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Vinod Kumar, K.

• Comparative Study of Two Meander Loops for their Planform in Hooghly River, West Bengal-Remote Sensing Based Approach

Abstract Views :165  |  PDF Views:2

Authors

K. vinod Kumar ¹, Bhattacharya Asis ¹

Affiliations
1 National Remote Sensing Agency, Department of Space, Balanagar, Hyderabad - 500 037, IN

Source

Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 67, No 6 (2006), Pagination: 777-782

Abstract

A comparative study on the planimetric changes of two meander loops in Hooghly river, West Bengal was carried out using remote sensing techniques. Multi date and multi sensor satellite data were used to map the changes. The lineament analysis of the basin was carried out to understand the morpho-tectonics relationship. The planirnetric meander geometry was measured from the satellite data in GIS environment Change detection study was carried out to understand the changes. It was seen that the Charchakundi Bisnupur meander loop behaved differently in comparison to the Diar-Balagachi meander loop in the same river system. The changes were attributed to the meander geometry controlled by lineaments. The latest data of 1991-Time period was taken since the oldest data of 1975-Time period had comparable spatial resolution with 1991 satellite data Landsat MSS of 1975-Time period had 80-Meter spatial resolution whereas Indian Remote Sensing data of 1991 time period (IRS- IA/IB) had 72-Meter resolution.

Keywords

Remote Sensing, GIS, Planform, Meander Geometry, Spatial Resolution Hooghly Never, West Bengal.

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• Remote Sensing of Delta Progradation in Mahanadi Delta, Orissa

Abstract Views :177  |  PDF Views:142

Authors

K. Vinod Kumar ¹, Asis Bhattacharya ¹

Affiliations
1 National Remote Sensing Agency, Balanagar, Hyderabad - 500037, IN

Source

Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 64, No 2 (2004), Pagination: 227-230

Abstract

Mahanadi Delta is a complex delta formed due to the coalescence of three sub-deltas. The northern portion of the delta i.e. Brahmani-Baitarani river mouth was monitored using IRS-1C LISS-3 satellite data. Barrier-Lagoon type of progradation was brought out by digitally enhancing the satellite data. Density slicing technique was employed to understand the sediment diffusion pattern. This was compared with the standard distribution pattern to understand the nature of dispersal mechanism near the river mouth here.

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• An Image Processing Approach for Converging ASTER-Derived Spectral Maps for Mapping Kolhan Limestone, Jharkhand, India

Abstract Views :263  |  PDF Views:99

Authors

Arindam Guha ¹, K. Vinod Kumar ¹, E. N. Dhananjaya Rao ², Reshma Parveen ³

Affiliations
1 National Remote Sensing Centre, Balanagar, Hyderabad 500 625, IN
2 Andhra University, Visakhapatnam 530 003, IN
3 Jharkhand Space Application Centre, Ranchi 834 004, IN

Source

Current Science, Vol 106, No 1 (2014), Pagination: 40-49

Abstract

In the present study, we have attempted the delineation of limestone using different spectral mapping algorithms in ASTER data. Each spectral mapping algorithm derives limestone exposure map independently. Although these spectral maps are broadly similar to each other, they are also different at places in terms of spatial disposition of limestone pixels. Therefore, an attempt is made to integrate the results of these spectral maps to derive an integrated map using minimum noise fraction (MNF) method. The first MNF image is the result of two cascaded principal component methods suitable for preserving complementary information derived from each spectral map. While implementing MNF, noise or non-coherent pixels occurring within a homogeneous patch of limestone are removed first using shift difference method, before attempting principal component analysis on input spectral maps for deriving composite spectral map of limestone exposures. The limestone exposure map is further validated based on spectral data and ancillary geological data.

Keywords

Limestone, Minimum Noise Fraction, Spectral Mapping, Image Processing.

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• Assessment of the Sunkoshi (Nepal) Landslide Using Multitemporal Satellite Images

Abstract Views :308  |  PDF Views:120

Authors

Priyom Roy ¹, Tapas R. Martha ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN

Source

Current Science, Vol 107, No 12 (2014), Pagination: 1961-1964

Abstract

No Abstract.

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• Potentials of Alternate Polarization of Envisat ASAR Data in Geological Mapping - A Case Study in Kurnool Group of Rocks, Andhra Pradesh

Abstract Views :162  |  PDF Views:0

Authors

Arindam Guha ¹, K. Vinod Kumar ¹, M. V. V. Kamaraju ¹, K. Babu Govindharaj ¹

Affiliations
1 Geosciences Division, National Remote Sensing Centre (Indian Space Research Organisation), Hyderabad - 500 625, IN

Source

Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 73, No 2 (2009), Pagination: 268-272

Abstract

The application of SAR data is a proven technology in geological studies but very few accounts are available in India, which can evaluate and demonstrate the utility of microwave signatures as an important tool for geological mapping. In this connection, the significance of polarization is an important parameter in enhancing geological elements. Present study reveals that the simple polarization composite prepared from different polarization channels can significantly aid the delineation of geological features as demonstrated from the Proterozoic metasedimentary sequences of Kurnool Group. The polarization colour composites reveal that different sedimentary units can be differentiated on the basis of variable back scattering return in different polarization channel. Further geological structures of regional importance can also be delineated in these colour composite images. Comparative analysis of different composite images with published geological maps, illustrates the capabilities of the microwave polarization in enhancing geological elements and how they can be used in updating geological data.

Keywords

Microwave Remote Sensing, Geological Mapping, Kurnool Group, Andhra Pradesh.

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• Simultaneous Estimation of Meclizine Hydrochloride and Nicotinic Acid in Pharmaceutical Dosage form by RP-HPLC Method

Abstract Views :212  |  PDF Views:136

Authors

M. Sudheer Reddy , P. Krishna Karthik , K. Vinod Kumar

Source

Asian Journal of Pharmaceutical Research and Health Care, Vol 5, No 2 (2013), Pagination: 73-80

Abstract

A simple, selective, rapid, precise and economical reverse phase high performance liquid chromatographic method has been developed for the simultaneous estimation of meclizine hydrochloride and nicotinic Acid from pharmaceutical formulation using C ₁₈ (25 cm x 4.6 mm i.d., 5μ ) column with a mobile phase consisting of Methanol: water (adjusted to pH 3.0 using orthophosphoric acid) in the ratio of 80:20 v/v. The detection wavelength was carried out at 231 nm. The linear regression analysis data for the linearity plot showed good linear relationship with correlation coefficient value for Meclizine Hydrochloride and Nicotinic Acid were R ² = 0.9991 and R ² = 0.9996 in the concentration range of 10-70 μg/ml, 5-35μg/ml respectively. The validation of method was carried out utilizing ICH-guidelines. The proposed method can be used for the estimation of these drugs in combined dosage forms.

Keywords

RP-HPLC, Meclizine Hydrochloride, Nicotinic Acid.

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• Buccal Patches-A Review

Abstract Views :182  |  PDF Views:0

Authors

D. Rajesh Kumar ¹, K. Vinod Kumar ¹, D. Sai Koteswar Sarma ¹, K. Sathish Kumar ¹, S. K. Shakir Ahmad ¹, M. Geethavani ¹

Affiliations
1 Siddartha Institute of Pharmaceutical Sciences, Jonnalagadda, Narsaraopeyt, Guntur (Dt), Andhrapradesh, IN

Source

Research Journal of Pharmaceutical Dosage Form and Technology, Vol 6, No 3 (2014), Pagination: 167-173

Abstract

Buccal delivery refers to the drug release which can occur when a dosage form is placed in the outer vestibule between the buccal mucosa and gingival. This route has various advantages includes bypass of first pass metabolism, better enzymatic flora for absorption and patient compliance. Buccal drug absorption occurs by passive diffusion of the nonionized species. Mucoadhesion may be affected by a number of factors, including hydrophilicity, molecular weight, cross-linking, swelling, pH, and the concentration of the active polymer. There are two types of buccal dosage form,they are matrix type and reservoir type. The basic components of buccal drug delivery system are drug substance, bio adhesive polymers, backing membrane permeation enhancers. There are two methods for preparation of buccal patches include solvent castng method and direct milling method. The evaluation tests include surface PH, thickness measurement, swelling study, thermal Analysis study, morphological characterizayion, water absorption capacity test, Ex-vivo bioadhesion test, in vitro drug release, permeation study, Ex-vivo mucoadhesion time and stability study in human saliva.

Keywords

First Pass Metabolism, PH, Matrix Type, Reservoir Type And Buccal Patches.

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• Buccal Patches-A Review

Abstract Views :224  |  PDF Views:0

Authors

D. Rajesh Kumar ¹, K. Vinod Kumar ¹, D. Sai Koteswar Sarma ¹, K. Sathish Kumar ¹, S. K. Shakir Ahmad ¹, M. Geethavani ¹

Affiliations
1 Siddartha Institute of Pharmaceutical Sciences, Jonnalagadda, Narsaraopeyt, Guntur (Dt), Andhrapradesh, IN

Source

Research Journal of Pharmaceutical Dosage Form and Technology, Vol 6, No 4 (2014), Pagination: 167-173

Abstract

Buccal delivery refers to the drug release which can occur when a dosage form is placed in the outer vestibule between the buccal mucosa and gingival. This route has various advantages includes bypass of first pass metabolism, better enzymatic flora for absorption and patient compliance. Buccal drug absorption occurs by passive diffusion of the nonionized species. Mucoadhesion may be affected by a number of factors, including hydrophilicity, molecular weight, cross-linking, swelling, pH, and the concentration of the active polymer. There are two types of buccal dosage form,they are matrix type and reservoir type. The basic components of buccal drug delivery system are drug substance, bio adhesive polymers, backing membrane permeation enhancers. There are two methods for preparation of buccal patches include solvent castng method and direct milling method. The evaluation tests include surface PH, thickness measurement, swelling study, thermal Analysis study, morphological characterizayion, water absorption capacity test, Ex-vivo bioadhesion test, in vitro drug release, permeation study, Ex-vivo mucoadhesion time and stability study in human saliva.

