Adopting Multisensor Remote Sensing Datasets and Coupled Models for Disaster Management

  • Gilbert L. Rochon
  • Dev Niyogi
  • Alok Chaturvedi
  • Rajarathinam Arangarasan
  • Krishna Madhavan
  • Larry Biehl
  • Joseph Quansah
  • Souleymane Fall
Part of the Environmental Science and Engineering book series (ESE)


An application and a process involving integration of dynamic models for a data-rich environment, incorporating a multi sensor dataset is discussed. The potential utility of such data fusion for different phases of disaster management: vulnerability assessment, early warning systems, disaster mitigation, response, damage assessment and recovery are delineated. Case studies are drawn from disaster scenarios for flooding, drought management, and heavy rains in India. Applicability of the technology and processes, with potentially different sources of data, is described. Solutions to several technological challenges to handle large data sets using distributed cluster technology and data visualization, using high-resolution large display systems, are presented. Taking an example of the July 26, 2005 heavy rain events in Mumbai, India, which caused flooding, and resulted in over 400 deaths and nearly a billion US economic losses, the ability of multiple models to study the predictability, variability and use of model – satellite data fusion for severe weather and disaster mitigation, as well as response needs is discussed. A case for multisensory satellite datasets and the use of upcoming technologies, including handheld computers and cell phones in facilitating early warning, evacuation and emergency intervention is addressed. A case is made for a technological and educational infrastructure development that can benefit from remote sensing centric models with different complexity and a community cyberinfrastructure for multidata access for disaster management.


