Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 9254004 |
| Seiten (von - bis) | 32-52 |
| Seitenumfang | 21 |
| Fachzeitschrift | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Jahrgang | 14 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Extern publiziert | Ja |
Abstract
The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
- Erdkunde und Planetologie (insg.)
- Atmosphärenwissenschaften
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in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Jahrgang 14, 9254004, 2021, S. 32-52.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources
T2 - Challenges of Large-Scale Wetland Classification Using Remote Sensing
AU - Amani, Meisam
AU - Brisco, Brian
AU - Mahdavi, Sahel
AU - Ghorbanian, Arsalan
AU - Moghimi, Armin
AU - Delancey, Evan R.
AU - Merchant, Michael
AU - Jahncke, Raymond
AU - Fedorchuk, Lee
AU - Mui, Amy
AU - Fisette, Thierry
AU - Kakooei, Mohammad
AU - Ahmadi, Seyed Ali
AU - Leblon, Brigitte
AU - Larocque, Armand
N1 - Funding Information: This work was supported in part by the Natural Resources Canada under a Grant to Meisam Amani and in part by the Canada Centre for Mapping and Earth Observation of Natural Resources Canada. Publisher Copyright: © 2008-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
AB - The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
KW - Big data
KW - Canada
KW - Google Earth Engine
KW - Landsat
KW - remote sensing (RS)
KW - wetlands
UR - http://www.scopus.com/inward/record.url?scp=85098761839&partnerID=8YFLogxK
U2 - 10.1109/jstars.2020.3036802
DO - 10.1109/jstars.2020.3036802
M3 - Article
AN - SCOPUS:85098761839
VL - 14
SP - 32
EP - 52
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
SN - 1939-1404
M1 - 9254004
ER -