Keywords

First Pass Metabolism, PH, Matrix Type, Reservoir Type and Buccal Patches.

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• A Bird's-Eye View of Landslide Dammed Lakes in Zanskar Himalaya, India

Abstract Views :443  |  PDF Views:98

Authors

K. Babu Govindha Raj ¹, Tapas R. Martha ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, Remote Sensing Applications Area, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, IN

Source

Current Science, Vol 112, No 06 (2017), Pagination: 1109-1112

Abstract

The landslide lakes or dams are temporary lakes in the river valleys formed by landslide debris. Landslide dammed lakes and their outburst floods (LLOFs) are not uncommon in the Indian Himalaya. Breaching of such temporary lakes with huge amount of accumulated water and sediments can create devastating floods in the downstream areas.

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References

• Costa, J. E. and Schuster, R. L., Geol. Soc. Am. Bull., 1988, 100(7), 1054–1068.
• Schuster, R. L., Wieczorek, G. F. and Hope II, D. G., US Geol. Surv. Prof. Pap., 1998, 1551-C, 51–70.
• Hewitt, K., In Hydrological Aspects of Alpine and High Mountain Areas, Proceedings of the Exeter Symposium, IAHS, July 1982, vol. 138, pp. 259–269.
• Cui, P., Zhu, Y. Y., Han, Y. S., Chen, X. Q. and Zhuang, J. Q., Landslides, 2009, 6(3), 209–223.
• Evans, S. G. and Delaney, K. B., In Natural and Artificial Rockslide Dams, 2011, pp. 543–559.
• http://en.wikipedia.org/wiki/Attabad_ Lake (accessed on 10 May 2016).
• http://en.wikipedia.org/wiki/2014_Sunkoshi_ blockage (accessed on 10 May 2016).
• Korup, O., Prog. Phys. Geogr., 2002, 26(2), 206–235.
• Schuster, R. L. and Costa, J. E., In Landslide Dams: Processes, Risk, and Mitigation. In Proceedings of a Session in Conjunction with the ASCE Convention, 1986, pp. 1–20.
• Dai, F. C., Lee, C. F., Deng, J. H. and Tham, L. G., Geomorphology, 2005, 65(3), 205–221.
• Walder, J. S. and O’Connor, J. E., Water Resour. Res., 1997, 33(10), 2337–2348.
• Weidinger, J. T., J. Asian Earth Sci., 1998, 16(2), 323–331.
• Martha, T. R., Govindharaj, K. B. and Kumar, K. V., Geosci. Front., 2015, 6(6), 793–805.
• Martha, T. R. and Kumar, K. V., Landslides, 2013, 10(4), 469–479.
• Weidinger, J. T., In Natural and Artificial Rockslide Dams, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 243–277.
• Fuchs, G. E. and Linner, M. A., Jahrb. Geol. Bundesanst., 1995, 138, 65–85.
• Baud, A. Y., Gaetani, M., Garzanti, E., Fois, E., Nicora, A. and Tintori, A., Eclogae Geol. Helv., 1984, 77(1), 171–197.
• Swanson, F. J., Oyagi, N. and Tominaga, M., In Landslide Dams: Processes, Risk, and Mitigation, Proceedings of a Session in Conjunction with the ASCE Convention, 1986, pp. 31–145.
• Schuster, R. L., Ital. J. Eng. Geol. Environ. (Spec. Issue), 2006, 1, 9–13.

• Resolution Of Two Point Objects With Primary Spherical Aberration Under Incoherent Illumination

Abstract Views :109  |  PDF Views:4

Authors

K. Vinod Kumar , D. Karuna Sagar , R. Sayanna

Source

International Journal of Innovative Research and Development, Vol 2, No 9 (2013), Pagination: 

Abstract

The influence of primary spherical aberration on the resolution of two point objects in the case of apodised rotationally symmetric optical systems has been studied by applying the modified Sparrow criterion introduced by Asakura to suit the case of unequally bright object points. The results are presented in terms of Rayleigh and Sparrow limits obtained for coherent, partially coherent and incoherent illuminations. It is found that the chosen Hanning amplitude filter is effective in increasing the resolving power of the aberrated optical imaging systems.

 

Keywords

Rotationally Symmetric Systems, Primary Spherical Aberration, Hanning amplitude filters

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• Assessment of the Valley-Blocking ‘So Bhir’ Landslide near Mantam Village, North Sikkim, India, Using Satellite Images

Abstract Views :287  |  PDF Views:103

Authors

Tapas R. Martha ¹, Priyom Roy ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN

Source

Current Science, Vol 113, No 07 (2017), Pagination: 1228-1229

Abstract

A massive landslide occurred near Mantam village (opposite the Passingdang– Mantam Road) in Sikkim, India around 13:30 h (IST) on 13 August 2016 (Figure 1 a). The location (at the centre of the zone of depletion) of the landslide was 27°32'22.92"N and 88°30'2.47"E. According to the news reports, formation of a lake and consequent rise in water level had submerged the bridge over Kanaka river and washed away about 300 m stretch of the road. Five houses in Mantam village were also submerged. The villages of Tingvong, Lingdem, Laven, Kayeem, Lingzya, Bay, Sakyong Pentong and Ruklu Kayeem were cut-off due to damage to the connecting road. However, there were no human deaths reported due to the incident.

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References

• Mantovani, F., Soeters, R. and van Westen, C. J., Geomorphology, 1996, 15(3–4), 213–225.
• Huang, R. and Fan, X., Nature Geosci., 2013, 6, 325–326.
• Martha, T. R., Roy, P., Mazumdar, R., Govindharaj, K. B. and Vinod Kumar, K., Landslides, 2017, 14(2), 697–704.
• Martha, T. R., Govindharaj, K. B. and Vinod Kumar, K., Geosci. Front., 2015, 6, 793–805.
• GSI, Seismotectonic Atlas of India, 2000.
• Ravi Kumar, M., Hazarika, P., Srihari Prasad, G., Singh, A. and Saha, S., Curr. Sci., 2012, 102, 788–792
• Ghosh, S., Chakraborty, I., Bhattacharya, D., Bora, A. and Kumar, A., Indian J. Geosci., 2012, 66, 27–38.
• Roy, P., Martha, T. R. and Vinod Kumar, K., Curr. Sci., 2014, 107(12), 1961–1964.
• Martha, T. R. et al., Landslides, 2017, 14(1), 373–383.

• Detection of Coastal Landforms in a Deltaic Area Using a Multi-Scale Object-Based Classification Method

Abstract Views :199  |  PDF Views:99

Authors

Tapas R. Martha ¹, A. Mohan Vamsee ², Vikas Tripathi ³, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad 500 037, IN
2 Department of Geo-Engineering, Andhra University College of Engineering (A), Vishakhapatnam 530 003, IN
3 Department of Physical Sciences, Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot 485 780, IN

Source

Current Science, Vol 114, No 06 (2018), Pagination: 1338-1345

Abstract

Coastal landforms play an important role in protecting deltaic areas from erosion due to the action of waves. However, landforms in the deltas are dynamic and vulnerable to changes due to the effect of natural disasters like floods and cyclones. Automatic detection of dynamic landforms from satellite data can provide important inputs for effective coastal zone management. In this study, we developed an Object-Based Image Analysis (OBIA) technique to identify and map landforms in the Krishna delta, east coast of India using Resourcesat-2 LISS-IV multispectral image (5.8 m) and digital elevation model (DEM) (4 m). Since landforms are represented at multiple scales, the plateau objective function method was used to select appropriate scales during multiresolution segmentation. Knowledge-based rules in OBIA, using the parameters tone, texture, shape and context derived from satellite images and height from DEM were developed for classification of landforms. A total of 11 landforms (beach, beach ridge, swale, tidal creek, marsh, spit, barrier bar, mangrove, natural levee, channel island and channel bar) were mapped using this approach. High detection accuracy of these landforms indicates that the method developed has the potential for geomorphological mapping of dynamic landforms in low lying deltaic areas.

Keywords

Beach, Cyclone, DEM, Image Segmentation, Mangrove, OBIA, Resourcesat-2.

