1. PA
  2. All Volumes and Issues
  3. Volume 1, Number 1, 2025
  4. Spatial Modelling of Agricultural Land Abandonment Probability in a Foothill Hromada Using Sentinel-2 Satellite Data

Spatial Modelling of Agricultural Land Abandonment Probability in a Foothill Hromada Using Sentinel-2 Satellite Data

PA.
2025;
Volume 1, Number 1
: pp. 11 - 20
Authors:
  1. Khrystyna Marusazh
  2. Yuliya Petryk
1
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University
2
Department of Photogrammetry and Geoinformatics, Lviv Polytechnic National University

Objective. The aim of this study is to identify potentially abandoned agricultural lands within the Vyhoda territorial community of Ivano-Frankivsk region through multi-criteria analysis based on Sentinel-2 satellite imagery, vegetation indices, and topographic and infrastructural parameters. Methodology. An  adapted multi-criteria spatial modeling approach was proposed to detect potentially abandoned agricultural lands. The study employed multispectral Sentinel-2 satellite images acquired in August 2024. Based on the pre-processed imagery, seven vegetation indices were calculated, of which NDVI, MSAVI2, and RECI were included in the final model as the most effective for reflecting vegetation condition, density, and type. Additionally, three non-spectral variables were considered: surface slope, distance to roads, and distance to residential areas. All spatial layers were harmonized to a common spatial resolution, normalized, and reclassified using a five-point suitability scale. Data integration was performed using the Weighted Overlay method in the ArcGIS Pro environment, which enabled the construction of an integrated probability layer of land abandonment. The weight coefficients of each layer were determined based on testing and expert evaluation. Results. The resulting abandonment risk map demonstrates spatial heterogeneity of agricultural land within the community. Areas with the highest risk classes (4–5) are predominantly located in the southern and northeastern parts of the territory, which is associated with mountainous terrain, limited accessibility, and fragmented land use patterns. The central part is dominated by areas with low abandonment risk (classes 1–2), typical of actively cultivated lands. The consistency between the results and the multispectral imagery confirms the effectiveness of the proposed approach in identifying abandoned lands in structurally complex foothill environments. Practical significance. The proposed approach allows for effective identification of spatial hotspots of land-use degradation to support local planning, agro-ecological monitoring, and the development of strategies for sustainable land use in mountainous communities.

