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Mapping Multi-Crop Cropland Abandonment in Conflict-Affected Ukraine Based on MODIS Time Series Analysis

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  • Nuo Xu

    (School of Earth Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China
    Zhejiang Key Laboratory of Geographic Information Science, Hangzhou 310028, China)

  • Hanchen Zhuang

    (School of Earth Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China
    Zhejiang Key Laboratory of Geographic Information Science, Hangzhou 310028, China)

  • Yijun Chen

    (School of Earth Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China
    Zhejiang Key Laboratory of Geographic Information Science, Hangzhou 310028, China)

  • Sensen Wu

    (School of Earth Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China
    Zhejiang Key Laboratory of Geographic Information Science, Hangzhou 310028, China)

  • Renyi Liu

    (School of Earth Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China
    Zhejiang Key Laboratory of Geographic Information Science, Hangzhou 310028, China)

Abstract

Since the outbreak of the Russia–Ukraine conflict in 2022, Ukraine’s agricultural production has faced significant disruption, leading to widespread cropland abandonment. These croplands were abandoned at different stages, primarily due to war-related destruction and displacement of people. Existing methods for detecting abandoned cropland fail to account for crop type differences and distinguish abandonment stages, leading to inaccuracies. Therefore, this study proposes a novel framework combining crop-type classification with the Bias-weighted Time-Weighted Dynamic Time Warping (BTWDTW) method, distinguishing between sowing and harvest abandonment. Additionally, the proposed framework improves accuracy by integrating a more nuanced analysis of crop-specific patterns, thus offering more precise insights into abandonment dynamics. The overall accuracy of the proposed method reached 88.9%. The results reveal a V-shaped trajectory of cropland abandonment, with abandoned areas increasing from 28,184 km 2 in 2022 to 33,278 km 2 in 2024, with 2023 showing an abandoned area of 24,007.65 km 2 . Spatially, about 70% of sowing abandonment occurred in high-conflict areas, with hotspots of unplanted abandonment shifting from southern Ukraine to the northeast, while unharvested abandonment was observed across the entire country. Significant variations were found across crop types, with maize experiencing the highest rate of unharvested abandonment, while wheat exhibited a more balanced pattern of sowing and harvest losses. The proposed method and results provide valuable insights for post-conflict agricultural recovery and decision-making in recovery planning.

Suggested Citation

  • Nuo Xu & Hanchen Zhuang & Yijun Chen & Sensen Wu & Renyi Liu, 2025. "Mapping Multi-Crop Cropland Abandonment in Conflict-Affected Ukraine Based on MODIS Time Series Analysis," Land, MDPI, vol. 14(8), pages 1-23, July.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:8:p:1548-:d:1711692
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    References listed on IDEAS

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    1. Li Chai & Ao Liu & Xuecao Li & Zhenshan Guo & Wanru He & Jianxi Huang & Tiecheng Bai & Jianguo Liu, 2024. "Telecoupled impacts of the Russia–Ukraine war on global cropland expansion and biodiversity," Nature Sustainability, Nature, vol. 7(4), pages 432-441, April.
    2. Thomas Glauben & Miranda Svanidze & Linde Götz & Sören Prehn & Tinoush Jamali Jaghdani & Ivan Đurić & Lena Kuhn, 2022. "The War in Ukraine, Agricultural Trade and Risks to Global Food Security," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(3), pages 157-163, May.
    3. Löw, Fabian & Prishchepov, Alexander V. & Waldner, François & Dubovyk, Olena & Akramkhanov, Akmal & Biradar, Chandrashekhar & Lamers, John P., 2018. "Mapping cropland abandonment in the Aral Sea Basin with MODIS time series," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(2), pages 1-24.
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