Remote sensing assessment of nature-based solution priorities and green finance needs for ecosystem restoration in Shahdag National Park
- 1Mingachevir State University
Abstract
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.
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BibTeX
@article{qadimli2026remote,
title = {{Remote sensing assessment of nature-based solution priorities and green finance needs for ecosystem restoration in Shahdag National Park}},
author = {Nushaba Qadimli and Ulvi Mammadov},
journal = {Zenodo (CERN European Organization for Nuclear Research)},
year = {2026},
month = aug,
doi = {10.5281/zenodo.22130332},
url = {https://doi.org/10.5281/zenodo.22130332},
note = {CC BY},
}
RIS
TY - JOUR
TI - Remote sensing assessment of nature-based solution priorities and green finance needs for ecosystem restoration in Shahdag National Park
AU - Nushaba Qadimli
AU - Ulvi Mammadov
PY - 2026
DA - 2026/08/27
JO - Zenodo (CERN European Organization for Nuclear Research)
DO - 10.5281/zenodo.22130332
UR - https://doi.org/10.5281/zenodo.22130332
AB - 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.
ER -