High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning
Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing sn...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Water |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frwa.2023.1128758/full |
| _version_ | 1797814396783689728 |
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| author | Kehan Yang Kehan Yang Aji John Aji John David Shean Jessica D. Lundquist Ziheng Sun Fangfang Yao Stefan Todoran Nicoleta Cristea Nicoleta Cristea |
| author_facet | Kehan Yang Kehan Yang Aji John Aji John David Shean Jessica D. Lundquist Ziheng Sun Fangfang Yao Stefan Todoran Nicoleta Cristea Nicoleta Cristea |
| author_sort | Kehan Yang |
| collection | DOAJ |
| description | Mountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m < DCE < 27m), with a median F1 score of 0.87 across the four study sites, and shows promising accuracy for areas very close (>10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow. |
| first_indexed | 2024-03-13T08:07:10Z |
| format | Article |
| id | doaj.art-3254940db0a244f9bc13c45b5a45829c |
| institution | Directory Open Access Journal |
| issn | 2624-9375 |
| language | English |
| last_indexed | 2024-03-13T08:07:10Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Water |
| spelling | doaj.art-3254940db0a244f9bc13c45b5a45829c2023-06-01T04:30:49ZengFrontiers Media S.A.Frontiers in Water2624-93752023-06-01510.3389/frwa.2023.11287581128758High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learningKehan Yang0Kehan Yang1Aji John2Aji John3David Shean4Jessica D. Lundquist5Ziheng Sun6Fangfang Yao7Stefan Todoran8Nicoleta Cristea9Nicoleta Cristea10Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United StateseScience Institute, University of Washington, Seattle, WA, United StateseScience Institute, University of Washington, Seattle, WA, United StatesDepartment of Biology, University of Washington, Seattle, WA, United StatesDepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, United StatesDepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, United StatesCenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, United StatesCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, United StatesComputer Science, University of Washington, Seattle, WA, United StatesDepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, United StateseScience Institute, University of Washington, Seattle, WA, United StatesMountain snowpack provides critical water resources for forest and meadow ecosystems that are experiencing rapid change due to global warming. An accurate characterization of snowpack heterogeneity in these ecosystems requires snow cover observations at high spatial resolutions, yet most existing snow cover datasets have a coarse resolution. To advance our observation capabilities of snow cover in meadows and forests, we developed a machine learning model to generate snow-covered area (SCA) maps from PlanetScope imagery at about 3-m spatial resolution. The model achieves a median F1 score of 0.75 for 103 cloud-free images across four different sites in the Western United States and Switzerland. It is more accurate (F1 score = 0.82) when forest areas are excluded from the evaluation. We further tested the model performance across 7,741 mountain meadows at the two study sites in the Sierra Nevada, California. It achieved a median F1 score of 0.83, with higher accuracy for larger and simpler geometry meadows than for smaller and more complexly shaped meadows. While mapping SCA in regions close to or under forest canopy is still challenging, the model can accurately identify SCA for relatively large forest gaps (i.e., 15m < DCE < 27m), with a median F1 score of 0.87 across the four study sites, and shows promising accuracy for areas very close (>10m) to forest edges. Our study highlights the potential of high-resolution satellite imagery for mapping mountain snow cover in forested areas and meadows, with implications for advancing ecohydrological research in a world expecting significant changes in snow.https://www.frontiersin.org/articles/10.3389/frwa.2023.1128758/fullhigh-resolution snow cover mappingforest snowmountain meadowsPlanet imagerymachine learning |
| spellingShingle | Kehan Yang Kehan Yang Aji John Aji John David Shean Jessica D. Lundquist Ziheng Sun Fangfang Yao Stefan Todoran Nicoleta Cristea Nicoleta Cristea High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning Frontiers in Water high-resolution snow cover mapping forest snow mountain meadows Planet imagery machine learning |
| title | High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning |
| title_full | High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning |
| title_fullStr | High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning |
| title_full_unstemmed | High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning |
| title_short | High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning |
| title_sort | high resolution mapping of snow cover in montane meadows and forests using planet imagery and machine learning |
| topic | high-resolution snow cover mapping forest snow mountain meadows Planet imagery machine learning |
| url | https://www.frontiersin.org/articles/10.3389/frwa.2023.1128758/full |
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