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References

• SAC (ISRO), Coastal Zones of India, Space Applications Centre (ISRO), Ahmedabad, India, 2012; http://sac.gov.in
• Nageswara Rao, K., Evolution and dynamics of the Krishna Delta, India. Natl. Geograph. J. India, 1985, 31, 1–9.
• Prabaharan, S., Srinivasa Raju, K., Lakshumanan, C. and Ramalingam, M., Remote sensing and GIS applications on change detection study in coastal zone using multi temporal satellite data. Int. J. Geomatics Geosci., 2010, 1, 2.
• Saranathan, E., Chandrasekaran, R., Soosai Manickaraj, D. and Kannan, M., Shoreline changes in Tharangampadi village, Nagapattinam district, Tamil Nadu, India – a case study. J. Indian Soc. Remote Sensing, 2011, 39, 107–115.
• Bishop, M. P., Shroder, J. J. F. and Colby, J. D., Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 2003, 55(1–4), 345–361.
• Iwahashi, J. and Pike, R. J., Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 2007, 86(3–4), 409–440.
• Smith, M. J. and Pain, C. F., Applications of remote sensing in geomorphology. Progress Phys. Geography, 2009, 33(4), 568–582.
• Gustavsson, M., Development of a detailed geomorphological mapping system and GIS geodatabase in Sweden, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 2006, p. 236.
• Philip, G. and Sah, M. P., Geomorphic signatures of active tectonics in the Trans-Yamuna segment of the western Doon valley, northwest Himalaya, India. Int. J. Appl. Earth Observ. Geoinfor., 1999, 1(1), 54–63.
• Shroder Jr, J. F. and Bishop, M. P., A perspective on computer modeling and fieldwork. Geomorphology, 2003, 53, 1–9.
• Martha, T. R., Sharma, A. and Vinod Kumar, K., Development of meander cutoffs – a multi-temporal satellite-based observation in parts of Sindh River, Madhya Pradesh, India. Arabian J. Geosci., 2015, 8(8), 5663–5668.
• Martha, T. R., Ghosh, D., Vinod Kumar, K., Lesslie, A. and Ravi Kumar, M. V., Geospatial technologies for national geomorphology and lineament mapping project – a case study of Goa state. J. Indian Soc. Remote Sensing, 2013, 41, 905–920.
• Xiaojun, Y., Damen, M. C. J. and Van Zuidam, R. A., Use of thematic mapper imagery with a geographic information system for geomorphologic mapping in a large deltaic lowland environment. Int. J. Remote Sensing, 1999, 20(4), 659–681.
• Dragut, L. and Blaschke, T., Automated classification of landform elements using object-based image analysis. Geomorphology, 2006, 81(3/4), 330–344.
• van Asselen, S. and Seijmonsbergen, A. C., Expert-driven semiautomated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology, 2006, 78(3–4), 309–320.
• Schneevoigt, N. J., van der Linden, S., Thamm, H. P. and Schrott, L., Detecting Alpine landforms from remotely sensed imagery – A pilot study in the Bavarian Alps. Geomorphology, 2008, 93(1–2), 104–119.
• Dragut, L. and Eisank, C., Automated object-based classification of topography from SRTM data. Geomorphology, 2012, 142/141, 21–33.
• Hay, G. J. and Castilla, G., Object-based image analysis: Strengths, weaknesses, opportunities and threats (SWOT). In Proceedings OBIA, Commission VI, WG VI/4, Calgary, CA, 2006.
• Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S. and Weng. Q., Per-pixel vs object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing Environ., 2011, 115, 1145–1161.
• Babu, P. V. L. P., Morphological evolution of the Krishna delta. Photonirvachak, 1975, 3, 21–27.
• Nageswara Rao, K. and Vaidyanadhan, R., Evolution of the coastal landforms in the Krishna delta front, India. Trans. Inst. Indian Geogr., 1979, 1, 25–32.
• Gamage, N. and Smakhtin, V., Do river deltas in east India retreat? a case of the Krishna Delta. Geomorphology, 2009, 103, 533–540.
• Baatz, M. and Schäpe, A., Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsveraarbeitung XII, Beitrage zum AGIT Symposium Salzburg (eds Strobl, L. J., Blaschke, T. and Griesebener, T.), Herbert Wichmann Verlag, Heidelberg, 2000, pp. 12–23.
• Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J. and Vinod Kumar, K., Characterizing spectral, spatial and morphometric properties of landslides for automatic detection using object-oriented methods. Geomorphology, 2010, 116(1–2), 24–36.
• Vamshi, G. T., Martha, T. R. and Vinod Kumar, K., An object-based classification method for automatic detection of lunar impact craters from topographic data. Adv. Space Res., 2016, 57, 1978–1988.
• Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V. and Vinod Kumar, K., Segment optimisation and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sensing, 2011, 49(12), 4928–4943.
• GSI, ISRO, Manual for National Geomorphological and Lineament Mapping on 1 : 50,000 scale. A Project under National (Natural) Resources Census (NRC), 2010.
• Shufelt, J. A., Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21(4), 311–326.

• Spectral Response of Few Important Textural Variants of Chromitite and its Potential in Estimating Relative Grades of Chromitite – A Case Study for Chromitite of Nuggihalli Schist Belt, India

Abstract Views :230  |  PDF Views:82

Authors

Arindam Guha ¹, Biswajit Ghosh ², Sukanya Chaudhury ², Komal Rani ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organization, Balanagar, Hyderabad 500 625, IN
2 Department of Geology, University of Calcutta, 35 Ballygunge Circular Road, Kolkata 700 019, IN

Source

Current Science, Vol 114, No 08 (2018), Pagination: 1721-1731

Abstract

We have collected, processed and analysed the reflectance spectra of representative chromitite samples of spot type, clot type and disseminated type textural variants to understand the diagnostic spectral features of each of these samples. We have found that the reflectance spectrum of each textural variant is distinct from the spectra of other variants despite having few common absorption features. Spectral features of chromitite samples are governed by the spectra of two dominant minerals, chromite and chlorite. Spectral features of chromitite at 550 nm and 1100 nm are governed by electronic transition process in Fe³⁺ and crystal field effect in Fe²⁺ ions present in chromite structure respectively. On the other hand, spectral features at 1400 nm, 1900 nm and 2300 nm are related to the vibration of O–H, H–OH and metal hydroxide bonds in chlorite. Amongst these features, the spectral feature at 1100 nm (due to Fe²⁺ in chromite grains) is common to all three major textural varieties of chromitite samples studied here. Electron probe micro analysis (EPMA) data of chromite and chlorite grains of each texture are used to relate the presence and abundance of Fe₂₊ (in chromite grains) with absorption feature. Width of the 1100 nm feature has a correlation value 0.95, while depth of the same feature has a correlation value 0.94 with the abundance of chromite mineral estimated using modal analysis of chromite samples. Therefore, spectrometric parameter of 1100 nm spectral feature of chromitite can be used as proxy for estimating modal abundance of chromite in chromitite samples after estimating deposit specific correlation coefficient.

Keywords

Chromitite, Electronic Processes, Modal Analysis, Spectral Feature, Texture, Vibrational Processes.

