880407f92cfc633b7 |
geographic |
Arctic Canada |
geographic_facet |
Arctic Canada |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8789-8803 (2021) |
op_relation |
https://ieeexplore.ieee.org/document/9523558/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3105645 https://doaj.org/article/4a807a95ce1b478880407f92cfc633b7 |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3105645 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
14 |
container_start_page |
8789 |
op_container_end_page |
8803 |
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1766346983537115136 |