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

Mannan Karim (), Jiqiu Deng (), Muhammad Ayoub, Wuzhou Dong, Baoyi Zhang, Muhammad Shahzad Yousaf, Yasir Ali Bhutto and Muhammad Ishfaque
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Mannan Karim: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Jiqiu Deng: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Muhammad Ayoub: School of Computer Science and Engineering, Central South University, Changsha 336017, China
Wuzhou Dong: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Baoyi Zhang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Muhammad Shahzad Yousaf: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Yasir Ali Bhutto: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Muhammad Ishfaque: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Land, 2023, vol. 12, issue 10, 1-24

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.

Keywords: cropland abandonment; vegetation indices (VIs); Vision Transformer (ViT); roadside greenways; Gaofen-6 (GF-6); excavation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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