abandoned lands
spatial modeling
satellite imagery
Sentinel-2
multi-criteria analysis
vegetation indices
Carpathian foothills
  1. Kulikovska, O., Pavlo, K., & Stupen, R. (2024). Rozkryttia mozhlyvostei vykorystannia multyspektralnykh znimkiv dlia tochnoi otsinky posiviv. Mistobuduvannia ta terytorialne planuvannia, (87), 368–387.
  2. Baumann, M. et al. (2011). Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy, 28(3), 552–562. DOI: https://doi.org/10.1016/j.landusepol.2010.11.003.
  3. Castillo, C. P., Kavalov, B., Diogo, V., Jacobs-Crisioni, C., e Silva, F. B., & Lavalle, C. (2018). Agricultural land abandonment in the EU within 2015–2030 (No. JRC113718). Joint Research Centre.
  4. Cramer, V. A., Hobbs, R. J., & Standish, R. J. (2008). What’s new about old fields? Land abandonment and ecosystem assembly. Trends in Ecology & Evolution, 23(2), 104–112. DOI: https://doi.org/10.1016/j.tree.2007.10.005.
  5. EOS Data Analytics. (2022). Vehetatsiini indeksy yak rozumni rishennia dlia ahrobiznesu. Retrieved from https://eos.com/uk/blog/vehetatsiini-indeksy/.
  6. Estel, S., Kuemmerle, T., Alcantara, C., Levers, C., Prishchepov, A. V., & Hostert, P. (2015). Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312–325.
  7. European Space Agency. (2015). Sentinel-2 user handbook (ESA Standard Document GMES-S2OP-EOPG-TN-13-0001). European Space Agency. Retrieved from https://sentinels.copernicus.eu/documents/247904/685211/Sentinel- 2_User_Handbook.
  8. FAO. (2023). Analysis of land abandonment and development of agricultural land markets in the Republic of North Macedonia – Conclusions and policy recommendations. Retrieved from https://www.fao.org/3/cc4778en/cc4778en.pdf.
  9. Hryhoriv, Y., Degtyarjov, V., Marenych, M., Hanhur, V., Karbivska, U., Gamajunova, V., ... & Konshin, R. (2024). Qualitative assessment of soils in Dolyna District of Ivano-Frankivsk region. Journal of Ecological Engineering, 25(9). DOI: 10.12911/22998993/191370.
  10. Keenleyside, C., Tucker, G., & McConville, A. (2010). Farmland Abandonment in the EU: an Assessment of Trends and Prospects. Institute for European Environmental Policy, London.
  11. Key, C. H., & Benson, N. C. (2006). Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio. In D. C. Lutes, R. E. Keane, J. F. Caratti, C. H. Key,
  12. N. C. Benson, S. Sutherland, & L. J. Gangi (Eds.), FIREMON: Fire Effects Monitoring and Inventory System. Ogden, UT: USDA Forest Service, Rocky Mountain Research Station, Gen. Tech. Rep.
  13. Kolecka, N., & Kozak, J. (2019). Wall-to-Wall Parcel-Level Mapping of Agricultural Land Abandonment in the Polish Carpathians. Land, 8(9), 129. DOI: https://doi.org/10.3390/land8090129.
  14. Kuemmerle, T., Hostert, P., Radeloff, V. C., van der Linden, S., Perzanowski, K., & Kruhlov, I. (2008). Cross-border comparison of post-socialist farmland abandonment in the Carpathians. Ecosystems, 11, 614–628. DOI: 10.1007/s10021-008-9146-z.
  15. Lesiv, M. et al. (2018). Spatial distribution of arable and abandoned land across former Soviet Union countries. Scientific Data, 5, 180056. DOI: https://doi.org/10.1038/sdata.2018.56.
  16. Prishchepov, A. V., Schierhorn, F., & Löw, F. (2021). Unraveling the Diversity of Trajectories and Drivers of Global Agricultural Land Abandonment. Land 2021, 10, 97. DOI: 10.3390/land10020097.
  17. Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126.
  18. Rouibah, K., & Belabbas, M. (2020). Applying multi‑index approach from Sentinel-2 imagery to extract urban area in dry season (semi‑arid land). Revista de Teledetección, 20, Article 13787.
  19. UABIO. (2019). Marhinalni zemli: osoblyvosti terminolohii ta vyznachennia. Retrieved from https://uabio.org/materials/100/.
  20. USGS. Landsat Surface Reflectance-derived Spectral Indices. Retrieved from https://www.usgs.gov/landsat- missions/landsat-surface-reflectance-derived-spectral-indices.
  21. Ustaoglu, E., & Collier, M. J. (2018). Farmland abandonment in Europe: An overview of drivers, consequences, and assessment of the sustainability implications. Environmental reviews, 26(4), 396–416. DOI: 10.1139/er-2018-0001.
  22. Wang, L., Li, Q., Wang, Y., Zeng, K., & Wang, H. (2024). An OVR-FWP-RF Machine Learning Algorithm for Identification of Abandoned Farmland in Hilly Areas Using Multispectral Remote Sensing Data. Sustainability, 16(15), 6443. DOI: https://doi.org/10.3390/su16156443.
  23. World Bank. Priorities for Agricultural Support in Ukraine (English). Washington, D.C.: World Bank Group. Retrieved from     http://documents.worldbank.org/curated/en/099062524074615884.
  24. Xiong, Y., Li, R., & Yue, Y. (2013). Quantitative estimation of photosynthetic pigments using new spectral indices. Journal of Forestry Research, 24, 477–483.
  25. Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of sensors, 2017(1), DOI: https://doi.org/10.1155/2017/1353691.
  26. Ye, J., Hu, Y., Feng, Z., Zhen, L., Shi, Y., Tian, Q., & Zhang, Y. (2024). Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China. Remote Sensing, 16(6), 1089.