シャフダグ国立公園における生態系回復のための自然を基盤とした解決策の優先順位とグリーンファイナンス需要のリモートセンシング評価

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a replicable remote sensing methodology for prioritizing NbS in mountain ecosystems, useful for comparative studies in other regions.

🏢実務担当者:Offers a spatial framework for identifying restoration priorities, which can inform protected area management and green finance project design.

🏛政策担当者:Highlights the importance of data-driven conservation planning and could support national strategies for ecosystem restoration and green development.

NbS are becoming an ever more critical strategy to restore degraded ecosystems, while also promoting biodiversity conservation, climate resilience and ecosystem services. An integrated remote sensing approach was employed in this study to evaluate the spatial extent of ecosystem degradation and to prioritize the areas for NbS implementation in the Shahdag National Park, Azerbaijan. In Google Earth Engine and QGIS, open access geospatial data was processed to include Sentinel-2 Level-2A imagery, Landsat 8 and 9 Collection 2 Level-2 thermal products, Shuttle Radar Topography Mission (SRTM) elevation data, Hansen Global Forest Change, and the World Database on Protected Areas. Ecosystem conditions were characterized using a suite of environmental indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), land surface temperature (LST), slope, elevation, and forest cover change. These were combined to create a composite Ecosystem Degradation Index to help determine where restoration should be a priority. Pearson correlation analysis was also used to assess the relationship between vegetation condition and surface temperature. Results showed significant spatial variation throughout the national park, characterized by decreased vigour of vegetation, diminished availability of moisture, and greater bare-soil exposure and surface temperatures in certain landscape units. The correlation between NDVI and LST was moderate (r =-0.567, p<0.001) and was statistically significant, which means that higher vegetation density is associated with lower LST. The estimated forest cover in 2000 is about 83,184.15 ha with a projected loss of 279.8 ha of tree cover between 2001 and 2025. Created maps offer a practical spatial framework for prioritising ecosystem restoration and for protected-area management. The proposed procedure illustrates the benefits of combining freely available satellite observations and geospatial analyses in evidence-based NbS planning in mountain protected areas and provides a replicable approach for comparable mountain landscapes in the South Caucasus. The results also aid in the development of sustainable environmental management and green development strategies in Azerbaijan, for data-driven conservation decision-making.

• openalex https://doi.org/10.5281/zenodo.22130333first seen 2026-08-29 04:42:57 · last seen 2026-08-29 04:42:58