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Martha, Tapas R.
- Assessment of the Sunkoshi (Nepal) Landslide Using Multitemporal Satellite Images
Abstract Views :310 |
PDF Views:121
Authors
Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
Source
Current Science, Vol 107, No 12 (2014), Pagination: 1961-1964Abstract
No Abstract.Full Text
- A Bird's-Eye View of Landslide Dammed Lakes in Zanskar Himalaya, India
Abstract Views :445 |
PDF Views:100
Authors
Affiliations
1 Geosciences Group, Remote Sensing Applications Area, National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad 500 037, IN
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: 1109-1112Abstract
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.Full Text
References
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- 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.
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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
Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
Source
Current Science, Vol 113, No 07 (2017), Pagination: 1228-1229Abstract
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.Full Text
References
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- 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.
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- 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 :200 |
PDF Views:100
Authors
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
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: 1338-1345Abstract
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.Full Text
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.
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- 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.
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- 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.
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- Reactivation of Minor Scars to Major Landslides–A Satellite-Based Analysis of Kotropi Landslide (13 August 2017) In Himachal Pradesh, India
Abstract Views :262 |
PDF Views:81
Authors
Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad - 500 037, IN
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad - 500 037, IN
Source
Current Science, Vol 115, No 3 (2018), Pagination: 395-398Abstract
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.Full Text
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.
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- Landslides Mapped using Satellite Data in the Western Ghats of India After Excess Rainfall During August 2018
Abstract Views :269 |
PDF Views:102
Authors
Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
Source
Current Science, Vol 117, No 5 (2019), Pagination: 804-812Abstract
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.Full Text
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).
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- 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.
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- 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.
- Regional Liquefaction Susceptibility Mapping in the Himalayas using Geospatial Data and AHP Technique
Abstract Views :236 |
PDF Views:80
Authors
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
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: 1868-1877Abstract
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
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