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Journal ArticleDOI

Enhancement of limestone mineral identification using Hyperion imagery: a case study from Tirunelveli District, Tamil Nadu, South India

01 Jan 2019-Arabian Journal of Geosciences (Springer Berlin Heidelberg)-Vol. 12, Iss: 2, pp 1-14
TL;DR: In this article, the spectral reflectance of limestone is characterized using analytical spectral devices like a field spectroradiometer, and the acquired reflectance image spectra are compared with the spectral libraries of USGS, JPL, and field spectra.
Abstract: Hyperspectral remote sensing consolidates imaging and spectroscopy in a solitary system which frequently comprises big datasets and necessitates the novel processing methods. In the present study, Cheranmadevi Block of Tirunelveli District in Tamil Nadu is selected to extract the abundant limestone mineral. Hyperion is one of the freely available hyperspectral imagery containing 242 spectral bands with 10-nm intervals in the wavelength between 400 and 2500 nm. The main objectives of the present research work are to enhance the imagery visualization, end member extraction, and classification, and estimate the abundant limestone quantity by removing the striping error in Hyperion imagery. The scanning electron microscope with energy-dispersive X-ray spectroscopy analysis is performed to identify the chemical composition of limestone mineral. The spectral reflectance of limestone is characterized using analytical spectral devices like a field spectroradiometer. Limestone has deep absorption in the short-wave infrared region (1900–2500 nm) around 2320–2340 nm due to their calcite composition (CaCO3). The feature extraction in Hyperion data is performed using various preprocessing steps like bad bands removal, vertical strip removal, and radiance and reflectance creation. To improve the classification accuracy, vertical strip removal process is performed using a local destriping algorithm. The absolute reflectance is achieved by the atmospheric correction module using Fast Line-of-sight Atmospheric Analysis of Hypercubes. The acquired reflectance image spectra are compared with the spectral libraries of USGS, JPL, and field spectra. Destriping enhances qualities of Hyperion data interims of the spectral profile, radiance, reflectance, and data reduction methods. The present research work focused on the local destriping algorithm to increase the quality and quantity of limestone deposit extraction.
Citations
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Journal ArticleDOI
01 Nov 2021-Geoderma
TL;DR: In this paper, the authors evaluated the potential of Vis-NIR spectroscopy in predicting the threshold friction velocity (TFV) of the soil, which is not always easy to measure, especially on regional and global scales.

13 citations

References
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Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of the vegetation.
Abstract: Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.

800 citations


"Enhancement of limestone mineral id..." refers background in this paper

  • ...Hyperion has 30 m spatial resolution and continuous 242 bands in the wavelength region between 400 and 2500 nm at 10-nm interval (Adam et al. 2010)....

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Journal ArticleDOI
TL;DR: Initial results over several sites with established ground truth and years of airborne hyperspectral data show that Hyperion data from the shortwave infrared spectrometer can be used to produce useful geologic (mineralogic) information, but also indicate that SNR improvements are required for future spaceborne sensors to allow the same level of mapping that is currently possible from airborne sensors such as AVIRIS.
Abstract: Airborne hyperspectral data have been available to researchers since the early 1980s and their use for geologic applications is well documented. The launch of the National Aeronautics and Space Administration Earth Observing 1 Hyperion sensor in November 2000 marked the establishment of a test bed for spaceborne hyperspectral capabilities. Hyperion covers the 0.4-2.5-/spl mu/m range with 242 spectral bands at approximately 10-nm spectral resolution and 30-m spatial resolution. Analytical Imaging and Geophysics LLC and the Commonwealth Scientific and Industrial Research Organisation have been involved in efforts to evaluate, validate, and demonstrate Hyperions's utility for geologic mapping in a variety of sites in the United States and around the world. Initial results over several sites with established ground truth and years of airborne hyperspectral data show that Hyperion data from the shortwave infrared spectrometer can be used to produce useful geologic (mineralogic) information. Minerals mapped include carbonates, chlorite, epidote, kaolinite, alunite, buddingtonite, muscovite, hydrothermal silica, and zeolite. Hyperion data collected under optimum conditions (summer season, bright targets, well-exposed geology) indicate that Hyperion data meet prelaunch specifications and allow subtle distinctions such as determining the difference between calcite and dolomite and mapping solid solution differences in micas caused by substitution in octahedral molecular sites. Comparison of airborne hyperspectral data [from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)] to the Hyperion data establishes that Hyperion provides similar basic mineralogic information, with the principal limitation being limited mapping of fine spectral detail under less-than-optimum acquisition conditions (winter season, dark targets) based on lower signal-to-noise ratios. Case histories demonstrate the analysis methodologies and level of information available from the Hyperion data. They also show the viability of Hyperion as a means of extending hyperspectral mineral mapping to areas not accessible to aircraft sensors. The analysis results demonstrate that spaceborne hyperspectral sensors can produce useful mineralogic information, but also indicate that SNR improvements are required for future spaceborne sensors to allow the same level of mapping that is currently possible from airborne sensors such as AVIRIS.

659 citations


"Enhancement of limestone mineral id..." refers background in this paper

  • ...The spectral range 0.4–2.5 μm provides abundant information about many important earth-surface minerals (Kruse et al. 2003)....

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Journal ArticleDOI
TL;DR: In this paper, a set of vegetation indices belonging to three classes (normalized difference vegetation index (NDVI), modified simple ratio (MSR) index and the modified chlorophyll absorption ratio index (MCARI, TCARI) were tested using the PROSPECT and SAIL models.

608 citations


"Enhancement of limestone mineral id..." refers background in this paper

  • ...8 Spectral profile plot prior to eliminating the bad bands field spectrum (Wu et al. 2008)....

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Journal ArticleDOI
TL;DR: Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina to determine the most effective bands for forest LAI estimation.
Abstract: Field spectrometer data and leaf area index (LAI) measurements were collected on the same day as the Earth Observing 1 satellite overpass for a study site in the Patagonia region of Argentina. We first simulated the total at-sensor radiances using MODTRAN 4 for atmospheric correction. Then ground spectroradiometric measurements were used to improve the retrieved reflectance for each pixel on the Hyperion image. Using the improved pixel-based surface reflectance spectra, 12 two-band "vegetation indices (VIs)" were constructed using all available 168 Hyperion bands. Finally, we evaluated the correlation of each possible vegetation index with LAI measurements to determine the most effective bands for forest LAI estimation. The experimental results indicate that most of the important hyperspectral bands with high R/sup 2/ are related to bands in the shortwave infrared (SWIR) region and some in the near-infrared (NIR) region. The bands are centered near 820, 1040, 1200, 1250, 1650, 2100, and 2260 nm with bandwidths ranging from 10-300 nm. It is notable that the originally defined VIs that use red and NIR bands did not produce higher correlation with LAI than VIs constructed with bands in SWIR and NIR regions.

346 citations


"Enhancement of limestone mineral id..." refers methods in this paper

  • ...The spectral analyst exploits different methods such as binary encoding, spectral angle mapper, and spectral feature fitting to identify the probability to determine the limestone spectra (Gong et al. 2003)....

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Journal ArticleDOI
TL;DR: Comparison of SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California finds no difference was found between classifiers when using object-based training samples, but SVM outperformed RF when additional training samples were used.
Abstract: The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

128 citations


"Enhancement of limestone mineral id..." refers background in this paper

  • ...Hyperion imagery contains 242 spectral bands: out of 242, 163 bands are available in calibrating conditions (Ballanti et al. 2016)....

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