Risk assessment and validation of farmland abandonment based on time series change detection

Abstract

Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km², constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km², only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.

Keywords: Farmland abandonment; NDVI; Risk assessment; Spatial distribution; Time series change detection.