Abstract

Detailed investigation on texture and mineralogy of beach sediments helps to understand their nature of deposition and potential targets of strategic mineral deposits. The beach sediments from the coast of Thiruvananthapuram, the southernmost district of Kerala, India, have been studied to understand the variation in grain size by using the spectral indices as derived from the visible-NIR-TIR bands of Landsat and ASTER remote sensing data. Further, an attempt has been made to map the distribution of strategic minerals present in beach sands using standardized hyperspectral analysis techniques. The grain size shows a remarkable variation from medium sand to fine sand. The THM (Total Heavy Minerals) content was estimated to about 80.04% and 52.33% along the coast of Kovalam and Varkala, respectively. The ilmenite mineral predominantly exists in these areas, followed by monazite, sillimanite, rutile, zircon, garnet, leucoxene, and Kyanite. The hyperspectral analysis extracts two endmembers of ilmenite and light minerals from the Landsat and ASTER imagery, which could be successfully, mapped using the SAM classification algorithm. The satellite-derived map showing texture and mineralogy has been validated with the results of laboratory analysis and shows strong correlation almost in all locations. This study illustrates the potential use of satellite remote sensing techniques for the mapping of natural resources, especially mineral resources.

摘要

详细调查海滩沉积物的质地和矿物学有助于了解其沉积性质和战略矿床的潜在目标。已经研究了来自印度喀拉拉邦最南端的蒂鲁文南特布勒姆海岸的海滩沉积物，通过使用从 Landsat 和 ASTER 遥感数据的可见-NIR-TIR 波段得出的光谱指数来了解粒度的变化。此外，已经尝试使用标准化的高光谱分析技术绘制海滩沙中存在的战略矿物的分布图。粒度从中砂到细砂有显着变化。科瓦兰和瓦卡拉沿岸的 THM（总重矿物）含量估计分别约为 80.04% 和 52.33%。钛铁矿主要存在于这些地区，其次是独居石、硅线石、金红石、锆石、石榴石、白榴石和蓝晶石。高光谱分析从 Landsat 和 ASTER 图像中提取了钛铁矿和轻质矿物的两个端元，可以使用 SAM 分类算法成功映射。显示纹理和矿物学的卫星衍生地图已通过实验室分析结果得到验证，并且几乎在所有位置都显示出强烈的相关性。这项研究说明了卫星遥感技术在自然资源特别是矿产资源测绘中的潜在用途。高光谱分析从 Landsat 和 ASTER 图像中提取了钛铁矿和轻质矿物的两个端元，可以使用 SAM 分类算法成功映射。显示纹理和矿物学的卫星衍生地图已通过实验室分析结果得到验证，并且几乎在所有位置都显示出强烈的相关性。这项研究说明了卫星遥感技术在自然资源特别是矿产资源测绘中的潜在用途。高光谱分析从 Landsat 和 ASTER 图像中提取了钛铁矿和轻质矿物的两个端元，可以使用 SAM 分类算法成功映射。显示纹理和矿物学的卫星衍生地图已通过实验室分析结果得到验证，并且几乎在所有位置都显示出强烈的相关性。这项研究说明了卫星遥感技术在自然资源特别是矿产资源测绘中的潜在用途。