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References

• Hunt, G. R. and Salisbury, J. W., Visible and near-infrared spectra of minerals and rocks: II. Carbonates. Mod. Geol., 1971, 2, 23–30.
• Clark, R. N. and Roush, T. L., Reflectance spectroscopy quantitative analysis techniques for remote sensing applications. J. Geo-phys. Res., 1984, 89(B7), 6329–6340.
• Clark, R. N., Spectroscopy of rocks and minerals, and principles of spectroscopy. USGS spectral laboratory; http://speclab.cr.usgs.gov/spectral-lib.html (accessed 7 December 2011).
• Clark, R. N., King, T. V. V., Klejwa, M. and Swaze, G. A., High spectral resolution reflectance spectroscopy of minerals. J. Geo-phys. Res., 1990, 95(B8), 12653–12680.
• Cloutis, E. A., Hyperspectral geological remote sensing: evaluation of analytical techniques. Int. J. Remote Sensing, 1996, 17(12), 2215–2242.
• Clark, R. N., Swayze, G. A., Heidebrecht, K., Green, R. O. and Goetz, A. F. H., Calibration to surface reflectance of terrestrial imaging spectrometry data: comparison of methods, summaries of the fifth annual JPL airborne geosciences workshop. Jet Propulsion Laboratory Special Publication, 1995, pp. 41–42.
• Guha, A., Chakraborty, D., Ekka, A. B., Pramanik, K. and Chatterjee, S., Spectroscopic study of rocks of Hutti-Maski Schist Belt, Karnataka. J. Geol. Soc. India, 2012, 79, 335–344.
• Guha, A., Rao, A., Ravi, S., Vinod Kumar, K. and Dhananjaya Rao, E. N., Analysis of the potentials of kimberlite rock spectra as spectral end member – a case study using kimberlite rock spectra from the Narayanpet kimberlite Field (NKF), Andhra Pradesh. Curr. Sci., 2012, 103(9), 1096–1104.
• Carli, C. and Sgavetti, M., Spectral characteristics of rocks: effects of composition and texture and implications for the interpretation of planet surface compositions. Icarus, 2011, 211(2), 1034–1048.
• Dennis, K. M., Spectral properties (0.4 to 25 microns) of selected rocks associated with disseminated gold and silver deposits in Nevada and Idaho. J. Geophys. Res.: Solid Earth, 1986, 91(B1), 2156–2202.
• Guha, A., Vinod Kumar, K., Ravi, S. and Dhanamjaya Rao, S., Reflectance spectroscopy of kimberlites – in parts of Dharwar Craton, India. Arabian J. Geosci., 2015, 8(11), 9373–9388.
• Khan, S. D. and Mahmood, K., The application of remote sensing techniques to the study of ophiolites. Earth Sci. Rev., 2008, 89, 135–143.
• Khan, S. D., Mahmood, K. and Casey, J. F., Mapping of Muslim Bagh ophiolite complex (Pakistan) using new remote sensing and field data. J. Asian Earth Sci., 2007, 30, 333–343.
• Pournamdari, M., Hashim, M. and Pour, A. B., Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, south Iran. Adv. Space Res., 2014, 54, 694–709.
• Rajendran, S. et al., ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: exploration strategy. Ore Geol. Rev., 2012, 44, 121–135.
• van der Meer, F., Analysis of spectral absorption features in hyperspectral imagery. Int. J. Appl. Earth Observ. Geoinfor., 2004, 5, 55–68.
• Tangestani, M. H., Jaffari, L., Vincent, R. K. and Maruthi Sridhar, B. B., Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: a case study from Neyriz ophiolite, SW Iran. Remote Sensing Environ., 2011, 115, 2243–2254.
• Mukherjee, R., Mondal, S. K., Rosing, M. T. and Frei, R., Compositional variations in the Mesoarchean chromites of the Nuggihalli schist belt, Western Dharwar Craton (India): potential parental melts and implications for tectonic setting. Lithos, 2010, 160, 865–885.
• ASD, I., Field spec specification, 2012; www.asdi.com.
• Blom, R. G., Abrams, M. J. and Adams, H. G., Spectral reflectance and discrimination of plutonic rocks in the 0.45–2.45 μm region. J. Geophys. Res., 1980, 85(B5), 2156–2202.
• Walter, L. S. and Salisbury, J. W., Spectral characterization of igneous rocks in the 8–12 μm region. J. Geophys. Res.-Solid Earth, 1989, 94(B7), 2156–2202.
• Cloutis, E. A., Sunshine, J. M. and Morris, R. V., Spectral reflectance-compositional properties of spinels and chromites: implications for planetary remote sensing and geothermometry. Meteor. Planet. Sci., 2004, 39(4), 545–565.
• Mitra, S. and Bidyananda, M., Evaluation of metallogenic potential of the Nuggihalli greenstone belt, South India. C. R. Geosci., 2003, 335(2), 185–192.
• Ramakrishnan, M., Precambrian mafic magmatism in the western Dharwar craton, Southern India. J. Geol. Soc. India, 2009, 73, 101–116.
• Radhakrishna, B. P. and Vaidyanathan, R., Geology of Karnataka, Geological Society of India, Bangalore, 1994, p. 298.
• Devaraju, T. C., Viljoen, R. P., Sawkar, R. H. and Sudhakara, T. L., Mafic and ultramafic magmatism and associated mineralization in the Dharwar craton, southern India. J. Geol. Soc. India, 2009, 73, 73–100.
• Droop, G. T. R., A general equation for estimating Fe³⁺ concentrations in ferromagnesian silicates and oxides from microprobe analysis, using stoichiometric criteria. Mineral. Mag., 2011, 51, 431–435.
• Ghosh, B. and Konar, R., Chromites from metaanorthosites, Sitampundi layered igneous complex, Tamil Nadu, southern India. J. Asian Earth Sci., 2011, 42, 1394–1402.
• Milton, E. J., Schaepman, M. E., Anderson, K., Kneubahler, M. and Fox, N., Progress in field spectroscopy. Remote Sensing Environ., 2009, 113, S92–S109.
• Baldridge, A. M., Hook, S. J., Grove, C. I. and Rivera, G., The ASTER spectral library version 2.0. Remote Sensing Environ., 2009, 113, 711–715.
• Nicodemus, F. F., Richmond, J. C., Hsia, J. J., GIinsberg, I. W. and Limperis, T. L., Geometrical considerations and nomenclature for reflectance In National Bureau of Standards Monograph (ed. Office, D. C. U. S. G.), Washington, 1977, p. 20402.
• Biggar, S. F., Labed, J., Santer, R. P. and Slater, P. N., Laboratory calibration of field reflectance panels. In Proceedings of SPIE – The International Society for Optical Engineering (ed. Slater, P. N.), Orlando, Florida, 1988, pp. 232–240.
• Bruegge, C. J., Chrien, N. and Haner, D., A spectralon BRF database for MISR calibration applications. Remote Sensing Environ., 2001, 76, 354–366.
• Crowley, J. K., Visible and near-infrared spectra of carbonate rocks reflectance variations related to petrographic texture and impurities. J. Geophys. Res., 1986, 91(B5), 5001–5012.
• Okada, K. and Iwashita, A., Hyper-multispectral image analysis based on waveform characteristics of spectral curve. Adv. Space Res., 1992, 12, 433–442.
• van der Meer, F. D., Basic physics of spectrometry. In Imaging Spectrometry: Basic Principles and Prospective Applications (eds van der Meer, F. D. and de Jong, S. M.), Springer, Dordrecht, 2006, pp. 3–16.
• Trude, K. V. V. and Clark, R. N., Spectral characteristics of chlorites and Mg-serpentines using high-resolution reflectance spectroscopy. J. Geophys. Res.: Solid Earth, 1989, B10, 13997–14008.
• Jafri, S. H., Khan, N., Ahmad, S. M. and Saxena, R., Geology and Geochemistry of Nuggihalli Schist belt, Dharwar craton, Karnataka, India. In Precambrian of South India (eds Naqvi, S. M. and Rogers, J. J. W.), Memoir Geological Society of India, 1983, vol. 4, pp. 110–120.

• Reactivation of Minor Scars to Major Landslides–A Satellite-Based Analysis of Kotropi Landslide (13 August 2017) In Himachal Pradesh, India

Abstract Views :259  |  PDF Views:80

Authors

Priyom Roy ¹, Tapas R. Martha ¹, Nirmala Jain ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad - 500 037, IN

Source

Current Science, Vol 115, No 3 (2018), Pagination: 395-398

Abstract

On 13 August 2017, a massive landslide occurred close to the village of Kotropi (near Kotropi bus stop) in Mandi district, Himachal Pradesh, India. It occurred on National Highway 154, the road between Mandi and Pathankot. Media reports suggest that a section of the slope totally collapsed and two buses of the Himachal State Transport Corporation along with few other vehicles were buried under the debris. News reports also suggest that there have been 46 fatalities from the incident. Around 300 m of the highway has been completely buried under debris, thus disrupting communication on an important route¹.

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References

• www.indiatoday.in
• Martha, T. R. et al., Landslides, 2015, 12(1), 135–146.
• Martha, T. R. et al., Landslides, 2017, 14(2), 697–704.
• Martha, T. R. et al., Landslides, 2017, 14(1), 373–383.
• Roy, P., Martha, T. R. and Vinod Kumar, K., Curr. Sci., 2014, 107(12), 1961– 1964.
• https://employee.gsi.gov.in/cs/groups/public/documents/document/b3zp/mtyx/~edisp/dcport1gsigovi161798.pdf
• Martha, T. R. et al., IEEE Geosci. Remote Sensing Lett., 2010, 7(3), 582–586.
• www.icimod.org/?q=14356
• Martha, T. R., Roy, P. and Vinod Kumar, K., Curr. Sci., 2017, 113(7), 1228–1229.

• Structural Controls on Coal Fire Distributions - Remote Sensing Based Investigation in the Raniganj Coalfield, West Bengal

Abstract Views :216  |  PDF Views:0

Authors

Arindam Guha ¹, K. Vinod Kumar ²

Affiliations
1 Geosciences Division, National Remote Sensing Centre (ISRO), Balanagar, Hyderabad - 500 037, IN
2 Geosciences Division, National Remote Sensing Centre (ISRO), Balanagar, Hyderabad - 500 037, IN

Source

Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 79, No 5 (2012), Pagination: 467-475

Abstract

Coal fires are serious problem in Raniganj coalfield as it is the case for some of the other coalfields of India like Jharia coalfield. Earlier efforts were made to map the coal fires of this coal-field based on satellite observation. But the restricted distribution of major coal fires in the particular portion of the coalfield makes the basis for finding the geological control if responsible for coal fire distribution. In present study, night time thermal data of ASTER (Advance spaceborne thermal emission and reflection radiometer) is used to map the latest distribution (December, 2006) of coal fires in the Raniganj coalfield. Coal fire map shows that most significant zone affected by fire is at the north-western portion of the coalfield; where NE- trending open cast mines are affected by fire. This fire zone is associated with high grade coal of the Barakar Formation. Coal fires are also mapped in open cast pits of Jambad-Mangalpur area occurring over rocks of the Raniganj Formation. By integrating geological map and satellite-derived coal fire map of Raniganj coal field, it is observed that the coal fires detected by remote sensing study are spatially associated with intraformational faults. These faults may have played significant role in supplying oxygen to these coal-fires and allowing them to propagate down the depth along the trends of the faults.

Keywords

ASTER, Coal Fire, Intraformational Faults, Thermal Channels, West Bengal.

