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Extraction of mineralization alteration information in Nanling-Xuancheng ore district based on Sentinel-2 data

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基于 Sentinel-2 卫星数据的南陵—宣城矿集区矿化蚀变信息提取

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Sept.2023, Vol.46 No.9: 1254-1262

DOI: 10.3969/j.issn.1003-5060.2023.09.016

Authors

YIN Mengjie $ ^{1} $, ZHAO Ping $ ^{1,2} $, ZHU Cuicui $ ^{1} $, SHEN Pengju $ ^{1} $, ZHAO Yuejiao $ ^{1} $

(1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China; 2. Institute of Spatial Information Intelligent Analysis and Application, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: The Nanling-Xuancheng ore district in the Middle-Lower Yangtze River Valley Metallogenic Belt (MLYB) was selected as the research object. Through the analysis of the corresponding relationship between the Sentinel-2 data bands and the spectral characteristics of the main alteration minerals in the study area, and on the basis of the removal of disturbance information such as vegetation, water and buildings, the information of iron, Al—OH, Mg—OH and carbonate anomalies was extracted using the principal component analysis (PCA) and band ratio method. The results of data extraction were compared with the results which were extracted from Landsat 8 remote sensing data extraction. The result shows that the Sentinel-2 data has higher spatial resolution and is less affected by mixed pixel, it is more advantageous for information extraction in areas with high vegetation coverage. Its data can retain information more effectively in the process of disturbance information removal. The correlation ratios of alteration anomaly information extracted from the Sentinel-2 data and Landsat 8 data to ore occurrence are 69.14% and 47.43%, respectively, which means that the accuracy of alteration information extraction from Sentinel-2 data is better than that from Landsat 8 data. This study

Keywords: Sentinel-2 data; Landsat 8 data; alteration information; principal component analysis (PCA); the Middle-Lower Yangtze River Valley Metallogenic Belt (MLYB)

作者信息

殷梦杰 $ ^{1} $，赵萍 $ ^{1,2} $，朱翠翠 $ ^{1} $，申鹏举 $ ^{1} $，赵月娇 $ ^{1} $

(1. 合肥工业大学资源与环境工程学院，安徽合肥 230009；2. 合肥工业大学空间信息智能分析与应用研究所，安徽合肥 230009)

摘要和关键词

摘要: 文章以长江中下游成矿带的南陵—宣城矿集区为研究区, 分析 Sentinel-2 卫星数据波段和研究区主要蚀变矿物光谱特征对应关系, 在对植被、水体、建筑物等干扰信息去除的基础上, 使用主成分分析 (principal component analysis, PCA) 法和波段比值法进行铁染异常、Al—OH 异常、Mg—OH 异常及碳酸盐异常信息提取, 并通过与研究区已知矿点的叠加分析, 与 Landsat 8 卫星遥感数据提取结果对比。结果表明: 在植被覆盖度较高区域, Sentinel-2 卫星数据具有更高的空间分辨率, 受混合像元影响更小, 对于高密度植被信息提取具有较大优势, 在干扰信息去除的过程中保留的有效信息更多; 从 Sentinel-2、Landsat 8 卫星数据提取的蚀变异常信息与已知矿点的相关比率分别为 69.14%、47.43%, Sentinel-2 卫星数据蚀变提取精度优于 Landsat 8 卫星数据。研究结果可为植被覆盖度较高区域矿产资源遥感调查提供参考, 为南陵—宣城矿集区找矿预测提供依据。

关键词: Sentinel-2 卫星数据；Landsat 8 卫星数据；蚀变信息；主成分分析（PCA）法；长江中下游成矿带 中图分类号：P627 文献标志码：A 文章编号：1003-5060（2023）09-1254-09

Funding

国家重点研发计划资助项目（2016YFC0600209）；安徽省大学生创新训练计划资助项目（S202010359211）

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