Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2024-07-10 DOI:10.1016/j.rsase.2024.101294
M. Monisha , M. Muthukumar , V.J. Rajesh
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Abstract

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

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印度 SGT 地区 Devanur 和 Manamedu 辉绿岩复合体分析:利用遥感技术和实验室光谱特征调查进行详细研究
这项研究利用先进的 ASTER 和 Sentinel-2A 卫星图像,对南部中央剪切带(CSZ)的 Devanur 和 Manamedu 蛇绿岩复合体进行了详细的岩性测绘。主要重点是 Manamedu 蛇绿岩复合体 (MOC) 和 Devanur 蛇绿岩复合体 (DOC)。利用色彩合成、主成分分析(PCA)和最小噪声分数(MNF)等图像增强技术来区分各种岩石类型。根据 PCA 和 MNF 输出得出的 RGB 波段组合显示了对岩石单元的有效区分。在 ASTER 和 Sentinel-2A 图像上采用了光谱角度绘图仪 (SAM) 和支持向量机 (SVM) 分类方法,得出的岩性分类与印度地质调查局 (GSI) 和其他研究的现有地图非常吻合,验证了研究结果的准确性。此外,还使用 ASD FieldSpec Pro® 分光辐射计对 10 个岩石样本进行了实验室光谱特征研究,提供了 350 纳米到 2500 纳米的反射光谱。这些光谱,特别是去除连续面的反射率,显示了诊断性的吸收特征,并得到了地球化学分析的证实。详细分析研究了元素组成和主要矿物如何影响吸收带。使用 XRF 方法确定了 DOC 和 MOC 样品的主要氧化物地球化学成分。这项研究的目的是通过遥感和光谱特征分析来确定 DOC 和 MOC 的特征。与 ASTER 相比,Sentinel-2A 数据在岩性判别方面更为有效,其光谱特征显示了铁(Fe)和镁(Mg)含量的存在。值得注意的是,与实地调查相比,Sentinel-2A MNF + DEM 数据的 SVM 分类总体准确率超过 90%。这项研究强调了处理 ASTER 和 Sentinel-2A 卫星图像的 VNIR 和 SWIR 波段以及 DEM 数据和地面勘测对于绘制 CSZ 的 DOC 和 MOC 区域的黑云母-超黑云母岩石图的有效性。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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