Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

Yali Wei (), Junjie Wen, Qunchao Zhou, Yan Zhang and Gaocheng Dong
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Yali Wei: College of Resource, Sichuan Agricultural University, Chengdu 611130, China
Junjie Wen: College of Resource, Sichuan Agricultural University, Chengdu 611130, China
Qunchao Zhou: College of Resource, Sichuan Agricultural University, Chengdu 611130, China
Yan Zhang: College of Resource, Sichuan Agricultural University, Chengdu 611130, China
Gaocheng Dong: College of Resource, Sichuan Agricultural University, Chengdu 611130, China

Land, 2024, vol. 13, issue 5, 1-25

Abstract: Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya’an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power.

Keywords: abandoned cropland; hilly; Google Earth Engine; Mingshan County; China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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