Presentation on theme: "Mineral Abundance Mapping Using Hyperion Dataset in Part of Udaipur Dist. Rajasthan, India Presented By Salaj.S.S PSNA College of Engineering and Technology."— Presentation transcript:
Mineral Abundance Mapping Using Hyperion Dataset in Part of Udaipur Dist. Rajasthan, India Presented By Salaj.S.S PSNA College of Engineering and Technology Dindigul, TamilNadu
Introduction Hyperspectral remote sensing, measuring hundreds of spectral bands from aircraft and satellite platforms, provides unique spatial/spectral datasets for analysis of surface mineralogy (Goetz et al., 1985; Kruse et al.,2003). Their use for geologic applications is well established (Goetz et al., 1985; Kruse et al., 1999,2003; Rowan and Mars, 2003). For the present study Hyperion, a hyperspectral imager on-board of EO-1 satellite is used contains 220 spectral bands ranging from nm and the spatial resolution is 30 meter per pixel, swath width is 7.5 km by 42 or 100 km area.
Comparison between Multispectral and Hyperspectral sensors
Study Area Hyperion data (FCC ) Part of Udaipur district – Aravalli Fold Belt, Rajasthan covering about sq. km Extent: 73˚ 33’ 25” E to 73˚42’53” E 24˚ 09’ 34” N to 24˚ 31’ 40” N The average height of the area is 598m from the sea level
Aims and Objectives The main aim of the study is to test the abilities of Hyperion data for mineral abundance mapping in the study area. The broad objectives are as follows Atmospheric Correction of Hyperion Data. Extraction of Endmembers from the image using advanced techniques like MNF, PPI etc. Mineralogical mapping using the various endmembers extracted from the image.
Data Used Hyperion image (Path : 148, Row : 43) L1R Image - Radiometrically corrected L1Gst – Radiometrically as well as geometrically and Topographically corrected Spectral Library(USGS) Geological Map of the study area Software Used ENVI 4.7 Arc GIS 9.3
Preprocessing Bad Column Removal Each bad column was replaced by average of previous and next column Before correction After correction
Atmospheric Correction using FLAASH Module
Sensor typeHyperion Pixel size30 Ground elevation.6 km Scene Centre Lat/Long24.6◦N,73.7◦E Visibility40 km Sensor altitude705km Flight date & Flight time19/01/2004,5:22:17 Atmospheric modelTropical Aerosol modelRural Water vapour retrieval1135nm Wavelength CalibrationYes Advanced parameters Output reflectance Scale factor10000 MODTRAN resolution15 cms-1 MODTRAN multi scattering modelScaled DISORT 8 Stream CO ppm
Geometric Correction Hyperion L1Gst image as base image and L1R image as warp image(Auto-image-to-image registration)
Dimensionality Reduction Minimum Noise fraction was used to reduce spectral dimensionality and redundancy Noise estimation from image Separating noise from image New uncorrelated 8 bands from 144original bands
Comparison of Geological map with MNF color composites MNF(R:2 G:3 B:6) Shelf Facies – Delwara Group and Debari Group Deep Sea Facies - Jarol Group and Lunavada Group with Ultramafics
Pixel Purity Index(PPI)
N-Dimensional Visualizer selecting the endmembers in n-D space. Pure pixels can be viewed in any angle by rotating the n-D scatterplot The clusters can be saved as ascii files Finalization of clusters after using spectral analyst
Endmember Identification Using Spectral Analyst Endmember identification using Spectral Angle Mapper and Spectral Feature Fitting with equal weightage Mineral with highest score was identified as material for a particular cluster Mapping Methods SAM (Spectral Angle Mapper) Mixture Tuned Matched Filtering(MTMF)
Post Classification End Member Collection Spectra Rule Classifier
Mineral Abundance Maps 1) Grossularite (Ca3Al2Si3O12) - Grossularite is especially characteristic of both thermally and regionally metamorphosed impure calcareous rocks. It also occurs in the rocks which have undergone calcium metamorphism. It may result from replacement of Wollastonite. It also occurs in association with serpentinite and has been described from highly metamorphosed layered complex possibly resulting from the alteration of anorthite.
2) Calcite (CaCO3) – Calcite is one of the most ubiquitous minerals, and in addition to being an important rockforming mineral in sedimentary environments, it also occurs in metamorphic and igneous rocks and is a common mineral of hydrothermal and secondary mineralization. In sedimentary rocks Calcite is the principal constituent of most limestones. It occurs both as a primary precipitate and in the form of fossil shells. Calcite is the stable form of CaCO3 and although approximately equal numbers of organisms make their shells of Calcite and aragonite (or, as for some of the mollusca, of both), the aragonite eventually undergoes recrystallization to calcite.
3) Pyrite (FeS2) - Pyrite is similar in appearance to chalcopyrite, pyrrhotite and marcasite, It can be distinguished from chalcopyrite since the latter mineral has a deeper yellow colour in reflected light, and is softer, being scratched by a knife, Pyrrhotite is bronze rather than brasscoloured, is also scratched by a knife and usually magnetic.
4) Andradite (Ca3(Fe+3, Ti)2Si3O12) - Typically occurs in contact or thermally metamorphosed impure calcareous sediments and particularly in the metamorphism and particularly metasomatic skarn deposits. This involves the addition of Fe2O2 and SiO2. If FeO is also introduced, hedenbergite may form in addition to andradite, and if insufficient silica is available magnetite may result giving the typical andradite-hedenbergite-magnetite skarn assemblage. Andradite also occurs as the result of metasomatism connected with the thermal metamorphism of calcic igneous rocks such as andesite
Conclusion Mineralogical mapping can be achieved using Hyperion data. Mapping is affected by topography and vegetation and ground in-situ conditions. False positives may be generated Limitations due to Standard spectral library. Need of Field spectral library for minerals is essential for accurate mapping.
References Kruse, F.A., J.W. Boardman And J.F. Huntington (2003): Evaluation and validation of hyperion for mineral mapping, IEEE Trans. Geosci. Remote Sensing, 41 (6), Kruse, F.A., J.W. Boardman And J.F. Huntington (1999), Fifteen years of hyperspectral data: Northern Grapevine Mountains, Nevada, in Proceedings of the 8th JPL Airborne Earth Science Workshop, Jet Propulsion Laboratory Publ , Rowan, L.C. And J.C. Mars (2003): Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Spectrometer (ASTER) data, Remote Sensing Environ., 84, S Sinha-Roy, G Malhotra, M Mohanty (1998), Geology of Rajasthan, Geological Society of India, 1st edition, 278 p. Deer W. A., Howie R. A. and Zussman J, 1966, Mineralogy, Longman Group Limited, 7 th edition, 528p.