Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data
The ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machin...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001980 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849694872009703424 |
|---|---|
| author | Nataliia Kussul Andrii Shelestov Bohdan Yailymov Hanna Yailymova Guido Lemoine Klaus Deininger |
| author_facet | Nataliia Kussul Andrii Shelestov Bohdan Yailymov Hanna Yailymova Guido Lemoine Klaus Deininger |
| author_sort | Nataliia Kussul |
| collection | DOAJ |
| description | The ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machine learning-based classification applied to Sentinel-1 and Sentinel-2 satellite imagery. By leveraging cloud computing platforms, including Google Earth Engine (GEE) and the Copernicus Data Space Ecosystem (CDSE), we develop high-resolution KPI-Ukraine (Igor Sikorsky Kyiv Polytechnic Institute (KPI) in Ukraine) land use maps spanning from 2016 to 2024. The study integrates Random Forest and Multi-Layer Perceptron classification techniques to improve accuracy, addressing spectral ambiguities and classification noise. Additionally, a novel transfer learning approach enables reliable classification in conflict-affected areas with limited ground-truth data.We achieved high classification accuracy across the 14 major crop types in Ukraine and abandoned land, validated through F1-scores exceeding 90 % for most classes. The fusion of the results generated on the GEE and CDSE platforms enhanced the classification accuracy for minor classes. Our analysis reveals significant reductions in cultivated land in 2022–2024, particularly in conflict zones, where agricultural activity has been heavily disrupted. Overall, Ukraine’s arable land area shrunk by 10 % nationwide. The consistently high accuracy of our classification methodology across the nine-year study period demonstrates its robustness and suitability for long-term monitoring of agricultural dynamics in conflict-affected regions and provides a valuable tool for guiding post-war recovery efforts. Our findings underscore the importance of leveraging satellite data for timely and accurate land use monitoring, supporting policymakers in addressing food security challenges and promoting sustainable agricultural practices. This framework also holds potential for broader applications in monitoring land use changes in conflict zones and regions undergoing rapid environmental shifts. |
| format | Article |
| id | doaj-art-dd050191793f4f8990e1dcefb5feaa34 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-dd050191793f4f8990e1dcefb5feaa342025-08-20T03:19:56ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010455110.1016/j.jag.2025.104551Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite dataNataliia Kussul0Andrii Shelestov1Bohdan Yailymov2Hanna Yailymova3Guido Lemoine4Klaus Deininger5University of Maryland, College Park, MD 20742, United States; National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave 37, 03056 Kyiv, Ukraine; Space Research Institute NASU-SSAU, Glushkov ave 40, 4/1, 03187 Kyiv, Ukraine; Corresponding author.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave 37, 03056 Kyiv, Ukraine; Space Research Institute NASU-SSAU, Glushkov ave 40, 4/1, 03187 Kyiv, UkraineSpace Research Institute NASU-SSAU, Glushkov ave 40, 4/1, 03187 Kyiv, UkraineNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi ave 37, 03056 Kyiv, Ukraine; Space Research Institute NASU-SSAU, Glushkov ave 40, 4/1, 03187 Kyiv, UkraineJoint Research Center of the European Commission, Ispra, ItalyThe World Bank, 1818 H Street NW, Washington, DC, United StatesThe ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machine learning-based classification applied to Sentinel-1 and Sentinel-2 satellite imagery. By leveraging cloud computing platforms, including Google Earth Engine (GEE) and the Copernicus Data Space Ecosystem (CDSE), we develop high-resolution KPI-Ukraine (Igor Sikorsky Kyiv Polytechnic Institute (KPI) in Ukraine) land use maps spanning from 2016 to 2024. The study integrates Random Forest and Multi-Layer Perceptron classification techniques to improve accuracy, addressing spectral ambiguities and classification noise. Additionally, a novel transfer learning approach enables reliable classification in conflict-affected areas with limited ground-truth data.We achieved high classification accuracy across the 14 major crop types in Ukraine and abandoned land, validated through F1-scores exceeding 90 % for most classes. The fusion of the results generated on the GEE and CDSE platforms enhanced the classification accuracy for minor classes. Our analysis reveals significant reductions in cultivated land in 2022–2024, particularly in conflict zones, where agricultural activity has been heavily disrupted. Overall, Ukraine’s arable land area shrunk by 10 % nationwide. The consistently high accuracy of our classification methodology across the nine-year study period demonstrates its robustness and suitability for long-term monitoring of agricultural dynamics in conflict-affected regions and provides a valuable tool for guiding post-war recovery efforts. Our findings underscore the importance of leveraging satellite data for timely and accurate land use monitoring, supporting policymakers in addressing food security challenges and promoting sustainable agricultural practices. This framework also holds potential for broader applications in monitoring land use changes in conflict zones and regions undergoing rapid environmental shifts.http://www.sciencedirect.com/science/article/pii/S1569843225001980Agricultural land use changeWar impactCroplandUncultivated landsUkraineGoogle Earth Engine |
| spellingShingle | Nataliia Kussul Andrii Shelestov Bohdan Yailymov Hanna Yailymova Guido Lemoine Klaus Deininger Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data International Journal of Applied Earth Observations and Geoinformation Agricultural land use change War impact Cropland Uncultivated lands Ukraine Google Earth Engine |
| title | Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data |
| title_full | Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data |
| title_fullStr | Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data |
| title_full_unstemmed | Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data |
| title_short | Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data |
| title_sort | assessment of war induced agricultural land use changes in ukraine using machine learning applied to sentinel satellite data |
| topic | Agricultural land use change War impact Cropland Uncultivated lands Ukraine Google Earth Engine |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001980 |
| work_keys_str_mv | AT nataliiakussul assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata AT andriishelestov assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata AT bohdanyailymov assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata AT hannayailymova assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata AT guidolemoine assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata AT klausdeininger assessmentofwarinducedagriculturallandusechangesinukraineusingmachinelearningappliedtosentinelsatellitedata |