The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform
Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapp...
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ftmdpi:oai:mdpi.com:/2072-4292/11/1/43/ 2023-08-20T04:08:04+02:00 The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh Saeid Homayouni Eric Gill agris 2018-12-28 application/pdf https://doi.org/10.3390/rs11010043 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs11010043 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 1; Pages: 43 wetland Google Earth Engine Sentinel-1 Sentinel-2 random forest cloud computing geo-big data Text 2018 ftmdpi https://doi.org/10.3390/rs11010043 2023-07-31T21:56:10Z Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies ... Text Newfoundland MDPI Open Access Publishing Remote Sensing 11 1 43 |
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Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
wetland Google Earth Engine Sentinel-1 Sentinel-2 random forest cloud computing geo-big data |
spellingShingle |
wetland Google Earth Engine Sentinel-1 Sentinel-2 random forest cloud computing geo-big data Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh Saeid Homayouni Eric Gill The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
topic_facet |
wetland Google Earth Engine Sentinel-1 Sentinel-2 random forest cloud computing geo-big data |
description |
Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies ... |
format |
Text |
author |
Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh Saeid Homayouni Eric Gill |
author_facet |
Masoud Mahdianpari Bahram Salehi Fariba Mohammadimanesh Saeid Homayouni Eric Gill |
author_sort |
Masoud Mahdianpari |
title |
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
title_short |
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
title_full |
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
title_fullStr |
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
title_full_unstemmed |
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform |
title_sort |
first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the google earth engine cloud computing platform |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs11010043 |
op_coverage |
agris |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Remote Sensing; Volume 11; Issue 1; Pages: 43 |
op_relation |
https://dx.doi.org/10.3390/rs11010043 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11010043 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
1 |
container_start_page |
43 |
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1774720136504147968 |