期刊
LANDSCAPE AND URBAN PLANNING
卷 214, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.landurbplan.2021.104170
关键词
Farmland abandoment Recultivation; Urbanization; Google Earth Engine; Landsat imagery; Logistic regression
类别
资金
- DFF-Danish ERC Support Program [116491, 912700001B]
- Overseas Expertise Introduction Project for Discipline Innovation (111 Project) [B17024]
- Research Fund Program of Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology [2018K01]
- Scientific Research Start-up Fund Projects of Intro-duced Talents [5909001803]
The study reveals widespread farmland abandonment in China's Sunan economic region, with approximately 232,700 ha of farmland being abandoned from 2001 to 2018 and the highest annual abandonment rate observed in 2017. The research shows that locational and socio-economic factors play a crucial role in determining farmland abandonment patterns.
Urbanization often results in agricultural expansion but can also lead to farmland abandonment. However, it remains unclear about the extent, exact timing and determinants of farmland abandonment in response to ongoing urbanization. Using the example of China's Sunan economic region, we present the spatiotemporal trajectories of farmland abandonment and recultivation from 2001 to 2018. We classified Landsat satellite image time-series with a regression trees classifier in Google Earth Engine (GEE). Further, we analyzed the spatiotemporal patterns and rates of farmland abandonment and recultivation. Spatially-explicit logistic regressions were applied to explore the determinants of farmland abandonment in space and time. Our results show widespread farmland abandonment: approximately 232,700 ha of farmland had ever been abandoned from 2001 to 2018, with the highest annual abandonment rate (8.5%) in 2017. Approximately 66,200 ha of abandoned fields were later recultivated, with the maximum recultivated area (13,600 ha) in 2018. Approximately 92% of abandoned fields were later recultivated or reused as the impervious surface within two years of the first detection of abandonment, suggesting more rapid land transformation. The regressions reveal that locational and socio-economic factors determined farmland abandonment patterns. Specifically, a higher likelihood of farmland abandonment was statistically associated with an increased distance from the nearest settlements; the significantly positive relationship between 'Non-agricultural GDP' and farmland abandonment strengthened over time. In contrast, a lower likelihood of farmland abandonment was observed in areas with more cash crops. The statistical results may extend the application of the Ricardian comparative advantage theory along with Alonso's bid rent theory to explain and predict abandonment patterns in response to ongoing urbanization. Our study is the first attempt in China to apply 30-m Landsat imageries to reconstruct abandonment patterns over long timeseries. The findings provide important insights into adjusting land-management practices for preventing farmland abandonment due to urbanization.
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