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The demand for iron ore has increased recently and employing geochemical and hyperspectral remote sensing techniques for discovering new ore and mineral resources have primarily been concentrated on the economic phases. The present study aimed to characterize the hyperspectral spectral signatures of iron ores of field samples to map the deposits that occurred in the Tirthamalai hill region, which lies in the parts of Harur Taluk, Dharmapuri district of Tamil Nadu state, India The measurement and study of spectral signatures of the different samples of the deposits showed strong spectral absorptions near 500 nm, 900 nm and 2400 nm wavelength regions and were confirmed with the Fourier Transform Infrared (FTIR) spectroscopy method. The spectral absorption characteristics of the samples were evaluated by the study of the physical, optical, and chemical characteristics of the samples. The study of hyperspectral and FTIR spectral signatures with petrographic and major chemical elements revealed the best absorption characteristics of the iron ore deposits of the study region and can be used elsewhere in the world. This report presents preliminary findings on the use of hyperspectral imaging to characterize iron ore. The mineralogical products produced from hyperspectral images may improve in situ grade control on an iron ore mine face. It will be extremely useful for businesses in measuring large numbers of commodities quickly and objectively. [ABSTRACT FROM AUTHOR]

• Comparative studies on the basis of presence and absence of minerals. • Discrimination of the mineral body from the settlements. • Pyrrhotite and limonite (iron ore) minerals have been traced. • Mixture Tuned Matched Filtering algorithm produces a better mineral map. Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms. [ABSTRACT FROM AUTHOR]

The present article analyses the accuracy of application of higher-order nonlinear interaction models on hyperspectral data to identify mangrove mixtures present in the Sunderbans Delta - a World Heritage Site in West Bengal, India. It is observed that intra-species interaction between similar mangrove species (interaction between the same type of end-members) in a homogeneous mangrove stand is more accurately modelled by the linear-quadratic model and hence results in more accurate fractional abundance estimations after unmixing when compared with linear-unmixing models. Specifically, we observe that quadratic models provide more accurate estimates than linear and bilinear models for the study area (Henry Island of Sunderbans), which is mostly dominated by pure and mixed mangrove species of Avicennia marina, Excoecaria agallocha, Avicennia alba, Phoenix paludosa, Avicennia officinalis, Ceriops decandra, Bruguiera cylindrica and Aegialitis. In this study, the quadratic nonlinear model successfully characterizes the interaction of endmember mixtures comprising E. agallocha, A. officinalis, B. cylindrica and A. alba in the study area. [ABSTRACT FROM AUTHOR]

This study aims at discriminating eight mangrove species ofRhizophoraceaefamily of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (chl-a) retrieved using the MERIS CoastColour (CC) algorithm was evaluated for the coastal waters of the west coast of India, against in situ observations made as part of the Satellite Coastal and Oceanographic Research (SATCORE) programme. These observations include profiles of surface solar irradiance (Es) along with those of upwelling radiance and downwelling irradiance, measured using hyperspectral radiometry. Chl-a was also estimated from water samples. Furthermore, remote-sensing reflectance (Rrs) and chl-a were retrieved from MODIS-Aqua using the OC3M algorithm, and from MERIS using the OC4E algorithm. In addition, to understand the long-term seasonal variability, chl-a retrieved from the MERIS-CC algorithm was overlaid on monthly mean chl-a time series data from MODIS. Comparison of chl-a using MERIS-CC to that measured in situ showed wide scatter around the linear trend line. We observed that chl-a from MERIS-CC was underestimated for two-thirds of the observations, whereas with MODIS and MERIS it was 51% and 44%, respectively. Statistical analysis showed an improved performance in chl-a retrieval using the operational OC4E algorithm as compared to that of MERIS-CC. The time series analysis showed a good match between in situ chl-a and that derived from MODIS using the OC3M algorithm, whereas the MERIS-CC algorithm showed inconsistency in match-up with regard to both magnitude and trend. This inconsistency was more prominent during the low-chl-a scenario during the northern winter. We infer that algorithms such as OC4E and OC3M that use bands from the blue and green regions of the spectrum offer better chlorophyll retrieval in high-TSM or -CDOM concentration waters in comparison with CoastColour, which uses all bands across the spectrum. [ABSTRACT FROM AUTHOR]

Development of a spectral library is a prerequisite for the higher order classification of satellite data and hyperspectral image analysis to map any ecosystem with rich diversity. In this study, sampling methodology, collection of field and laboratory spectral signatures and post-processing methodologies were investigated for developing an exclusive spectral library of mangrove species using hyperspectral spectroscopic techniques. Canopy level field spectra and leaf level laboratory spectra were collected for 34 species (25 true and 9 associated mangroves) from two different mangrove ecosystems of the Indian east coast. Post-processing steps such as removal of water vapour absorption bands, correction of drifts which occur due to the thermal properties of the instrument during data collection and smoothing of spectra for its further utilisation were applied on collected spectra. The processed spectra were then compiled as spectral library. [ABSTRACT FROM AUTHOR]

Here we discuss hyperspectral imaging techniques and present an overview of a number of systems that have been developed by various space agencies. We also present the evolution of these systems and the progress made by the Indian space programme in this field. [ABSTRACT FROM AUTHOR]