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References

• CHATTERJEE, R.S. (2006) Coal fire mapping from satellite thermal IR data – A case example in Jharia Coalfield, Jharkhand, India. ISPRS Jour. Photogr. Rem. Sens., v.60, pp.113-128.
• CRACKNELL, A.P. and MANSOR, S.B. (1992) Detection off subsurface coal fires using Landsat Thematic Mapper data. Int. Jour. Photogr. Rem. Sens, v.29 (b7), pp.750-753.
• GANGOPADHYAY, P.K. (2003) Coalfire detection and monitoring in Wuda, North China, A multispectral and multi-sensor approach. Unpubld. Ph.D. Thesis, ITC Netherlands (in English).
• GANGOPADHYAY, P.K., LAHIRI-DUTT, K. and SAHA, K. (2006) Application of remote sensing to identify coal fires in the Raniganj Coal belt, India. Internat. Jour. Appld. Earth Observation and Geoinformation, v.8, pp.188-195.
• GANGOPADHYAY, P.K., MALTHUIS, B. and VAN DINK (2005) ASTER derived emissivity and coal-fire related surface temperature anomaly a case study in Wuda, North China. Internat. Jour. Remote Sensing, v.26, pp.5555-5571.
• GILLESPIE, A.R., ROKUGAWA, S., HOOK, S., MATSUNAGA, T. and KAHLE, A.B. (1998) A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Trans. Geosci. Rem. Sens., v.36, pp.1113-1126.
• GSI (2003) Bulletin of the Geological Survey of India. Coalfields of India. Series 45A, pp.128-137
• GUHA, A., VINOD KUMAR, K. and KAMARAJU, M.V.V. (2008) A satellite-based study of coal fires and open-cast mining activity in Raniganj coalfield, West Bengal, Curr. Sci., v.95, pp.1603-1607
• MARTHA, T.R, BHATTACHARYA, A. and VINOD KUMAR, K. (2005) Coal-fire detection and monitoring in Raniganj coalfield, India – A remote sensing approach. Curr. Sci., pp.21-24.
• PRAKASH, A., GUPTA, R.P. and SARAF, A.K. (1997) Landsat-TM based comparative study of surface and subsurface fires in the Jharia coalfield. India. Internat. Jour. Rem. Sens., v.18(11), pp.2463-2469.
• REDDY, C.S.S., SRIVASTAVA, S.K. and BHATTACHARYA, A. (1993) Application of thematic mapper short wavelength infrared data for the detection and monitoring of high temperature related Geoenvironmental features. Internat. Jour. Rem. Sens., v.14, pp.3125-3132.
• SARAF, A.K., PRAKASH, A., SENGUPTA, S. and GUPTA, R.P. (1995) Landsat-TM data for estimating ground temperature and depth of sub-surface coal fire in the Jharia coalfiled, India Internat. Jour. Remote sensing, v.16, pp.2111-2124.
• SCHMUGGE, T., FRENCH, RITCHIE, J.C., RANGO, A. and PELGRUM, H. (2002) Temperature and emissivity separation from multispectral thermal infrared observation. Remote Sensing of Environment, v.79, pp.189-198.
• ZHANG, J., WAGNER, W., PRAKASH, A., MEHL, H. and VOIGT, S. (2004) Detecting coal fires using remote sensing technique. Internat. Jour. Remote Sensing, v.25, pp.3193-3220.

• Spectroscopic Study of Rocks of Hutti-Maski Schist Belt, Karnataka

Abstract Views :204  |  PDF Views:0

Authors

Arindam Guha ¹, Debashish Chakraborty ², A. B. Ekka ², Kaushik Pramanik ², K. Vinod Kumar ¹, S. Chatterjee ², S. Subramanium ¹, D. Ananth Rao ¹

Affiliations
1 National Remote Sensing Centre, Balanagar, Hyderabad - 500 037, IN
2 Geological Survey of India, Jawaharlal Nehru Road, Kolkata - 700 016, IN

Source

Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 79, No 4 (2012), Pagination: 335-344

Abstract

Recent developments in sensor technology have given an onset for studying the earth surface features based on the detailed spectroscopic observation of different rocks and minerals. The spectroscopic profiles of the rocks are always quite different than their constituent minerals however, the spectral profile of a rock can be broadly reconstituted from the spectral profile of each constituent minerals. Interpretation of rock spectra using the spectra of constituent minerals based on relative spectral matching can bring out interesting information on the rock. Present study is an effort toward this and it highlights how visible-near infrared-shortwave-infrared (VNIR-SWIR) rock spectroscopy acts as an useful tool for understanding the rock-mineralogy in indirect and rapid way. It has also been observed that spectral signatures of rocks; studied in present case, are related to spectral signatures of constituent minerals although absorption features of constituent mineral in the rock are also modified by the other minerals juxtaposed in the rock fabric. However, each rock of the study area has their significant absorption features, but many of the absorption signatures are closely spaced, as altered rock has significant absorption at 2305 nm whereas amphibolite has its important absorption signature in 2385 nm and metabasalt has its significant absorption at 2342 nm. Therefore spectral measurement of high spectral resolution with appreciable signal to noise ratio (SNR) only can detect rocks from each other based on the absorption signatures mentioned above (each of which is 10 to 20 nm apart from the other) and therefore spectroscopy of rock is an innovative technique to map rocks and minerals based on the spectral signatures.

Keywords

Rock Fabric, Spectroscopy, Spectral Matching, Spectral Resolution.

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References

• BALDRIDGE, A.M., HOOK, S.J., GROVE, C.I. and RIVERA, G. (2009) The ASTER spectral library version 2.0, Remote Sensing of Environment, v.113 (4), pp.711-715
• BIGGAR, S.F., LABED, J., SANTER, R.P. and SLATER, P.N. (1988) Laboratory calibration of field reflectance panels. In: P.N. Slater (Ed.), Proc. SPIE — The International Society for Optical Engineering, Orlando, Florida, pp.232-240.
• BRUEGGE, C.J., CHRIEN, N. and HANER, D. (2001) A Spectralon BRF database for MISR calibration applications. Remote Sensing of Environment, v.76, pp.354-366.
• BRUEGGE, CAROL, J., STIEGMAN, ALBERT, E., COULTER, DANIEL, R., HALE, ROBERT, R.,DINER, DAVID, J. and SPRINGSTEEN, ARTHUR, W. (1991) Reflectance stability analysis of Spectralon diffuse calibration panels. Proceedings of SPIE —The International Society for Optical Engineering, v.1493, pp.132-142.
• CLARK, R.N., KING, T.V.V., KLEJWA, M., SWAYZE, G. and VERGO, N. (1990) High Spectral Resolution Reflectance Spectroscopy of Minerals. Jour. Geophys Res., v.95, pp.12653-12680.
• CLARK, R.N. and LUCEY, P.G. (1984) Spectral Properties of IceParticulate Mixtures and Implications for Remote Sensing I: Intimate Mixtures. Jour. Geophys Res., v.89, pp.6341-6348.
• CLARK, R.N. Spectroscopy of Rocks and Minerals, and Principles of spectroscopy, http://speclab.cr.usgs.gov/PAPERS.refl-mrs/refl4.html (accessed online on 15. 02.2011).
• FIELDSPEC SPECTRORADIOMETER. http://www.asdi.com/products/fieldspec-3-portable-spectroradiometer (accessed online on 15.02.2011).
• GOETZ, A.F.H., VANE, G., SOLOMON, J.E. and ROCK, B.N. (1985) Imaging spectrometry for earth remote sensing: Science, 228, pp 1147-1153.
• GU, X.F. and GUYOT, G. (1993) Effect of diffuse irradiance on the reflectance factor of reference panels under field conditions. Remote Sensing of Environment, v.45, pp.249-260.
• HUNT, G.R. and SALISBURY, J.W. (1970) Visible and near infrared spectra of minerals and rocks. I. Silicate minerals, Mod. Geol., v.1, pp.283-300.
• HUNT, G.R. (1977) Spectral signatures of particulate minerals, in the visible and near-infrared. Geophysics, v.42, pp.501-513.
• HUNT, G.R. (1979) Near-infrared (1.3-2.4 µm) spectra of alteration minerals-Potential for use in remote sensing. Geophysics, v.44, pp.1974-1986.
• MARKHAM, B.L.,WILLIAMS, D.L., SCHAFER, J.R.,WOOD, F. and KIM, M.S. (1995) Radiometric characterization of diode-array field spectroradiometers. Remote Sensing of Environment, v.51, pp.317-330.
• MARTONCHIK, J.V., BRUEGGE, C.J. and STRAHLER, A.H. (2000) A review of reflectance nomenclature used in remote sensing. Remote Sensing Reviews, v.19, pp.9-20.
• NICODEMUS, F.F., RICHMOND, J.C., HSIA, J.J., GINSBERG, I.W. and LIMPERIS, T.L. (1977). Geometrical considerations and nomenclature for reflectance. National Bureau of Standards Monograph, Washington, v.160, pp.20402.
• OKADA, K. and IWASHITA, A. (1992) Hyper-multispectral Image Analysis Based on Waveform Characteristics of Spectral Curve. Adv. Space Res., v.12, pp.433-442.
• ROY, A. (1979) Polyphase folding deformation in the Hutti-Maski schist belt, Karnataka. Jour. Geol. Soc. India, v.20, pp.598-607
• VAN DER MEER, F.D., DE JONG, S.M. and BEKKER,W (2001) Imaging spectrometry: basic analytical techniques. In: F.D. Van der Meer and S.M. De Jong (Eds.), Imaging spectrometry: basic principles and prospective applications. pp-15-61(Springer-Verlag).

• Landslides Mapped using Satellite Data in the Western Ghats of India After Excess Rainfall During August 2018

Abstract Views :268  |  PDF Views:101

Authors

Tapas R. Martha ¹, Priyom Roy ¹, Kirti Khanna ¹, K. Mrinalni ¹, K. Vinod Kumar ¹

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN

Source

Current Science, Vol 117, No 5 (2019), Pagination: 804-812

Abstract

Excess rainfall during August 2018 triggered numerous landslides in the Western Ghats region of India covering the states of Kerala, Karnataka and Tamil Nadu. These landslides caused widespread damage to property, loss of life and adversely affected various land resources. In this article, we present an inventory of landslide prepared from the analysis of multitemporal high-resolution images acquired before and after the rainfall event from Resourcesat-2, WorldView-2, GF-2, SPOT-6 and 7, Pleiades-1, Kompsat-3 and Sentinel-2 Earth observation satellites. A total of 6970 landslides with a cumulative area of 22.6 sq. km were mapped for this rainfall event. Majority of landslides have occurred in Kerala (5191), followed by Karnataka (993) and Tamil Nadu (606). Landslides are mostly debris slide and debris flow type with entrainment along the channels. Results show that landslides (83.2%) are triggered by very high rainfall. Also, very high rainfall has resulted in 14.9% of landslides even though slopes are moderate, mainly in the Kodagu district of Karnataka.

