Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Singh, Gulab
- Remote Sensing Analysis of Changes in Chorabari Glacier, Central Himalaya, India
Abstract Views :283 |
PDF Views:99
Authors
Affiliations
1 Centre for Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
1 Centre for Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
Source
Current Science, Vol 114, No 04 (2018), Pagination: 771-775Abstract
In this study, mass balance for Chorabari glacier of the Central Himalaya, India has been estimated. This glacier has been considered for the case study as it feeds the Mandakini River and was one of the reasons for flash floods in Rudraprayag district, Uttarakhand in 2013. The observations are based on glacier area/length change and rate of melting in the multidecade (1976–2016) using Landsat data. The study estimates an overall decrease in area at 0.8% per year. Elevation change has been studied using geodetic method utilizing shuttle radar topography mission and TanDEM-X datasets, which have shown a decrease in elevation in 16 years (2000–16). From these remotely observed parameters, a negative mass balance for the decade 2000–11 indicates glacier retreat. This study highlights the applicability of optical and radar remote sensing for the Himalayan glaciers, for better disaster management and understanding glacier dynamics in response to climate change.Keywords
Chorabari, Elevation Change, Mass Balance, Remote Sensing Analysis.Full Text
References
- Wagnon, P. et al., Four years of mass balance on Chhota Shigri Glacier, Himachal Pradesh, India, a new benchmark glacier in the western Himalaya. J. Glaciol., 2007, 53, 603–611.
- Kulkarni, A. V. and Karyakarte, Y., Observed changes in Himalayan glaciers. Curr. Sci., 2014, 106, 237–244.
- Pathak, J., Measuring glacier change in the Himalayas. Environ. Dev., 2012, 4, 172–183.
- Bolch, T. et al., The state and fate of Himalayan Glaciers. Science, 2012, 336, 310–314.
- Mehta, M. et al., Monitoring of glacier changes and response time in Chorabari Glacier, Central Himalaya, Garhwal, India. Curr. Sci., 2014, 107, 281–288.
- Mehta, M., Majeed, Z., Dobhal, D. P. and Srivastava, P., Geomorphological evidences of post-LGM glacial advancements in the Himalaya: a study from Chorabari Glacier, Garhwal Himalaya, India. J. Earth Syst. Sci., 2012, 121, 149–163.
- Gonzalez, C., Brautigam, B., Martone, M. and Rizzoli, P., Relative height error estimation method for TanDEM-X DEM products. Proc. EuSAR, 2014, pp. 1001–1004.
- Werner, M., Shuttle radar topography mission (SRTM): experience with the X-band SAR interferometer. In Radar 2001 CIE International Conference Proceedings (Cat No. 01TH8559), 2000, pp. 634–638; doi:10.1109/ICR.2001.984798.
- Deo, R., Manickam, S., Rao, Y. S. and Gedam, S. S., Evaluation of interferometric SAR DEMs generated using TanDEM-X data. In International Geoscience and Remote Sensing Symposium, IEEE International, 2013, pp. 2079–2082; doi:10.1109/IGARSS.2013.6723221.
- Pratap, B., Dobhal, D. P., Bhambri, R., Mehta, M. and Tewari, V. C., Four decades of glacier mass balance observations in the Indian Himalaya. Reg. Environ. Change, 2016, 16, 643–658.
- Rufino, G., Moccia, A. and Esposito, S., DEM generation by means of ERS TanDEM data. IEEE Trans. Geosci. Remote Sensing, 1998, 36(6), 1905–1912.
- Basnett, S., Kulkarni, A. V. and Bolch, T., The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya, India. J. Glaciol., 2013, 59, 1035–1046.
- Dobhal, D. P., Mehta, M. and Srivastava, D., Influence of debris cover on terminus retreat and mass changes of Chorabari Glacier, Garhwal region, central Himalaya, India. J. Glaciol., 2013, 59, 961–971.
- Rizzoli, P., Bräutigam, B., Kraus, T., Martone, M. and Krieger, G., Relative height error analysis of TanDEM-X elevation data. ISPRS J. Photogramm. Remote Sensing, 2012, 73, 30–38.
- Pandey, P. and Venkataraman, G., Comparison of DEMs Derived from TanDEM-X and SRTM-C for Himalayan Centre of Studies in Resources Engineering, Indian Institute of Technology, Bombay, 2013, pp. 322–325.
- Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images
Abstract Views :163 |
PDF Views:94
Authors
Affiliations
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
Source
Current Science, Vol 120, No 5 (2021), Pagination: 915-925Abstract
In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.Keywords
Disaster Management, Floods, Inundation Mapping, Remote Sensing, Rule-based Classification.Full Text
References
- Huang, X. et al., Flood hazard in hunan province of China: an economic loss analysis. Nat. Hazards, 2008, 47, 65–73.
- Rijal, S., Rimal, B. and Sloan, S., Flood hazard mapping of a rapidly urbanizing city in the foothills (Birendranagar, Surkhet) of Nepal. Land, 2018, 7, 60.
- Ologunorisa, T. and Abawua, M., Flood risk assessment: a review.J. Appl. Sci. Environ. Manage., 2005, 9, 57–63.
- Panhalkar, S. and Jarag, A. P., Flood risk assessment of Panchganga River (Kolhapur district, Maharashtra) using GIS-based multicriteria decision technique. Curr. Sci., 2017, 112, 785–793.
- Cleve, C., Kelly, M., Kearns, F. R. and Moritz, M., Classification of the wildland–urban interface: a comparison of pixel-and objectbased classifications using high-resolution aerial photography. Comput. Environ. Urban Syst., 2008, 32, 317–326.
- Horritt, M. S., Mason, D. C. and Luckman, A. J., Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model. Int. J. Remote Sensing, 2001, 22, 2489–2507.
- Horritt, M., Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sensing Environ., 2003, 85, 271–281.
- Zhou, C., Luo, J., Yang, C., Li, B. and Wang, S., Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogrammetric Eng. Remote Sensing, 2000, 66(5), 633–638.
- Pierdicca, N., Pulvirenti, L., Boni, G., Squicciarino, G. and Chini, M., Mapping flooded vegetation using COSMO-SkyMed: comparison with polarimetric and optical data over rice fields. IEEE J. Selected Top. Appl. Earth Observ. Remote Sensing, 2017, 10, 2650–2662.
- Hess, L. L., Melack, J. M. and Simonett, D. S., Radar detection of flooding beneath the forest canopy: a review. Int. J. Remote Sensing, 1990, 11, 1313–1325.
- Hess, L. L., Melack, J. M. and Davis, F. W., Mapping of floodplain inundation with multi-frequency polarimetric SAR: use of a tree-based model. In Proceedings of IGARSS ’94 – IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 1994, pp. 1072–1073.
- Hess, L. L., Melack, J. M., Filoso, S. and Wang, Y., Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Trans. Geosci. Remote Sensing, 1995, 33, 896–904.
- Hess, L. L. and Malack, J. M., Mapping floodplain vegetation in the central Amazon basin with multi-temporal SIR-C data. In IGARSS 98 – Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings (Cat. No.98CH36174), IEEE, Seattle, WA, USA, 1998, vol. 4, p. 2115.
- Hess, L., Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sensing Environ., 2003, 87, 404–428.
- Arnesen, A. S. et al., Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sensing Environ., 2013, 130, 51–61.
- Alahacoon, N., Matheswaran, K., Pani, P. and Amarnath, G., A decadal historical satellite data and rainfall trend analysis (2001– 2016) for flood hazard mapping in Sri Lanka. Remote Sensing, 2018, 10, 448.
- Martinis, S. and Rieke, C., Backscatter analysis using multitemporal and multi-frequency SAR data in the context of flood mapping at River Saale, Germany. Remote Sensing, 2015, 7, 7732–7752.
- Pierdicca, N., Chini, M., Pulvirenti, L. and Macina, F., Integrating physical and topographic information into a Fuzzy scheme to map flooded area by SAR. Sensors, 2008, 8, 4151–4164.
- Pulvirenti, L., Pierdicca, N., Boni, G., Fiorini, M. and Rudari, R., Flood damage assessment through multitemporal COSMOSkyMed data and hydrodynamic models: the Albania 2010 case study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2014, 7, 2848–2855.
- Pulvirenti, L., Marzano, F. S., Pierdicca, N., Mori, S. and Chini, M., Discrimination of water surfaces, heavy rainfall and wet snow using COSMO-SkyMed observations of severe weather events. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 858–869.
- Voormansik, K., Praks, J., Antropov, O., Jagomagi, J. and Zalite, K., Flood mapping with TerraSAR-X in forested regions in estonia. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2014, 7, 562–577.
- Cao, H., Zhang, H., Wang, C. and Zhang, B., Operational flood detection using Sentinel-1 SAR data over large areas. Water, 2019, 11, 786.
