High-Resolution Dynamic Monitoring of Rocky Desertification of Agricultural Land Based on Spatio-Temporal Fusion

Xin Zhao, Zhongfa Zhou (), Guijie Wu, Yangyang Long, Jiancheng Luo, Xingxin Huang, Jing Chen and Tianjun Wu
Additional contact information
Xin Zhao: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Zhongfa Zhou: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Guijie Wu: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Yangyang Long: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Jiancheng Luo: Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xingxin Huang: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Jing Chen: School of Geography and Environmental Science/School of Karst Science, Guizhou Normal University, Guiyang 550025, China
Tianjun Wu: School of Land Engineering, Chang’an University, Xi’an 710064, China

Land, 2024, vol. 13, issue 12, 1-22

Abstract: The current research on rocky desertification primarily prioritizes large-scale surveillance, with minimal attention given to internal agricultural areas. This study offers a comprehensive framework for bedrock extraction in agricultural areas, employing spatial constraints and spatio-temporal fusion methodologies. Utilizing the high resolution and capabilities of Gaofen-2 imagery, we first delineate agricultural land, use these boundaries as spatial constraints to compute the agricultural land bedrock response Index (ABRI), and apply the spatial and temporal adaptive reflectance fusion model (STARFM) to achieve spatio-temporal fusion of Gaofen-2 imagery and Sentinel-2 imagery from multiple time periods, resulting in a high-spatio-temporal-resolution bedrock discrimination index (ABRI*) for analysis. This work demonstrates the pronounced rocky desertification phenomenon in the agricultural land in the study area. The ABRI* effectively captures this phenomenon, with the classification accuracy for the bedrock, based on the ABRI* derived from Gaofen-2 imagery, reaching 0.86. The bedrock exposure area in the farmland showed a decreasing trend from 2019 to 2021, a significant increase from 2021 to 2022, and a gradual decline from 2022 to 2024. Cultivation activities have a significant impact on rocky desertification within agricultural land. The ABRI significantly enhances the capabilities for the dynamic monitoring of rocky desertification in agricultural areas, providing data support for the management of specialized farmland. For vulnerable areas, timely adjustments to planting schemes and the prioritization of intervention measures such as soil conservation, vegetation restoration, and water resource management could help to improve the resilience and stability of agriculture, particularly in karst regions.

Keywords: agricultural land; rocky desertification; spatio-temporal fusion; remote; sensing; karst regions (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:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/12/2173/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/12/2173/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:12:p:2173-:d:1543202

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().