Work

Lithological Mapping in High-Vegetation Areas Using Sentinel-2, Sentinel-1, and Digital Elevation Models

Year: 2025

Type: article

Abstract: The extraction of lithological information in areas with high vegetation coverage presents numerous challenges, particularly in identifying concealed lithological features. This study focuses on a typical high-vegetation coverage area in Taiwan Province, China, utilizing multi-source data from Sentinel-2, Sentinel-1, and DEM, and using the Random Forest algorithm for lithological mapping. The results demonstrate that the optimal combination of Sentinel-2 and DEM significantly enhances the classification performance, achieving an overall accuracy (OA) of 84.30% and a Kappa coefficient of 0.83 in the validation set. Geological conditions have specific limiting effects on ecosystems, as spectral features (such as B2 and NDBI) and topographic features (such as elevation) contribute significantly to the classification results. This study provides valuable reference information for lithological information extraction in areas with high vegetation coverage.

Source: Sensors

DOI: 10.3390/s25072136

Authors: Yansi Chen, Genyuan Liu, Zhihong Song, Ming Li, Minhua Wang, Shuang Wang

Institution: China Geological Survey

Language: English

Topic: Geochemistry and Geologic Mapping

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,137