Remote sensing assessment of nature-based solution priorities and green finance needs for ecosystem restoration in Shahdag National Park
DOI:
https://doi.org/10.5281/zenodo.22130333Keywords:
Nature-based solutions, remote sensing, ecosystem degradation, Shahdag National Park, NDVIAbstract
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.
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Arifzade, E., Mammadov, U., & Abbasova, G. (2026). Economic and ecological analysis of the relationship between PM2.5 and PM10 levels and industrial and transport factors in the cities of Sumgayit and Almaty. Journal of Economics International Scientific Journal, 3(2), 14–22. https://doi.org/10.36719/3104-4727/7/14-22
Arifzade, E., Zhumazhanov, A., & Yermoldanov, Y. (2026). Assessing the relationship between industrial air pollution, vegetation cover and surface temperature in Temirtau, Kazakhstan. Journal of Green Economy and Optimization Research, 1(Special Issue 1), 5–18. https://doi.org/10.5281/zenodo.22128026
Cohen-Shacham, E., Walters, G., Janzen, C., & Maginnis, S. (Eds.). (2016). Nature-based solutions to address global societal challenges. International Union for Conservation of Nature. https://doi.org/10.2305/IUCN.CH.2016.13.en
European Commission. (n.d.). Nature restoration regulation. Retrieved July 21, 2026, from https://environment.ec.europa.eu/topics/nature-and-biodiversity/nature-restoration-regulation_en
Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Guliyeva, S., Gadimli, N., Azizov, Y., Hajiyev, Z., & Bayramov, S. (2021). Fiscal policy effectiveness and risks in the development of Azerbaijan’s economy during economic shock. Journal of Eastern European and Central Asian Research, 8(2), 171–183. https://doi.org/10.15549/jeecar.v8i2.710
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
International Union for Conservation of Nature. (2020). Global standard for nature-based solutions: A user-friendly framework for the verification, design and scaling up of NbS (1st ed.). https://doi.org/10.2305/IUCN.CH.2020.08.en
Mammadov, U. (2026). Remote sensing-derived maps of ecosystem condition and degradation in Shahdag National Park, Azerbaijan [Dataset]. Green Economy and Optimization Research Lab. https://doi.org/10.13140/RG.2.2.36843.30243
Mammadov, U., & Gurbanov, A. (2026). Field photographic dataset of forest ecosystems, mountain landscapes and ecological features in and around Shahdag National Park, Azerbaijan [Dataset]. Green Economy and Optimization Research Lab. https://doi.org/10.13140/RG.2.2.19542.41280
Mammadov, U., Namazova, T., & Ahmadova, N. (2026). Microplastic pollution and coastal anthropogenic pressure in the Caspian Sea Basin through systematic review and secondary data analysis. American Journal of Energy and Natural Resources, 5(1), 54–64. https://doi.org/10.54536/ajenr.v5i1.8234
Organisation for Economic Co-operation and Development. (2018). Cost-benefit analysis and the environment: Further developments and policy use. OECD Publishing. https://doi.org/10.1787/9789264085169-en
Pettorelli, N., Laurance, W. F., O’Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: Opportunities and challenges. Journal of Applied Ecology, 51(4), 839–848. https://doi.org/10.1111/1365-2664.12261
Qadimli, N., & Mammadov, U. (2026). Urban expansion, vegetation patterns and surface heat distribution in Luanda, Angola. Journal of Green Economy and Optimization Research, 1(2), 24–35. https://doi.org/10.5281/zenodo.22057564
Qədimli, N., & Məmmədov, Ü. (2026). Azərbaycanda yaşıl maliyyənin inkişafı və iqtisadi fəaliyyətlərin iqlimə təsiri. In “Azərbaycanda turizm potensialının qiymətləndirilməsi və təkmilləşdirilməsi yolları” Respublika Elmi Konfransının materialları (pp. 212–215). https://doi.org/10.5281/zenodo.21074297
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium (Vol. 1, pp. 309–317). NASA.
Sadiqov, E., Qadimli, N., & Mammadov, U. (2026). Green finance instruments for supporting solar energy development in Azerbaijan. American Journal of Energy and Natural Resources, 5(1), 18–26. https://doi.org/10.54536/ajenr.v5i1.7984
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
United Nations Environment Programme. (2026). State of finance for nature 2026. https://www.unep.org/resources/state-finance-nature-2026
Valiyev, A., Huseynov, A., Gadimli, N., Huseynov, I., & Elmar, A. (2021). A practical model to implementing IFRS in the national accounting system: The case of Azerbaijan. Journal of Eastern European and Central Asian Research, 8(4), 585–598. https://doi.org/10.15549/jeecar.v8i4.836
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), 375–386. https://doi.org/10.1016/j.rse.2006.09.003
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