Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques
The importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any fie...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2024.2438638 |
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| author | Elham Shafeian Bryan J. Mood Kenneth W. Belcher Colin P. Laroque |
| author_facet | Elham Shafeian Bryan J. Mood Kenneth W. Belcher Colin P. Laroque |
| author_sort | Elham Shafeian |
| collection | DOAJ |
| description | The importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any field campaigns. We performed a Random Forest (RF) classification to map the spatial distribution of native trees in different scenarios. The optimal model showed an overall accuracy and kappa of 0.99 and 0.98, respectively. We mapped 40,500 km2 of tree cover, including native tree cover (approximately 29,565 km2 ≈10.5%), excluding plantations, regional and provincial parks, and water bodies in the Canadian prairie region of Saskatchewan. According to our results, the highest numbers of native trees were found in the eastern and northwestern parts of the study area – cluster “BLK_1” and the “Black” soil zone, with total cover of 5,388 and 13,233 km2, respectively. The lowest numbers of native trees were found in the southwest side of the study area – cluster “BRN_6” and the “Brown” soil zone, with total cover of 2.38 and 979.5 km2, respectively. This research is important as detecting and quantifying native trees is an integral part of studies on carbon sequestration, economics, and effective management strategies. |
| format | Article |
| id | doaj-art-92059611cb72487daa3d335adf404ff0 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-92059611cb72487daa3d335adf404ff02025-08-20T01:59:08ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2024.2438638Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniquesElham Shafeian0Bryan J. Mood1Kenneth W. Belcher2Colin P. Laroque3Mistik Askiwin Dendrochronology Laboratory (MAD Lab), University of Saskatchewan, Saskatoon, CanadaMistik Askiwin Dendrochronology Laboratory (MAD Lab), University of Saskatchewan, Saskatoon, CanadaDepartment of Agricultural and Resource Economics, University of Saskatchewan, Saskatoon, CanadaMistik Askiwin Dendrochronology Laboratory (MAD Lab), University of Saskatchewan, Saskatoon, CanadaThe importance of trees in non-forest landscapes has been the focus of only a few studies. However, these trees provide many important ecosystem services. In this study, we mapped and quantified these trees using Sentinel-2 (S2) and very high-resolution (VHR) Google satellite imagery without any field campaigns. We performed a Random Forest (RF) classification to map the spatial distribution of native trees in different scenarios. The optimal model showed an overall accuracy and kappa of 0.99 and 0.98, respectively. We mapped 40,500 km2 of tree cover, including native tree cover (approximately 29,565 km2 ≈10.5%), excluding plantations, regional and provincial parks, and water bodies in the Canadian prairie region of Saskatchewan. According to our results, the highest numbers of native trees were found in the eastern and northwestern parts of the study area – cluster “BLK_1” and the “Black” soil zone, with total cover of 5,388 and 13,233 km2, respectively. The lowest numbers of native trees were found in the southwest side of the study area – cluster “BRN_6” and the “Brown” soil zone, with total cover of 2.38 and 979.5 km2, respectively. This research is important as detecting and quantifying native trees is an integral part of studies on carbon sequestration, economics, and effective management strategies.https://www.tandfonline.com/doi/10.1080/22797254.2024.2438638Native treeremote sensingCanadian prairieSentinel-2very high-resolution imagery |
| spellingShingle | Elham Shafeian Bryan J. Mood Kenneth W. Belcher Colin P. Laroque Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques European Journal of Remote Sensing Native tree remote sensing Canadian prairie Sentinel-2 very high-resolution imagery |
| title | Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques |
| title_full | Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques |
| title_fullStr | Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques |
| title_full_unstemmed | Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques |
| title_short | Assessing spatial distribution and quantification of native trees in Saskatchewan’s prairie landscape using remote sensing techniques |
| title_sort | assessing spatial distribution and quantification of native trees in saskatchewan s prairie landscape using remote sensing techniques |
| topic | Native tree remote sensing Canadian prairie Sentinel-2 very high-resolution imagery |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2024.2438638 |
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