Improved Cropland Abandonment Detection with Deep Learning Vision Transformer (DL-ViT) and Multiple Vegetation Indices

Abstract

Cropland abandonment is a worldwide problem that threatens food security and has significant consequences for the sustainable growth of the economy, society, and the natural ecosystem. However, detecting and mapping abandoned lands is challenging due to their diverse characteristics, like varying vegetation cover, spectral reflectance, and spatial patterns. To overcome these challenges, we employed Gaofen-6 (GF-6) imagery in conjunction with a Vision Transformer (ViT) model, harnessing self-attention and multi-scale feature learning to significantly enhance our ability to accurately and efficiently classify land covers. In Mianchi County, China, the study reveals that approximately 385 hectares of cropland (about 2.2% of the total cropland) were abandoned between 2019 and 2023. The highest annual abandonment occurred in 2021, with 214 hectares, followed by 170 hectares in 2023. The primary reason for the abandonment was the transformation of cropland into excavation activities, barren lands, and roadside greenways. The ViT’s performance peaked when multiple vegetation indices (VIs) were integrated into the GF-6 bands, resulting in the highest achieved results (F1 score = 0.89 and OA = 0.94). Our study represents an innovative approach by integrating ViT with 8 m multiband composite GF-6 imagery for precise identification and analysis of short-term cropland abandonment patterns, marking a distinct contribution compared to previous research. Moreover, our findings have broader implications for effective land use management, resource optimization, and addressing complex challenges in the field.