Keywords

Debris Flows, Disaster Response, Excess Rainfall, Landslides, Satellite Data.

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References

• https://indianexpress.com/article/india/483-dead-in-kerala-floods-and-landslides-losses-more-than-annual-plan-outlay-pinarayi-vijayan-5332306/ (accessed on 3 June 2019).
• https://www.indiatoday.in/india/story/kerala-rains-all-5-gates-idukki-dam-open-1310804-2018-08-10 (accessed on 3 June 2019).
• https://www.indiatoday.in/india/story/tamil-nadu-heavy-rain-triggers-flood-landslides-in-attakatti-1316906-2018-08-17 (accessed on 3 June 2019).
• https://timesofindia.indiatimes.com/city/bengaluru/landslides-on-ghats-how-to-connect-bengaluru-mangaluru-and-udupi/articleshow/65505135.cms (accessed on 3 June 2019).
• Martha, T. R., Roy, P., Mazumdar, R., Babu Govindharaj, K. and Vinod Kumar, K., Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides, 2017, 14, 697–704.
• Martha, T. R., Roy, P. and Vinod Kumar, K., Rapid assessment of the valley blocking ‘So Bhir’ landslide near Mantam village, North Sikkim using satellite image. Curr. Sci., 2017, 113, 1228–1229.
• Metternicht, G., Hurni, L. and Gogu, R., Remote sensing of landslides: an analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing Environ., 2005, 98, 284–303.
• van Westen, C. J. and Lulie Getahun, F., Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology, 2003, 54, 77–89.
• Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K. and Mehl, H., Satellite image analysis for disaster and crisismanagement support. IEEE Trans. Geosci. Remote Sensing, 2007, 45, 1520–1528.
• Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J. and Vinod Kumar, K., Characterizing spectral, spatial and morphometric properties of landslides for automatic detection using objectoriented methods. Geomorphology, 2010, 116, 24–36.
• Voigt, S. et al., Global trends in satellite-based emergency mapping. Science, 2016, 353, 247–252.
• www.bhuvan-noeda.nrsc.gov.in/disaster/disaster/disaster.php?id=landslide_monitor (accessed on 21 January 2019).
• https://disastercharter.org/web/guest/activations/-/article/landslide-in-india-activation-584 (accessed on 21 January 2019).
• Thampi, P. K., Mathai, J. and Sankar, G., Landslides (urul pottal) in Western Ghats: some field observations. In Proceedings of the Seventh Kerala Science Congress, Palakkad, January 1995, pp. 97–99.
• Kuriakose, S. L., Sankar, G. and Muraleedharan, C., History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala, India. Environ. Geol., 2009, 57(7), 1553–1568.
• Sajinkumar, K. S., Anbazhagan, S., Pradeepkumar, A. P. and Rani, V. R., Weathering and landslide occurrences in parts of Western Ghats, Kerala, India. J. Geol. Soc. India, 2011, 78, 249–257.
• Jaiswal, P., van Westen, C. J. and Jetten, V., Quantitative assessment of landslide hazard along transportation lines using historical records. Landslides, 2011, 8, 279–291.
• Radhakrishna, B. P., Geomorphic rejuvenation of the Indian Peninsula. In Sahyadri: The Great Escarpment of the Indian Subcontinent (eds Gunnel, Y. and Radhakrishna, B. P.), Geological Society of India, Bengaluru, 2001, pp. 201–211.
• Soman, K., Geology of Kerala, Geological Society of India, Bengaluru, 2002, p. 335.
• Myers, N., Threatened biotas: ‘hotspots’ in tropical forests. Environmentalist, 1988, 8, 1–20.
• Ramesh, B. R., Patterns of vegetation, biodiversity and endemism, in Western Ghats. Mem. Geol. Soc. India, 2001, 47, 973–981.
• https://whc.unesco.org/en/list/1342 (accessed on 21 January 2019).
• www.hydro.imd.gov.in/hydrometweb (accessed on 21 January 2019).
• www.cpc.ncep.noaa.gov (accessed on 21 January 2019).
• Martha, T. R., Kamala, P., Josna, J., Vinod Kumar, K. and Jai Sankar, G., Identification of new landslides from high resolution satellite data covering a large area using object-based change detection methods. J. Indian Soc. Remote Sensing, 2016, 44, 515– 524.
• Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V. and Vinod Kumar, K., Segment optimisation and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sensing, 2011, 49, 4928–4943; doi:10.1109/TGRS.2011.2151866.

• Potential of Airborne Hyperspectral Data for Geo-Exploration over Parts of Different Geological/Metallogenic Provinces in India based on AVIRIS-NG Observations

Abstract Views :207  |  PDF Views:111

Authors

Satadru Bhattacharya ¹, Hrishikesh Kumar ¹, Arindam Guha ², Aditya K. Dagar ¹, Sumit Pathak ¹, Komal Rani (Pasricha) ², S. Mondal ³, K. Vinod Kumar ², William Farrand ⁴, Snehamoy Chatterjee ⁵, S. Ravi ⁶, A. K. Sharma ¹, A. S. Rajawat ¹

Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 042, IN
3 Department of Geophysics, Indian Institute of Technology (ISM), Dhanbad 826 004, IN
4 Space Science Institute, Boulder, Colorado 80301, US
5 Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, Michigan 49931, US
6 Geological Survey of India Training Institute, Bandlaguda, Hyderabad 500 068, US

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1143-1156

Abstract

In this article, we discuss the potential of airborne hyperspectral data in mapping host rocks of mineral deposits and surface signatures of mineralization using AVIRIS-NG data of a few important geological provinces in India. We present the initial results from the study sites covering parts of northwest India, as well as the Sittampundi Layered Complex (SLC) of Tamil Nadu and the Wajrakarur Kimberlite Field (WKF) of Andhra Pradesh from southern India. Modified spectral summary parameters, originally designed for MRO-CRISM data analysis, have been implemented on AVIRIS-NG mosaic of Jahazpur, Rajasthan for the automatic detection of phyllosilicates, carbonates and Fe–Mg-silicates. Spectral analysis over Ambaji and the surrounding areas indicates the presence of calcite across much of the study area with kaolinite occurring as well in the north and east of the study area. The deepest absorption features at around 2.20 and 2.32 μm and integrated band depth were used to identify and map the spatial distribution of phyllosilicates and carbonates. Suitable thresholds of band depths were applied to map prospective zones for marble exploration. The data over SLC showed potential of AVIRIS-NG hyperspectral data in detecting mafic cumulates and chromitites. We also have demonstrated the potential of AVIRIS-NG data in detecting kimberlite pipe exposures in parts of WKF.

Keywords

Data, Geological Provinces, Host Rocks, Hyperspectral, Mineral Deposits.