Geographic Information System Disaster Management Tropical Rainfall Measurement Mission Early Warning System Heavy Rain Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Blaschke T, 1999, Sustainability with GIS: geo/people/tblaschke/ publications/ sustain_abs.html.Google Scholar
  2. Breman JG, Alilio MS, and Mills A, 2001, The Intolerable Burden of Malaria: A New Look at the Numbers, American Journal of Tropical Medicine and Hygiene 64 (1, 2 Suppl): 1-106.Google Scholar
  3. Chang Hsin-I, A. Kumar, D. Niyogi, U. C. Mohanty, F. Chen, and J. Dudhia, 2008, Impact of convection and land surface parameterizations on the simulation of the July 26, 2005 heavy rain event over Mumbai, India. Global Planetary Change, accepted.Google Scholar
  4. Charter on Cooperation to Achieve the Coordinated Use of Space Facilities in the Event of Natural or Technological Disasters, 2000, European Space Agency (ESA), French Space Agency (CNES), Canadian Space Agency (CSA) Rev. 3.2 (May 25, 2000).Google Scholar
  5. Chenoweth JL, Sarah A, and Bird E and JF, 2002, Procedures for Ensuring Community Involvement in Multi-jurisdictional River Basins: A Comparison of the Murray-Darling and Mekong River Basins, Environmental Management, 29: 497-509.CrossRefGoogle Scholar
  6. Coops NC., Wulder MA, and JC White, 2006, Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation, Remote Sensing of Environment, 105, 83-97.CrossRefGoogle Scholar
  7. Cyranoski D, 2005, Solo Efforts Hamper Tsunami Warning, Nature, 433, Jan. 27, 2005, p. 343.Google Scholar
  8. Data Sharing for Disasters, 2005, Editorial, Nature, 433, 271, January, 2005, p. 339.Google Scholar
  9. De US, RK Dube, GS Prakasa Rao, 2005, Extreme weather events over India in the last 100 years, J. Ind. Geophys. Union, 9, 173–187.Google Scholar
  10. Douglas E, Niyogi D, Frolking S, Yeluripati JB, Pielke, RA Sr., Niyogi N, Vörösmarty CJ, Mohanty UC, 2006, Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon belt, Geophysical Research Letters, 33, L14403, doi:10.1029/2006GL026550.CrossRefGoogle Scholar
  11. Dungeon D, 2000, Large–Scale Hydrological Changes in Tropical Asia: Prospects for Riverine Biodiversity. Cambridge University Press, Cambridge, UK.Google Scholar
  12. Flood Management and Mitigation in the Mekong River Basin, 1998, FAO of the UN. Proceedings of the Regional Workshop. Vientiane, Lao PDR, 19-21 March. RAP Publication # 1999/14. AC146E00.htm # TOC.Google Scholar
  13. Filippidis A, Jain LC, and Martin NM, 1999, Using Genetic Algorithms and Neural Networks for Surface Land Mine Detection. IEEE Transactions on Signal Processing, 47 (1): 176-186.CrossRefGoogle Scholar
  14. Finkelstein ND, Adams WK, Kller CJ, Kohl PB, Perkins KK,, Podolefsky S, Reid S., Lemaster R., 2005, When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment, Phys. Rev. ST Phys. Educ. Res., 1, 010103, 8 pages.Google Scholar
  15. Fox J, Ledgerwood J, 1999, Dry-Season Flood-Recession Rice in the Mekong Delta: Two Thousand Years of Sustainable Agriculture? Asian Perspectives: the Journal of Archaeology for Asia and the Pacific, 38.Google Scholar
  16. global Land Cover Facility (GLCF). Tsunami + Remote Sensing + Protected Areas. NASA and University of Maryland, Dept. of Geography & Institute for Advanced Computer Studies. Scholar
  17. Govindaraju , S., et al. 2008, A cyberinfrastructure for environmental exploration, engagement, and education (C4E4), ASCE J. Hydrol. Engg (Cyberinfrastructure for Environmental Observations- Special issue), in press.Google Scholar
  18. Grose C MD, 2004, Avian Influenza Virus Infection of Children in Vietnam and Thailand. Pediatric Infectious Disease Journal. 23: 793-794.CrossRefGoogle Scholar
  19. Gupta A, Chen P, 2001, Remote Sensing and Environmental Management in the Mekong Basin, 22nd Asian Conference on Remote Sensing, Singapore, 5-9 November.Google Scholar
  20. Habib MK, 2001, Mine Detection and Sensing Technologies-New Development Potentials in the Context of Humanitarian Demining. IECON’01 27th Annual Conference of the IEEE Industrial Electronics Society, 1612-1621.Google Scholar
  21. Harris P, 2006, NGIS-Australia: Mekong River Commission: Remote Sensing & Flood Management. International+Projects/ 59.aspx.Google Scholar
  22. Holecz F, Heimo C, Moreno J, Goussard J, Fernandez D, Luis Rubio J, Erxue C, Magsar E, Lo M, Chemini A, Stoessel F, and Rosenqvist A, 2003, Desertification – A land degradation support service. IEEE 3:1490 – 1492.Google Scholar
  23. Italian Cooperation & UNCCD, 1999. Early warning systems and desertification, pp. 30.Google Scholar
  24. IPCC, 2007: The intergovernmental panel on climate change – accessed November 2007.Google Scholar
  25. Jacobs JW 1994, Toward Sustainability in Lower Mekong River Basin Development. Water International, 19, 43-51.CrossRefGoogle Scholar
  26. Kogan FN, 1995, Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data. Bull. Amer. Meteor. Soc., 76, 655–668.CrossRefGoogle Scholar
  27. Kovacs JM, Wang J, and Blanco-Correa M, 2001, Mapping disturbances in a mangrove forest using multi-date Landsat TM imagery. Environmental Management, 27: 763-776.CrossRefGoogle Scholar
  28. Kumar A, Dudhia J, Rotunno R, Weismann M, Niyogi D, and Mohanty UC, 2008, Analysis of the 26 July 2005 Heavy Rain event over Mumbai, Quart. J. Roy. Meteorol. Soc., in review.Google Scholar
  29. Laben C, 2002, Integration of Remote Sensing Data and Geographic Information System Technology for Emergency Managers and their Applications at the Pacific Disaster Center, Optical Engineering, 41: 2129-2136.CrossRefGoogle Scholar
  30. Lanly J, 1982, Tropical forest resources. Food and Agriculture Organization of the United Nations (FAO) paper no. 30. Rome: FAO, UN.Google Scholar
  31. Lantieri, D, 2003, Potential use of satellite remote sensing for land degradation assessment in drylands. Published by LADA, pp. 77.Google Scholar
  32. Lei M., D. Niyogi, C. Kishtawal, R. Pielke Sr., A. Beltrán-Przekurat, T. Nobis, and S. Vaidya, 2008, Effect of explicit urban land surface representation on the simulation of the 26 July 2005 heavy rain event over Mumbai, India, Atmos. Chem. Phys. Discussions, accepted.Google Scholar
