Agricultural Land Abandonment and Retirement Mapping in the Northern China Crop-Pasture Band Using Temporal Consistency Check and Trajectory-Based Change Detection Approach

Abstract

Agricultural land abandonment and retirement are important and lead to different types of land use and cover change. Generally, abandonment and retirement are caused by different social and environmental factors and result in different ecological and economic benefits and costs. Faced with the complexity of agricultural land change over time, this study aims to develop a new framework to distinguish between agricultural land abandonment and retirement and detect the extent and exact year of abandonment and retirement using Google Earth Engine (GEE). We tested our approach for three typical regions in the northern China crop-pasture band, where agricultural land abandonment and retirement are widespread. First, based on the spectral features obtained from Landsat images, annual land-cover maps were obtained with sample migration and random forest from 1998 to 2019 (0.87 overall accuracy). Second, a temporal consistency check method was proposed to further improve the classification performance (0.92 overall accuracy). Third, a trajectory-based change detection approach was developed to identify abandonment and retirement (F1 score for abandonment: 0.74, retirement: 0.83). Our results indicate that the spatiotemporal patterns of abandonment and retirement in the study area greatly differed. Overlapping of the topography and climate data showed that agricultural land with steep slopes (<inline-formula> <tex-math notation="LaTeX">$&gt; 10^{\circ }$ </tex-math></inline-formula>) was more likely to be retired and that abandonment was more likely to occur in areas with less precipitation. Overall, the methods used herein are robust for agricultural land abandonment and retirement monitoring and may be extended to other land-cover change studies.