Author
Listed:
- Yan Li
(Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
Center for Spatial Information Technology, Yunnan Satellite Remote Sensing Technology Application Engineering Center, Kunming 650118, China)
- Xiping Yuan
(Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
School of Surveying and Information Engineering, West Yunnan University of Applied Sciences, Dali 671000, China)
- Shu Gan
(Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)
- Changsi Mu
(Geological Exploration Institute, 209 Geological Brigade of Nuclear Industry of Yunnan Province, Kunming 650032, China)
- Zhi Lin
(Center for Spatial Information Technology, Yunnan Satellite Remote Sensing Technology Application Engineering Center, Kunming 650118, China)
- Xiong Duan
(Sichuan Provincial Engineering Laboratory of Monitoring and Control for Soil Erosion in Dry Valley, School of Geographical Sciences, China West Normal University, Nanchong 637009, China)
- Yanyan Shao
(Center for Spatial Information Technology, Yunnan Satellite Remote Sensing Technology Application Engineering Center, Kunming 650118, China)
- Yanying Wang
(Center for Spatial Information Technology, Yunnan Satellite Remote Sensing Technology Application Engineering Center, Kunming 650118, China)
- Lin Hu
(Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)
Abstract
Carbonate-hosted clay-type lithium deposits have emerged as strategic resources critical to the global energy transition, yet their exploration faces the dual challenges of technical complexity and environmental sustainability. Traditional methods often entail extensive land disruption, particularly in ecologically sensitive ecosystems where vegetation coverage and weathered layers hinder mineral detection. This study presents a case study of the San Dan lithium deposit in central Yunnan, where we propose a hierarchical anomaly extraction and multidimensional weighted comprehensive analysis. This comprehensive method integrates multi-source data from GF-3 QPSI SAR, GF-5B hyperspectral, and Landsat-8 OLI datasets and is structured around two core parts, as follows: (1) Hierarchical Anomaly Extraction: Utilizing principal component analysis, this part extracts hydroxyl and iron-stained alteration anomalies. It further employs the spectral hourglass technique for the precise identification of lithium-rich minerals, such as montmorillonite and illite. Additionally, concealed structures are extracted using azimuth filtering and structural detection in radar remote sensing. (2) Multidimensional Weighted Comprehensive Analysis: This module applies reclassification, kernel density analysis, and normalization preprocessing to five informational layers—hydroxyl, iron staining, minerals, lithology, and structure. Dynamic weighting, informed by expert experience and experimental adjustments using the weighted weight-of-evidence method, delineates graded target areas. Three priority target areas were identified, with field validation conducted in the most promising area revealing Li 2 O contents ranging from 0.10% to 0.22%. This technical system, through the collaborative interpretation of multi-source data and quantitative decision-making processes, provides robust support for exploring carbonate-clay-type lithium deposits in central Yunnan. By promoting efficient, data-driven exploration and minimizing environmental disruption, it ensures that lithium extraction meets the growing demand while preserving ecological integrity, setting a benchmark for the sustainable exploration of clay-type lithium deposits worldwide.
Suggested Citation
Yan Li & Xiping Yuan & Shu Gan & Changsi Mu & Zhi Lin & Xiong Duan & Yanyan Shao & Yanying Wang & Lin Hu, 2025.
"Environmentally Sustainable Lithium Exploration: A Multi-Source Remote Sensing and Comprehensive Analysis Approach for Clay-Type Deposits in Central Yunnan, China,"
Sustainability, MDPI, vol. 17(8), pages 1-20, April.
Handle:
RePEc:gam:jsusta:v:17:y:2025:i:8:p:3732-:d:1638867
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