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References

• Goetz, A. F. H., Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sensing Environ., doi:10,1016/j.res.2007.12.014.
• Goetz A. F., Vane G., Solomon J. E. and Rock B. N., Imaging spectrometry for Earth remote sensing. Science, 1985, 228, 4704, 1147–1153.
• Clark, R. N., King, T. V. V., Klejwa, M. and Swayze, G. A., High spectral resolution reflectance spectroscopy of minerals. JGR, 1990, 95, 12653–12680.
• Pieters, C. M. et al., Character and spatial distribution of OH/H2O on the surface of the Moon seen by Chandrayaan-1. Science, 2009, 326; doi:10.1126/science.1178658.
• Clark, R. N., Detection of adsorbed water and hydroxyl on the Moon. Science, 2009, 326; doi:10.1126/science.118105.
• Hampton, D. L. et al., An overview of the instrument suite for the deep impact mission. Space Sci. Rev., 2005, 117, 43–93.
• Brown, R. H. et al. The Cassini visual and infrared mapping spectrometer (VIMS) investigation. Space Sci. Rev., 2004, 115, 111– 168.
• Murchie, S. L. et al., Compact reconnaissance imaging spectrometer for mars investigation and dataset from the mars reconnaissance orbiter’s primary science phase. J. Geophys. Res., 2009, 114, E00D07.
• Sunshine, J. M. et al., Temporal and spatial variability of lunar hydration as observed by the deep impact spacecraft. Science, 2009, 326, doi:10.1126/science.1179788.
• Goswami, J. N. and Annadurai, M., An overview of the Chandrayaan1 mission. Curr. Sci., 2009, 96, 486–491.
• Kumar, A. S. K. et al., Hyperspectral imager for lunar mineral mapping in visible and near infrared band. Curr. Sci., 2009, 96, 496.
• Bhattacharya, S., Majumdar, T. J., Rajawat, A. S., Panigrahi, M. K. and Das, P. R., Utilization of Hyperion data over Dongargarh, India, for mapping altered/weathered and clay minerals along with field spectral measurements. Int. J. Remote Sensing, 2012, 33(17), 5438–5450.
• Kusuma, K. N., Ramakrishnan, D. and Pandalai, H. S., Spectral pathways for effective delineation of high-grade bauxites: a case study from the Savitri River Basin, Maharashtra, India, using EO-1 Hyperion data. Int. J. Remote Sensing, 2012, 33(22), 7273– 7290.
• Magendran, T. and Sanjeevi, S., Hyperion image analysis and linear spectral unmixing to evaluate the grades of iron ores in parts of Noamundi, eastern India. Int. J. Appl. Earth Obs. Geoinf., 2014, 26, 413–426.
• Rani, N., Mandla, V. R. and Singh, T., Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, southern India using hyperspectral remote sensing data. Geocarto Int., 2017, 32(3), 225–237.
• Shanmugam, S. and Abhishekh, P. V., Spectral unmixing of hyperspectral data to map bauxite deposits. In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications (eds Smith, W. Sr. L. et al.), SPIE, 2006, vol. 6405, pp. 64051N.
• Thompson, D. R., Boardman, J. W., Eastwood, M. L. and Green, R. O., A large airborne survey of Earth’s visible–infrared spectral dimensionality. Opt. Express, 2017, 25(8), 9186–9195.
• Thompson, D. R. et al., Atmospheric correction with the Bayesian empirical line. Opt. Exp., 2016, 24, 2134–214.
• Gupta, S. N., Arora, Y. K., Mathur, R. K., Iqballuddin, Prasad, B., Sahai, T. N. and Sharma, S. B., Lithostratigraphic Map of Aravalli region, Southeastern Rajasthan and Northern Gujarat. Geological Survey of India, Hyderabad, 1980.
• Dey, B., Das, K., Dasgupta, N., Bose, S. and Ghatak, H., Zircon U–Pb SHRIMP dating of the Jahazpur granite and its implications on the stratigraphic status of the Hindoli–Jahazpur group. In Seminar Abstract Volume: Developments in Geosciences in the Past Decade – Emerging Trends for the Future and Impact on Society and Annual General Meeting of the Geological Society of India, IIT Kharagpur, 21–23 October 2016.
• Pandit, M. K., Sial, A. N., Malhotra, G., Shekhawat, L. S. and Ferreira, V. P., C-, O-isotope and whole-rock geochemistry of Proterozoic Jahazpur carbonates. NW Indian Craton. Gondwana Res., 2003, 6(3), 513–522.
• Geology and Mineral Resources of Rajasthan, Geological Survey of India Miscellaneous Publication No. 30, Part 12, 3rd revised edn, 2011, ISSN 0579-4706.
• Pelkey, S. M. et al., CRIMS multispectral summary products: parameterizing mineral diversity on Mars from reflectance. J. Geophys. Res., 2007, 112(E8), doi:10.1029/2006JE002831.
• Viviano-Beck, C. E. et al., Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars. J. Geophys. Res.: Planets, 2014, 119, 6; doi:10.1002/2014JE004627.
• Bhattacharya, S., Chauhan, P., Rajawat, A. S., Ajai and Kumar, A. S. K., Lithological mapping of central part of Mare Moscoviense using Chandrayaan-1 Hyperspectral Imager (HySI) data. Icarus, 2011, 211, 470–479.
• Pieters, C. M., Gaddis, L., Jolliff, B. and Duke, M., Rock types of South Pole-Aitken basin and extent of basaltic volcanism. J. Geophys. Res., 2001, 106, 28001–28022; doi:10.1029/2000JE001414.
• Crawford, A. R., The Precambrian geochronology of Rajasthan and Bundelkhand, northern India. Can. J. Earth Sci., 1970, 7(1), 91–110.
• Sharma, N. L. and Nandy, N. C., A note on the petrological classification of the basic intrusives of Danta State (N. Gujarat). Proc. Indian Acad. Sci., 1936, 3, 366–376.
• Deb, M., Genesis and metamorphism of two stratiform massive sulfide deposits at Ambaji and Deri in the Precambrian of western India. Econ. Geol., 1980, 75(4), 572–591.
• Mustard, J. F. et al., Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data. J. Geophys. Res., Planets, 2011, 116, EOOG12.
• Clark, R. N. and Roush, T. L., Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J. Geophys. Res.: Solid Earth, 1984, 89(B7), 6329–6340.
• Rodger, A., Laukamp, C., Haest, M. and Cudahy, T., A simple quadratic method of absorption feature wavelength estimation in continuum removed spectra. Remote Sensing Environ., 2012, 118, 273–283.
• Hunt, G. R. and Salisbury, J. W., Visible and near infrared spectra of minerals and rocks. II. Carbonates. Modern Geol., 1971, 2, 23– 30.
• Gaffey, S. J., Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 μm); calcite, aragonite, and dolomite. Am. Mineral., 1986, 71(1–2), 151–162.
• Adams, J. B. and McCord, T. B., Optical properties of mineral separates, glass, and anorthositic fragments from Apollo mare samples. In Lunar and Planetary Science Conference Proceedings, 1971, vol. 2, p. 2183.
• Longhi, J., Walker, D. and Hays, J. F., Fe and Mg in plagioclase. In Lunar and Planetary Science Conference Proceedings, 1976, vol. 7, pp. 1281–1300.
• Clark, R. N., Spectroscopy of rocks and minerals, and principles of spectroscopy. Man. Remote Sensing, 1999, 3, 3–58.
• Launeau, P., Girardeau, J., Sotin, C. and Tubia, J. M., Comparison between field measurements and airborne visible and infrared mapping spectrometry (AVIRIS and HyMap) of the Ronda peridotite massif (southwest Spain). Int. J. Remote Sensing, 2004, 25(14), 2773–2792.
• Guha, A., Ghosh, B., Vinod Kumar, K. and Chowdhary, S., Implementation of reflection spectroscopy based new aster indices and principal components to delineate chromitite and associated ultramafic–mafic complex in parts of Dharwar Craton, India. Adv. Space Res., 2015, 56, 1453–14680.
• Rajendran, S. et al., ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: exploration strategy. Ore Geol. Rev., 2012, 44, 121– 135.
• Cooley, T. et al., FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. International Geoscience and Remote Sensing Symposium (IGARSS), 2002, 3, 1414–1418.
• Sultan, S. A., Mansour, S. A., Santos, F. M. and Helaly, A. S., Geophysical exploration for gold and associated minerals, case study: Wadi ElBeida area, South Eastern Desert, Egypt. J. Geophys. Eng., 2009, 6, 345–356.
• Tontini, F. C., de Ronde, C. E., Scott, B. J., Soengkono, S., Stagpoole, V., Timm, C. and Tivey, M., Interpretation of gravity and magnetic anomalies at Lake Rotomahana: geological and hydrothermal implications. J. Volcanol. Geother. Res., 2016, 314, 84– 94.
• Subramaniam, A. P., Mineralogy and petrology of the Sittampundi complex, Salem district, Madras State, India. Geol. Soc. Am. Bull., 1956, 67, 317–390.
• Windley, B. F., Bishop, F. C. and Smith, J. V., Metamorphosed layered igneous complexes in Archean granulite – gneiss belts. Annu. Rev. Earth Planet. Sci., 1981, 9, 175.
• Kraut, S., Scharf, L. L. and Butler, R. W., The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic. IEEE Trans. Signal Process, 2005, 53(2), 427–438.
• Murthy, D. S. N. and Dayal, A. M., Geochemical characteristics of kimberlite rock of the Anantapur and Mahbubnagar districts, Andhra Pradesh, South India. J. Asia Earth Sci., 2001, 19, 311– 319.
• Arindam, G., Vinod Kumar, K., Ravi, S. and Dhanamjaya Rao, S., Reflectance spectroscopy of kimberlite – in parts of Dharwar Craton, India. Arab. J. Geosci., 2015, 8(11), 9373–9388.

• Regional Liquefaction Susceptibility Mapping in the Himalayas using Geospatial Data and AHP Technique

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Authors

Ramesh Pudi ¹, Tapas R. Martha ¹, Priyom Roy ¹, K. Vinod Kumar ¹, P. Rama Rao ²

Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
2 Department of Geophysics, College of Science and Technology, Andhra University, Visakhapatnam 530 003, IN

Source

Current Science, Vol 116, No 11 (2019), Pagination: 1868-1877

Abstract

Liquefaction susceptibility (LS) assessment is a necessary input for seismic zonation studies. LS can be done using geospatial models by integration of thematic layers. In this study, we have used analytical hierarchy process for integration of thematic layers (e.g. water table depth, peak horizontal acceleration, etc.) to generate a regional LS map for Uttarakhand and Himachal Pradesh in India. The final map was classified as liquefaction-likely, liquefaction-possible and liquefaction-not-likely zones. Results show Doon valley and Himalayan foothills are more prone to LS than the higher Himalayas.

Keywords

Analytical Hierarchy Process, Earthquakes, Geospatial Data, Liquefaction Susceptibility.