- Anusha, N. and Bharathi, B., Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data. Egypt. J. Remote Sensing Space Sci., 2020, 23, 207–219.
- Kundu, S., Aggarwal, S., Kingma, N., Mondal, A. and Khare, D., Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India. Natural Hazards, 2015, 76, 123– 138.
- Baghdadi, N., Bernier, M., Gauthier, R. and Neeson, I., Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sensing, 2001, 22, 71–88.
- Greifeneder, F., Wagner, W., Sabel, D. and Naeimi, V., Suitability of SAR imagery for automatic flood mapping in the Lower Mekong basin. Int. J. Remote Sensing, 2014, 35, 2857–2874.
- Yulianto, F., Sofan, P., Zubaidah, A., Sukowati, K. A. D., Pasaribu, J. M. and Khomarudin, M. R., Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia. Nat. Hazards, 2015, 77, 959–985.
- Chini, M., Hostache, R., Giustarini, L. and Matgen, P., A hierarchical split-based approach for parametric thresholding of SAR images: flood inundation as a test case. IEEE Trans. Geosci. Remote Sensing, 2017, 55, 6975–6988.
- Giustarini, L., Hostache, R., Matgen, P., Schumann, G. J.-P., Bates, P. D. and Mason, D. C., A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sensing, 2013, 51, 2417–2430.
- Refice, A. et al., SAR and InSAR for flood monitoring: examples with COSMO-SkyMed data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2014, 7, 2711–2722.
- Pulvirenti, L., Chini, M., Pierdicca, N. and Boni, G., Use of SAR data for detecting floodwater in urban and agricultural areas: the role of the interferometric coherence. IEEE Trans. Geosci. Remote Sensing, 2016, 54, 1532–1544.
- Amitrano, D., Di Martino, G., Iodice, A., Riccio, D. and Ruello, G., Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans. Geosci. Remote Sensing, 2018, 56, 3290– 3299.
- Martinis, S., Plank, S. and Ćwik, K., The use of Sentinel-1 timeseries data to improve flood monitoring in arid areas. Remote Sensing, 2018, 10, 583.
- Jo, M.-J., Osmanoglu, B., Zhang, B. and Wdowinski, S., Flood extent mapping using dual-polarimetric Sentinel-1 synthetic aperture radar imagery. ISPRS – Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2018, XLII–3, 711–713.
- Tsyganskaya, V., Martinis, S., Marzahn, P. and Ludwig, R., Detection of temporary flooded vegetation using Sentinel-1 time series data. Remote Sensing, 2018, 10, 1286.
- Plank, S., Jüssi, M., Martinis, S. and Twele, A., Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS2/PALSAR-2 imagery. Int. J. Remote Sensing, 2017, 38, 3831–3850.
- Ramsar, Annotated list of wetlands of international importance, 2018; http://saconenvis.nic.in/publication/Ramsar-Sites-annotatedsummaryIndia.pdf
- India Meteorological Department, Performance of South West Monsoon 2018 over Kerala. Meteorological Centre, Thiruvananthapuram, 2018, pp. 1–16.
- Bhatt, C. M., Rao, G. S., Diwakar, P. G. and Dadhwal, V. K., Development of flood inundation extent libraries over a range of potential flood levels: a practical framework for quick flood response. Geomat., Nat. Hazards Risk, 2017, 8(2), 384–401.
- Chung, H.-W., Liu, C.-C., Cheng, I.-F., Lee, Y.-R. and Shieh, M.-C., Rapid response to a typhoon-induced flood with an SARderived map of inundated areas: case study and validation. Remote Sensing, 2015, 7, 11954–11973.
- Stephens, E., Schumann, G. and Bates, P., Problems with binary pattern measures for flood model evaluation. Hydrol. Process., 2014, 28, 4928–4937.
- Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R. and Chokmani, K., Flood mapping in a complex environment using bistatic TanDEM-X/TerraSAR-X InSAR coherence. Remote Sensing, 2018, 10(12), 1873.
- Feng, Q., Liu, J. and Gong, J., Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier – a case of Yuyao, China. Water, 2015, 7, 1437–1455.
- Feng, Q., Gong, J., Liu, J. and Li, Y., Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier – the case of Yuyao, China. Remote Sensing, 2015, 7, 12539–12562.
- CWC, Study Report: Kerala Flood of August 2018. Hydrological Studies Organization, Central Water Commission, Government of India, 2018, pp. 1–46.