Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories
Abstract Alpine peatlands are one of the carbon reservoirs, provide vital ecosystem services, and support endangered biodiversity. However, they are globally understudied, including those in the Italian Alps, which host thousands of small sites averaging under 1 ha. Their complex geomorphology makes...
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American Geophysical Union (AGU)
2025-07-01
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| Series: | Earth and Space Science |
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| Online Access: | https://doi.org/10.1029/2025EA004201 |
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| author | Qiqi Li Manudeo Singh Sonia Silvestri |
| author_facet | Qiqi Li Manudeo Singh Sonia Silvestri |
| author_sort | Qiqi Li |
| collection | DOAJ |
| description | Abstract Alpine peatlands are one of the carbon reservoirs, provide vital ecosystem services, and support endangered biodiversity. However, they are globally understudied, including those in the Italian Alps, which host thousands of small sites averaging under 1 ha. Their complex geomorphology makes detection challenging with single‐sensor, low‐resolution remote sensing imagery. In the last decade, high resolution multi‐source imagery (e.g., Sentinel series) and the cloud‐based computation platforms (e.g., Google Earth Engine—GEE) have become available. Using these advancements, we developed a method to map the distribution of alpine peatlands. Utilizing 1 and 30 m digital elevation models (DEMs), optical, and microwave data sets, our method exploits a pixel‐based Random Forest (RF) machine‐learning algorithm on the GEE platform to map alpine peatlands in the Avisio River basin of the Italian Alps. The results show that the data set of single‐year time series multi‐source imagery, binary samples (peatland or non‐peatland), and 30 m DEM is the most effective for mapping alpine peatlands. The method achieved an overall accuracy of 90.5%, with 81.8% true positives and 0.8% false positives. The method identified 11.635 km2 of alpine peatlands, surpassing the 7.764 km2 documented in official inventories, this discrepancy may be due to overestimation but also gaps in the existing reference inventory. In the classification process, DEM derived variables proved more effective than optical and microwave derived variables. Variable importance analysis in the RF model indicated that elevation is the most influential factor, while the microwave derived VV‐VH difference (ascending track) contributes the least. |
| format | Article |
| id | doaj-art-032b8d5a2f89410aba040569561c9987 |
| institution | DOAJ |
| issn | 2333-5084 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Geophysical Union (AGU) |
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| series | Earth and Space Science |
| spelling | doaj-art-032b8d5a2f89410aba040569561c99872025-08-20T02:45:27ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842025-07-01127n/an/a10.1029/2025EA004201Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous TerritoriesQiqi Li0Manudeo Singh1Sonia Silvestri2Department of Biological, Geological, and Environmental Sciences University of Bologna Bologna ItalyInstitute of Geosciences University of Potsdam Potsdam GermanyDepartment of Biological, Geological, and Environmental Sciences University of Bologna Bologna ItalyAbstract Alpine peatlands are one of the carbon reservoirs, provide vital ecosystem services, and support endangered biodiversity. However, they are globally understudied, including those in the Italian Alps, which host thousands of small sites averaging under 1 ha. Their complex geomorphology makes detection challenging with single‐sensor, low‐resolution remote sensing imagery. In the last decade, high resolution multi‐source imagery (e.g., Sentinel series) and the cloud‐based computation platforms (e.g., Google Earth Engine—GEE) have become available. Using these advancements, we developed a method to map the distribution of alpine peatlands. Utilizing 1 and 30 m digital elevation models (DEMs), optical, and microwave data sets, our method exploits a pixel‐based Random Forest (RF) machine‐learning algorithm on the GEE platform to map alpine peatlands in the Avisio River basin of the Italian Alps. The results show that the data set of single‐year time series multi‐source imagery, binary samples (peatland or non‐peatland), and 30 m DEM is the most effective for mapping alpine peatlands. The method achieved an overall accuracy of 90.5%, with 81.8% true positives and 0.8% false positives. The method identified 11.635 km2 of alpine peatlands, surpassing the 7.764 km2 documented in official inventories, this discrepancy may be due to overestimation but also gaps in the existing reference inventory. In the classification process, DEM derived variables proved more effective than optical and microwave derived variables. Variable importance analysis in the RF model indicated that elevation is the most influential factor, while the microwave derived VV‐VH difference (ascending track) contributes the least.https://doi.org/10.1029/2025EA004201Italian alpine peatlandsalpine peatlands mappingrandom forest classificationmulti‐source remote sensing dataGoogle Earth Engine |
| spellingShingle | Qiqi Li Manudeo Singh Sonia Silvestri Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories Earth and Space Science Italian alpine peatlands alpine peatlands mapping random forest classification multi‐source remote sensing data Google Earth Engine |
| title | Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories |
| title_full | Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories |
| title_fullStr | Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories |
| title_full_unstemmed | Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories |
| title_short | Remote Sensing of Alpine Peatlands: Challenges of Mapping Thousands of Sparse Small Sites Scattered Across Extensive Mountainous Territories |
| title_sort | remote sensing of alpine peatlands challenges of mapping thousands of sparse small sites scattered across extensive mountainous territories |
| topic | Italian alpine peatlands alpine peatlands mapping random forest classification multi‐source remote sensing data Google Earth Engine |
| url | https://doi.org/10.1029/2025EA004201 |
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