Full Text

   

References

• Youd, T. L., Liquefaction, flow and associated ground failure. Circular 688, US Geological Survey, 1973.
• Sonmez, H. and Gokceoglu, C., A liquefaction severity index suggested for engineering practice. Environ. Geol., 2005, 48(1), 81–91.
• Seed, H. B. and Idriss, I. M., Simplified procedure for evaluating soil liquefaction potential. J. Soil Mech. Found. Div., ASCE, 1971, 97(9), 1249–1273.
• Iwasaki, T., Tokida, K., Tatsuoka F., Watanabe, S., Yasuda, S. and Sato, H., Microzonation for soil liquefaction potential using simplified methods. In Proceedings of the third International Earthquake Microzonation Conference, 1982, vol. 3, pp. 1319– 1330.
• Seed, H. B., Tokimatsu, K., Harder Jr, L. F. and Chung, R., Influence of SPT procedures in soil liquefaction resistance evaluations. J. Geotech. Eng. Div., 1985, 111(12), 1425–1445.
• Youd, T. L. and Idriss, I. M., Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J. Geotech. Geoenviron. Eng., 2001, 127(4), 297–313.
• Youd, T. L. et al., Liquefaction resistance of soils: summary report from the 1996 NCEER 14 and 1998 NCEER/NSF workshop on evaluation of liquefaction resistance of soils. J. Geotech. Geoenviron. Eng., 2001, 127(10), 817–833.
• Youd, T. L. and Perkins, D. M., Mapping liquefaction induced ground failure potential. J. Geotech. Eng. Div., 1978, 104(4), 433– 446.
• Chung, J. W., Rogers, J. D., P. E. and ASCE, M., Simplified method for spatial evaluation of liquefaction potential in the St. Louis Area. J. Geotech. Geoenviron. Eng., 2011, 137, 505–515; doi:10.1061/(ASCE) GT.1943-5606.0000450.
• Kramer, S. L., Geotechnical Earthquake Engineering, Prentice Hall, Upper Saddle River, NJ, USA, 1996.
• Tuttle, M., Chester, J., Lafferty, R., Dyer-Williams, K. and Cande, B., Paleoseismology study northwest of the New Madrid Seismic Zone. US Nuclear Regulatory Commission, 1999.
• Marcuson III, W. F, Definition of terms related to liquefaction. J. Geotech. Eng. Div., ASCE, 1978, 104(9), 1197–1200.
• Wakamatsu, K., Evaluation of liquefaction susceptibility based on detailed geomorphological classification. In Proceedings of Technical Papers of Annual Meeting, Architectural Institute of Japan, 1992, vol. B, pp. 1443–1444.
• Torres, R. C., Paladio, M. L., Punongbayan, R. S. and Alonso, R. A., Liquefaction inventory and mapping in the Philippines. In National Disaster Mitigation in the Philippines. Proceedings of the National Conference on Natural Disaster Mitigation, DOSTPHIVOLCS, Philippine, 1994, pp. 45–60.
• Akin, K. M., Topal, T. and Kramer, S. L., A newly developed seismic microzonation model of Erbaa (Tokat, Turkey) located on seismically active eastern segment of the North Anatolian Fault Zone (NAFZ). Nat. Hazards, 2013, 65, 1411–1442; doi:10.1007/ s11069-012-0420-1.
• Taskin, B., Sezen, A. and Tugsal, U. M., The aftermath of 2011 Van earthquakes: evaluation of strong motion, geotechnical and structural issues. Bull. Earthq. Eng., 2013, 11, 285.
• Holzer, T. L., Noce, T. E. and Bennett, M. J., Scenario liquefaction hazard maps of Santa Clara Valley, northern California. Bull. Seismol. Soc. Am., 2009, 99, 367–381.
• Brankman, C. M., amd Baise, L. G., Liquefaction susceptibility mapping in Boston, Massachusetts. Environ. Eng. Geosci., 2008, 14, 1–16.
• Zhu, J., Baise, L. G. and Thompson, E. M., An updated geospatial liquefaction model for global application. Bull. Seismol. Soc. Am., 2017, 107(3), 1365–1385; doi:10.1785/0120160198.
• Baise, L. G., Daley, D., Zhu, J., Thompson, E. M. and Knudsen, K., Geospatial liquefaction hazard model for Kobe, Japan and Christchurch, New Zealand. Seismol. Res. Lett., 2012, 83, 458.
• Ganapathy, G. P. and Rajawat, A. S., Evaluation of liquefaction potential hazard of Chennai City, India: using geological and geomorphological characteristics. Nat. Hazards, 2012, 64(2), 1717–1729.
• Zhu, J., Daley, D., Baise, L. G., Thompson, E. M., Wald, D. J. and Knudsen, K. L., A geospatial liquefaction model for rapid response and loss estimation. Earthq. Spectra, 2015, 31, 1813– 1837.
• Puri, N. and Jain, A., Preliminary investigation for screening of liquefiable areas in Haryana state, India. ISET J. Earthq. Technol., 2014, 51(1–4), 19–34.
• Sekac, T., Jana, S. K., Pal, I. and Pal, D. K., A GIS based approach into delineating liquefaction susceptible zones through assessment of site–soil–geology – a case study of Madang and Morobe Province in Papua New Guinea (PNG). Int. J. Innov. Res. Sci., Eng. Technol., 2016, 3(8), 6616–6629.
• Oldham, T. A., catalogue of Indian earthquakes from the earliest to the end of 1869. Mem. Geol. Surv. India, 1883, 19(1), 163–215.
• Rajendran, C. P., Rajendran, K., Sanwal, J. and Sandiford, M., Archeological and historical database on the medieval earthquakes of the Central Himalaya: ambiguities and inferences. Seismol. Res. Lett., 2013, 84(6), 1098–1108.
• Hough, S. E. and Bilham, R., Site response of the Ganges basin inferred from re-evaluated macroseismic observations from the 1897 Shillong, 1905 Kangra, and 1934 Nepal earthquakes. J. Earth Syst. Sci., 2008, 117(S2), 773–782.
• Rajendran, C. P., John, B., Rajendran, K. and Sanwal, J., Liquefaction record of the great 1934 earthquake predecessors from the north Bihar alluvial plains of India. J. Seismol., 2016; doi:10.1007/s10950-016-9554-z.
• Wadia, D. N., The Geology of India, Macmillan, New York, USA, 1957, 3rd edn., pp. 353, 383.
• Gansser, A., Geology of the Himalayas, Interscience, New York, USA, 1964, p. 289.
• Valdiya, K. S., Geology of the Kumaun Lesser Himalaya, The Himachal Press, Wadia Institute of Himalayan Geology, Dehradun, 1980, p. 219.
• BIS IS 1893–2002 (Part 1): Indian standard criteria for earthquake resistant design of structures. Part 1 – general provisions and buildings. Bureau of Indian Standards, New Delhi, 2002.
• Knudsen, K. and Bott, J., Geologic and geomorphic evaluation of liquefaction case histories for rapid hazard mapping. Seismol. Res. Lett., 2011, 82, 334.
• Idriss, I. M. and Boulanger, R. W., Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dyn. Earthq. Eng., 2006, 26, 115–130.
• Shankar, D. and Shubham, On the seismic hazard in Himachal Pradesh and Utarakhand States. Geoscience, 2018, 8(2), 21–31.
• Saaty, T. L., The Analytic Hierarchy Process, McGraw-Hill International, New York, USA, 1980.
• Obermeier, S. F., Use of liquefaction induced features for seismic analysis – an overview of how seismic liquefaction features can be distinguished from other features and how their regional distribution and properties of source sediment can be used to infer the location and strength of holocene Paleo-earthquakes. Eng. Geol., 1996, 44, 1–76.
• Thakur, V. C., Active tectonics of Himalayan Frontal Thrust and seismic hazard to Ganga Plain. Curr. Sci., 2004, 86(11), 1554– 1560.
• Mahajan, A. K. and Virdi, N. S., Macroseismic field generated by 29 March 1999 Chamoli earthquake and its seismotectonics. J. Asian Earth Sci., 2001, 19, 507–516.
• Malik, J. N., Sahoo, A. K., Shah, A. A., Rawat, A. and Chaturved, A., Farthest recorded liquefaction around Jammu caused by 8 October 2005, Muzaffarabad earthquake of MW = 7.6. J. Geol. Soc. India, 2007, 69, 39–61.
• Mahajan, A. K., Slob, S., Ranjan, R., Sporry, R., Champati Ray, P. K. and Van Westen, C. J., Seismic microzonation of Dehradun City using geophysical and geotechnical characteristics in the upper 30 m of soil column. J. Seismol., 2007, 11, 355–370; doi:10.1007/s10950-007-9055-1.
• Mahajan, A. K., NEHRP soil classification and estimation of 1-D site effect of Dehradun fan deposits using shear wave velocity. Eng. Geol., 2009, 104, 232–240.
• Mahajan, A. K., Thakur, V. C., Sharma, M. L. and Chauhan, M., Probabilistic seismic hazard map of NW Himalaya and its adjoining area, India. Nat. Hazards, 2010, 53, 443–457; doi:10.1007/ s11069-009-9439-3.
• Sathyaseelan, R., Mundepi, A. K. and Kumar, N., Quantifying seismic vulnerability, dynamical shear strain and liquefaction of the Quaternary deposits in the Doon valley near the Main Boundary Thrust in the Northwest Himalaya, India. Quaternary Int., 2017, 462, 162–175.
• Kotoda, K., Wakamatsu, K. and Oya, M., Mapping liquefaction potential based on geomorphological land classification. In Proceedings of Ninth World Conference on Earthquake Engineering, Tokyo-Kyoto, Japan, 1988, vol. 3, p. 195.
• Wakamatsu, K., Akio, Y. and Ichiro, T., Geomorphological criteria for evaluating liquefaction potential considering the level-2 ground motion in Japan (26 March 2001). In International Conference Recent Advances in Geotechnical Earthquake Engineering Soil Dynamics, 2001; paper http://scholarsmine.mst.edu/icrageesd/04icrageesd/session04/3
• Witter, R. C. et al., Maps of Quaternary deposits and liquefaction susceptibility in the central San Francisco Bay region, California. US Geol Survey Open-File Report 06-1037, 2006; http://pubs.usgs.gov/of/2006/1037/
• GSI and NRSC, Manual for national geomorphological and lineament mapping on 1 : 50,000 scale. (Document control number: NRSC-RS&GISAA-ERG-G&GD-FEB’ 10-TR149), National Remote Sensing Centre, Hyderabad, 2010.
• Martha, T. R., Ghosh, D., Kumar, K. V., Lesslie, A. and Ravi Kumar, M. V., Geospatial technologies for national geomorphology and lineament mapping project – a case study of Goa state. J. Indian Soc. Remote Sensing, 2013, 41(4), 905–920; doi:10.1007/s12524012-0260-1.
• http//nbsslup.in