Next Article in Journal
Fusing Semantic Features with Gaussian Splatting for Enhanced Satellite Image Surface Reconstruction
Previous Article in Journal
PM2.5 Concentration Estimation in Single Hazy Images Using Luminance–Spatial Decoupling
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 18
Issue 10
10.3390/rs18101562
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Monitoring Abandoned Cropland in Fragmented Mountainous Landscapes Based on the ML-LandTrendr Framework

by
Ying Wang
1,2,†,
Zhongyuan Xie
3,†,
Huaiyong Shao
1,*,
Jichong Han
1,
Xiaofei Sun
1,
Long Ling
1,
Jiamei Long
1,
Ying Lin
1 and
Liangliang Zhang
1
1
College of Geography and Planning, Chengdu University of Technology, East 3rd Road, Erxianqiao, Chenghua District, Chengdu 610059, China
2
Chengdu Technological University, No. 1, Section 2, Zhongxin Avenue, Pidu District, Chengdu 611730, China
3
Sichuan Land Consolidation and Rehabilitation Center, No. 189, Wanfeng Road, Wuhou District, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2026, 18(10), 1562; https://doi.org/10.3390/rs18101562
Submission received: 24 March 2026 / Revised: 10 May 2026 / Accepted: 12 May 2026 / Published: 13 May 2026
(This article belongs to the Special Issue AI and Remote Sensing Applications for Sustainable Land Use and Management)
Versions Notes

Abstract

Cropland abandonment is increasing in the upper and middle Yangtze River Basin due to complex terrain, urbanization, and labor migration. This threatens regional food security. To address the challenge of monitoring abandonment in fragmented hilly areas, we developed a framework. We integrated machine learning with time-series analysis. We mapped cropland probability using multi-source remote sensing data, random forest, and kernel density estimation, then applied LandTrendr to detect land-use changes and track the spatiotemporal evolution of abandonment from 2000 to 2022. Next, we combined Geodetector and linear regression to identify driving factors. The results show that abandoned cropland exhibited an increasing trend from 2000 to 2010, with an average annual growth rate of 20.4%. From 2010 to 2013, the area of abandoned cropland declined rapidly, decreasing by 44.6%. Between 2013 and 2022, abandoned cropland decreased steadily, with an average annual reduction rate of 24.7%. Spatially, abandonment was clustered in the central mountains and southern hills. Key drivers included distance to towns (DtT), total grain output (GTO), and GDP. Our approach supports cropland management and rural revitalization in regions with complex terrain.
Keywords: cropland abandonment; LandTrendr; machine learning; Geodetector; mountainous area cropland abandonment; LandTrendr; machine learning; Geodetector; mountainous area

Share and Cite

MDPI and ACS Style

Wang, Y.; Xie, Z.; Shao, H.; Han, J.; Sun, X.; Ling, L.; Long, J.; Lin, Y.; Zhang, L. Monitoring Abandoned Cropland in Fragmented Mountainous Landscapes Based on the ML-LandTrendr Framework. Remote Sens. 2026, 18, 1562. https://doi.org/10.3390/rs18101562

AMA Style

Wang Y, Xie Z, Shao H, Han J, Sun X, Ling L, Long J, Lin Y, Zhang L. Monitoring Abandoned Cropland in Fragmented Mountainous Landscapes Based on the ML-LandTrendr Framework. Remote Sensing. 2026; 18(10):1562. https://doi.org/10.3390/rs18101562

Chicago/Turabian Style

Wang, Ying, Zhongyuan Xie, Huaiyong Shao, Jichong Han, Xiaofei Sun, Long Ling, Jiamei Long, Ying Lin, and Liangliang Zhang. 2026. "Monitoring Abandoned Cropland in Fragmented Mountainous Landscapes Based on the ML-LandTrendr Framework" Remote Sensing 18, no. 10: 1562. https://doi.org/10.3390/rs18101562

APA Style

Wang, Y., Xie, Z., Shao, H., Han, J., Sun, X., Ling, L., Long, J., Lin, Y., & Zhang, L. (2026). Monitoring Abandoned Cropland in Fragmented Mountainous Landscapes Based on the ML-LandTrendr Framework. Remote Sensing, 18(10), 1562. https://doi.org/10.3390/rs18101562

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop