基于变化和时序信息的耕地撂荒遥感识别方法OA

Obtaining timely and accurate information on the distribution of abandoned cropland is of crucial significance for cropland protection and agricultural production management.To address the challenges of difficult identification and limited mapping accuracy of abandoned cropland,a framework integrating change detection and land cover classification methods was proposed to identify abandoned cropland in the Nansi Lake region of Shandong Province.Using Sentinel-2 satellite remote sensing imagery from 2019 to 2024 as the primary data source,a phenology-enhanced change vector analysis method was introduced to detect changed areas.On this basis,the random forest algorithm was utilized to perform land cover classification within these changed areas.Subsequently,a temporal sliding window algorithm was adopted to identify the specific timing and location of the abandoned cropland.The results indicated that the F1 score of abandoned cropland using the proposed method stabilized between 86.05%and 89.89%,outperforming that of traditional methods based on land cover post-classification comparison and change vector analysis.From 2019 to 2024,the cumulative area of abandoned cropland in the study region was 604.70 km2,yielding an abandonment rate of 3.87%based on the 2019 cropland baseline map.Spatially,the distribution of abandoned cropland exhibited a distinct pattern of"high in the northeast and low in the southwest."The framework developed demonstrated high accuracy and applicability for monitoring abandoned cropland,providing crucial data and methodological support for advancing research on cropland abandonment and facilitating cropland protection workflows.