Spatiotemporal Analysis and War Impact Assessment of Agricultural Land in Ukraine Using RS and GIS Technology

Yue Ma, Dongmei Lyu, Kenan Sun, Sijia Li, Bingxue Zhu, Ruixue Zhao, Miao Zheng and Kaishan Song ()
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Yue Ma: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Dongmei Lyu: School of Electrical and Computer Engineering, Jilin Jianzhu University, Changchun 130118, China
Kenan Sun: School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
Sijia Li: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Bingxue Zhu: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Ruixue Zhao: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Miao Zheng: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Kaishan Song: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Land, 2022, vol. 11, issue 10, 1-18

Abstract: Military conflicts are one of the inevitable factors that can cause countries to suffer from food insecurity due to reduced agricultural productivity, increased food prices, and the deterioration of agricultural land and infrastructure. Farmland may become fallowed and abandoned as a result of reduced investment in agricultural management caused by military conflicts. To rapidly assess the impact of conflicts on agricultural land and food security, the utilization of effective and feasible methods for the regular monitoring agricultural management status is necessary. To achieve this goal, we developed a framework for analyzing the spatiotemporal distribution of agricultural land and assessing the impact of the Ukraine–Russia war on agricultural management in Ukraine using remote sensing (RS) and geographic information system (GIS) technology. The random forest (RF) classifier, gap filling and Savitzky–Golay filtering (GF-SG) method, fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) algorithm, and kernel density method were jointly used to classify and reveal the spatiotemporal distribution of fallowed and abandoned croplands from 2018 to 2022 based on Landsat time series data on the Google Earth Engine (GEE) platform. The results demonstrated that fallowed and abandoned croplands could be successfully and effectively identified through these proven methods. Hotspots of fallowed croplands frequently occurred in eastern Ukraine, and long-term consecutive fallow agricultural management caused cropland abandonment. Moreover, hotspots of war-driven fallowed croplands were found in western Kherson and the center of Luhansk, where the war has been escalated for a long time. This reveals that the war has had a significant negative impact on agricultural management and development. These results highlight the potential of developing an accessible methodological framework for conducting regular assessments to monitor the impact of military conflicts on food security and agricultural management.

Keywords: remote sensing; geographic information system technology; agricultural land; military conflicts (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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