  33. Linthicum, KJ, 1999, Climate & Satellite indicators to forecast Rift Valley Fever epidemics in Kenya. Science 285, 397-4000.CrossRefGoogle Scholar
  34. Mason PJ, Rosenbaum MS, 2002, Geohazard mapping for predicting landslides: an example from the Langhe Hills in Piemonte, NW Italy. Quarterly Journal of Engineering Geology & Hydrogeology 35: n317-326; DOI: 10.1144/1470-9236/00047.CrossRefGoogle Scholar
  35. MCEER, 2005, Multidisciplinary Center for Earthquake Engineering Research (MCEER), Remote Sensing Technologies Applied Following South Asian Tsunami, tsunami/ default.asp.Google Scholar
  36. Netzband M, Stefanov WL, 2004, Urban Land Cover and Spatial Variation Using ASTER and MODIS Satellite Image Data. ISPRS Commission VII, Istanbul, Turkey.Google Scholar
  37. Norman DA, Spohrer JC, 1996, Learner centered education, Communicaitons of the ACM, 39, 24 – 27.CrossRefGoogle Scholar
  38. Pielke Sr R.A., Stokowski D, Wang J.-W., Vukicevic T., Leoncini G., Matsui T, Castro C., Niyogi D., Kishtawal CM, Biazar A., Doty K., McNider RT, Nair US, Tao EK, 2007: Satellite-based model parameterization of diabatic heating. EOS, 88, 96-97CrossRefGoogle Scholar
  39. Pielke Sr. R.A., and D. Niyogi, 2008, The role of landscape processes within the climate system. In: Otto, J.C. and R. Dikaum, Eds., Landform - Structure, Evolution, Process Control: Proceedings of the International Symposium on Landforms organised by the Research Training Group 437. Lecture Notes in Earth Sciences, Springer, Vol. 115, in press.Google Scholar
  40. Prince SD, SN Goward, 1995, Global primary production: A remote sensing approach, J. Biogeography, 22, 815–835, doi:10.2307/2845983CrossRefGoogle Scholar
  41. Pyle P., D. Niyogi, S. P. Arya, M. Shepherd, F. Chen, B. Wolfe, 2008, An Observational and Modeling-based Storm Climatology Assessment for the Indianapolis, urban region, J. Appl. Meteorol. and Clim., in revision.Google Scholar
  42. Quarmby N, Cushnie J, 1989, Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England, International Journal of Remote Sensing 10: 593-963.CrossRefGoogle Scholar
  43. Reichle RH, Koster RD, Dong J, Berg A.A., 2004, Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation. J. Hydrometeor., 5, 430–442.CrossRefGoogle Scholar
  44. Reigeluth CM, Schwartz E, 1989, An Instructional Theory for the Design of Computer-Based Simulations, Journal of Computer-Based Instruction, 16, 1-10.Google Scholar
  45. Rochon GL, Loring NF, Jafvert CT, Stuart JA, Mohtar RH, Quansah J, and Martin A, 2006, Education in Sustainable Production in US Universities, Clean Technologies and Environmental Policy 8: 38-48.CrossRefGoogle Scholar
  46. Springer Verlag, 2004, Originally presented to the NATO CCMS Pilot Study on Clean Products & Processes Annual Meeting, May 2-6, Budapest, Hungary.Google Scholar
  47. Rochon GL, Johannsen C, Landgrebe D, Engel B, Harbor J, Majumder S, and Biehl L, 2004, Remote Sensing for Monitoring Sustainability, In: Sikdar SK, Glavič P, Jain R (eds) Technological Choices for Sustainability. Springer-Verlag Publishers, Berlin, Heidelberg & NY.Google Scholar
  48. Rochon GL, Szlag D, Daniel FB, and Chifos C, 2002, Remote Sensing Applications for Sustainable Watershed Management and Food Security, In: Begni G (ed), Observing our Environment from Space: New Solutions for a New Millennium. A.A. Balkema Publishers, Lisse, The Netherlands.Google Scholar
  49. Rogers DJ, Randolph S, 1993, Monitoring Trypanosomiasis in Space and Time, Parasitology 351: 739-741.Google Scholar
  50. Sanderson SE, Redford KH, 2003, Contested Relationships between Biodiversity Conservation and Poverty Alleviation. Cambridge University Press, Cambridge.Google Scholar
  51. Shepherd M., Burian S., 2003, Detection of Urban-Induced Rainfall Anomalies in a Major Coastal City. Earth Interactions, 7, 1–17.CrossRefGoogle Scholar
  52. Sims A., Niyogi D, Raman S, 2002, Adopting Drought Indices for Estimating Soil Moisture: A North Carolina case study, Geophysical Research Letters, 29 , 241 – 244.CrossRefGoogle Scholar
  53. Sinha AK, 2003, Development of an integrated disaster management system for India: Importance of reliable information. National Center for Disaster Management, India.Google Scholar
  54. Tucker CJ, Choudhury BJ, 1997, Satellite remote sensing of drought conditions, Remote Sensing of Environment, 23, 243–251.CrossRefGoogle Scholar
  55. UNDP India, 2007, Disaster Preparedness and Response Plan, Ver. 45, 17 July 2007 version, Available from Scholar
  56. USEPA, 2005, Technologies and techniques for early warning systems to monitor and evaluate drinking water quality: State-of-the-art review. U.S. EPA Office of Water, pp. 158.Google Scholar
  57. Vajpeyi D, 2001, Deforestation, Environment and Sustainable Development: A Comparative Analysis. Praeger Press, Westport, Connecticut.Google Scholar
  58. Vinodkumar, Chandrasekar A., Alapaty K., Niyogi D. 2008, The impact of assimilating soil moisture, surface temperature, and humidity and the traditional four dimensional data assimilation on the simulation of a monsoon depression over India using a mesoscale model, Journal of Applied Meteorology and Climatology, in press.Google Scholar
  59. WCDR, 2005, World Conference on Disaster Reduction (WCDR), Kobe, Hyogo, Japan. January 18-22.Google Scholar
  60. Wulder MA., Dymond CC., White JC, Leckie DG, Carroll AL, 2006: Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities, Forest Ecology and Management, 221, 27-41.Google Scholar
  61. Xavier VF., Chandrasekar A., Rahman H., Niyogi D., Alapaty K., 2008, The Effect of Assimilation of Satellite and Conventional Meteorological Data for the Prediction of a Monsoon Depression over India using a Mesoscale Model, Meteorology and Atmospheric Physics, in press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gilbert L. Rochon
  • Dev Niyogi
  • Alok Chaturvedi
  • Rajarathinam Arangarasan
  • Krishna Madhavan
  • Larry Biehl
  • Joseph Quansah
  • Souleymane Fall

There are no affiliations available

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