This paper reports the results of a study to differentiate iron ores in terms of their grades, using the hyperspectral (EO-1 Hyperion) image data, covering a mineralized belt in the Noamundi area, eastern India. The study involves hyperspectral data collection, pre-processing (reduction of atmospheric and solar flux effects), generation of spectral curves from the image for the iron ore deposits, extraction of key spectral parameters and linear spectral unmixing for mapping iron ore abundance. Spectral curves for iron ore deposits extracted from the Hyperion image pixels exhibit strong absorption at 850–900 nm and 2150–2250 nm wavelengths, which is typical of iron ores. The strength of the absorption features in the continuum removed spectra varies spatially in the image around the mining areas, indicating differences in composition/grade of the iron ores. Spectral parameters such as the depth, width, area and wavelength position of the absorption features, derived from image spectra in the 850–900 nm and 2150–2250 nm regions, correlate well with the concentration of iron-oxide and alumina (gangue) in the ore samples obtained from the mine face. Well defined correlations are evident between the concentration of iron oxide and (i) the depth of NIR absorption feature ( R 2 = 0.883); (ii) the width of NIR absorption feature ( R 2 = 0.912); and (iii) the area of the NIR absorption feature and ( R 2 = 0.882). Further, the linear spectral unmixing resulted in an iron ore abundance map which, in conjunction with the image- and laboratory-spectra, helped in assessing the grades of iron ores in the study area. Thus, this study demonstrates the feasibility of discriminating grades of iron ores based on spectral information derived from spaceborne hyperspectral imagery. [ABSTRACT FROM AUTHOR]

This paper present the results of a study made to assess the grades of iron ore deposits in parts of Noamundi area using the hyperspectral image data (EO-1 Hyperion). The study involves hyperspectral image data, preprocessing (removal of atmospheric effects), spectral curve generation from the image, extraction of certain spectral parameters and spectral data analysis. Atmospheric correction was carried out by using algorithms involving atmospheric analysis of spectral hyper-cubes and Quick Atmospheric Correction modules. Spectral curves generated for the pixels that contain iron ores resemble the spectral signature of iron ores, i.e., very strong absorption at 850-900nm region is observed. The strength of the absorption varies spatially at different pixels. Hence, spectral parameters such as radius of curvature of NIR absorption trough (750-1000nm), position of NIR absorption trough and distance from a reference(100% reflectance) line have been derived for all the image-derived spectra and were compared with the concentration of iron in the ore samples of mines. Empirical models were generated by relating the image-derived spectral parameters of iron ore locations and geochemistry of the samples collected from the same location which shows a reasonable match. Strong negative correlation is seen between the radius of curvature of the NIR absorption trough and concentration of iron with a R value of 0.809. The position of the NIR absorption trough is seen to shift towards longer wavelengths with decreasing iron content and the distance from the reference line increases with increasing iron content. Thus, this study indicates that it is feasible to discriminate the grades of iron ores from the spectral parameters derived from Hyperspectral satellite images. [ABSTRACT FROM AUTHOR]

The aim of the present work is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. The paper deals with the classification of an Earth Observing-1 Hyperion image of the mangrove area of Bhitarkanika National Park, Odisha, India into mangrove floristic composition classes. Out of 196 calibrated bands of the image, 56 were found to be highly uncorrelated and contained maximum information; therefore, these 56 bands were used for classification. Amongst the three full-pixel classifiers tested in the investigation, Support Vector Machine produced the best results in terms of training pixel accuracy with overall precision of 96.85 %, in comparison to about 70-72.0 % for the other two classifiers. A total of five mangrove classes were obtained - pure or dominant class of Heritiera fomes, mixed class of H. fomes, mixed Excoecaria agallocha with Avicennia officinalis, mixed class of fringing Sonneratia apetala and class comprising of mangrove associates with salt resistant grasses. Post-classification field data also established the same. Pure or dominant classes of H. fomes occupied more than 50 % of the total mangrove vegetation in the forest blocks of the National Park. Spectral profile matching of image pixels with that of in- situ collected canopy reflectance profile revealed good match for H. fomes (pure or dominant stands). Red-edge index, which was a preferred criterion for matching was notably correlated in case of H. fomes and E. agallocha. The outcomes indicated the efficacy of hyperspectral canopy reflectance library for such kind of work. It is hoped that the methodology presented in this paper will prove to be useful and may be followed for producing mangrove floristic maps at finer levels. [ABSTRACT FROM AUTHOR]

Oceansat-2 mission is envisaged to provide continuity of services for the operational users of Ocean Colour Monitor (OCM) data as well as enhance the application potential in other areas. It was successfully launched by PSLV-C14 flight on 23 September 2009 from Shriharikota launch pad. It carried three payload instruments, viz. OCM 2, a Ku-band scatterometer (OSCAT) and a Radio Occultation Sounder for Atmosphere (ROSA) which has been developed by the Italian Space Agency. A controlled calibration and validation site is envisaged in southeast Arabian Sea to monitor the OCM 2 sensor stability owing to its post-launch performance and ageing-related problems in orbit. This site measures hyper-spectral optical radiances (both in air and underwater), meteorological parameters and CIMEL 318 sun-photometer for sun and sky radiation in automated mode. All these datasets are received from the site via satellite communication. These measurements are analysed along with top-of-the-atmosphere (TOA) radiance measured by OCM 2 sensor. The OCM 2 sensor performance is monitored against the radiance computed at TOA through a forward radiative transfer model for the period spanning from 23 September 2009 to 24 April 2010. [ABSTRACT FROM AUTHOR]