Various disaster management using Remote sensing with case studies.

1. What is disaster?
A Disaster is a situation in which the community is incapable of
coping. It is a natural or human-caused event which causes intense
negative impacts on people, goods, services and/or the
environment, exceeding the affected community’s capability to
respond

2. What is disaster management?
 Disaster management can be defined as the discipline and
profession of applying science, technology, planning and
management to deal with extreme events.
 The emphasis of disaster management is prevention and loss
reduction
Disaster management activity is divided into the following phases
as
 Planning
 Mitigation
 Preparedness
 Response
 Recovery

3. ROLES THAT REMOTE SENSING AND GIS PLAY IN
DISASTER MANAGEMENT PHRRASES
 Planning
• GIS is useful in helping with forward planning.
• It provides the framework for planners and disaster managers to view spatial data by
way of computer based maps.
Mitigation
•Representation of High risk areas
•Facilitates the implementation of necessary mechanism to lessen the impact.
Preparedness
•Identification of emergency areas
•Positions of related departments, Agencies, and Human Resources
•Make it easier for security and shelters provides to plan the strategies
Response
•Provide accurate information on exact location of an emergency situation
•Time saving during the determination of trouble areas (Quick Response)
•Used as floor guide for evacuation routes

4. Recovery
Mapping level of damage
Information related to disrupted infrastructure, number of
persons died or injured and impact on Environment.
GIS and data gathering-
The data required for disaster management is coming from different
scientific disciplines, and should be integrated
Data integration is one of the strongest points of GIS. In general the
following types of data are required:
• Data on the disastrous phenomena (e.g. landslides, floods,
earthquakes), their location, frequency, magnitude etc.
• Data on the environment in which the disastrous events might take
place: topography, geology, geo-morphology, soils, hydrology, land
use, vegetation etc.
• Data on the elements that might be destroyed if the event takes
place: infrastructure, settlements , population, socio-economic data

5. Role of remote sensing in CYCLONE-MITIGATION
PREPAREDNESS RESCUE RECOVERY SATELLITES USED:
Risk modelling;
vulnerability analysis.
Early warning;
long-range climate
modelling
Identifying escape
routes;
crisis mapping;
impact assessment;
cyclone monitoring;
storm surge
predictions.
Damage assessment;
spatial planning.
KALPANA-1;
INSAT-3A; QuikScat
radar; Meteosat
Fig: Movement of cyclone

6. .
Through these pictures one can estimate the storm's position,
direction and speed, maximum wind speeds, areas likely to be
affected, and likely storm surges. The programme issues these to
government officials, river port authorities, the general public, coast
guard, non-governmental organisations and cyclone preparedness
programmes across the world

7. CASE STUDY ON PHALIN CYCLONE-
7th oct,2013
 8th oct,2013
10th oct,2013
12th oct,2013

8. 7th October, 2013: Indian Meteorological Department received
information from KALPANA I, OCEANSAT and INSAT 3A Doppler radars
deployed at vulnerable places, with over-lap, sensors in the sea and
through the ships, about a cyclone forming in the gulf between
Andaman Nicobar and Thailand named PHAILIN (Thai for “Sapphire”).
8th October, 2013: IMD confirmed cyclone formation and predicted it
as “severe cyclone” and its effects would be felt from Kalingapatnam
in Andhra Pradesh to Paradeep in Odisha, and that it would probably
first strikethe port of Gopalpur in Ganjam district at about 5 pm on 12
October. The wind speed could touch 200(km/h).
10th October, 2013: IMD prediction of a severe cyclone was
converted to a “very severe cyclonic storm” with wind speeds up to
220 kmph. the US Navy’s Joint Typhoon Warning Centre predicted it
would have wind speeds up to 315 km/h.
12th October, 2013: The “very severe” cyclonic storm had its landfall
at Gopalpur port at about 9 pm with a wind speed of 200 km/h.

9. MITIGATION PREPAREDNESS RESPONSE RECOVERY
GIS: Risk modelling;
vulnerability analysis;
Strengthening EWS;
Disaster Response
Infrastructures; Disaster Drills
Early Warning System;
Constant updates from
ISRO, IMD and USNJTWC
etc.;
Distribution of Satellite
Phones , VHF and
HAMRADIO to DMs,
BDO’s, Sarpanch etc.;
Mass Evacuation on the
basis of cyclone’s path
over the state.
Google Crisis Map; Google
People Finder;
ODRAF & NDRF Deployment;
Relief Operations
coordinated by
Navy & Air Force;
Disaster
Assessment;
Logistics
Coordinated by
Centrally Operated
Units;
Spatial planning;

10. MITIGATION PREPAREDNESS RESCUE RECOVERY SATELLITES USED
Mapping flood-prone
areas;
delineating flood-plains;
land-use mapping.
Flood detection;
early warning;
rainfall mapping.
Flood mapping;
evacuation
planning;
damage
assessment.
Damage
assessment;
spatial planning.
Tropical Rainfall
Monitoring
Mission;
AMSR-E; KALPANA
I;
Role of GIS in floods

12. CASE STUDY
TITLE: GIS-based disaster management, A case study for Allahabad
Sadar sub-district(India) by S.H. Abbas, R.K. Srivastava and R.P.
Tiwari ( 2009)
JOURNAL :Management of Environmental Quality: An
International Journal,2009
OBJECTIVE
To demonstrate a Geographic Information System (GIS)-based study
on development of District Disaster Management System for floods
for Allahabad Sadar Sub-District(India)

13. STUDY AREA
The study area is Sadar, sub-district of Allahabad (India) which is
surrounded by river Ganga and Yamuna
located between 81º 45ʹ to 82º latitude and 25º 15ʹ to 25º 30ʹ longitude
METHODOLOGY
•An approach has been designed to explore the scope for the
combination of Disaster Management and GIS.
•The flood-prone areas have been identified and their positions are
marked using Arc View.
• GIS has been exploited to obtain the spatial information for the
effective Disaster Management for flood-affected areas

14. Fig: Map showing Ganga and Yamuna river around the study area

15. GIS-based maps for Disaster Management
Various maps were generated for the analysis in the GIS platform
like-
• Flood-affected areas of Sadar sub-district
• Population density distribution in flood prone areas
• Villages having road connectivity ,hospital facility in flood
affected areas
• Route map for the disaster prone area

16. and Yamuna river both
Fig: Map showing areas affected by flood by Ganga and Yamuna river

17. • If any government agency or any non-governmental organization
wants to provide any type of help to the affected people, they can
follow above generated map for having idea about the requirement.
•Village administrator can monitor all flood management operations
using GIS data base
Fig: Map showing road connectivity

18. • Previous shows the road network of villages that are more
vulnerable and are not been connected by main road as well as
metal road.
•The villages that are not having transport connectivity can be
identified.
•With the help of above information, one can
provide rescue first to those villages not connected through metal
road and after that provide transportation to metal road connected
villages.

19. SUMMARY-
• It shows that in that sub-district Sadar of Allahabad 54 villages are
affected by flood when high flood level reaches up to 84.50 meters.
• The GIS generated map shows that out of 54 villages only seven
villages have mud road and 47 villages have paved road.
•Thus, GIS tool can be beneficial for getting all the relevant
information at the time of occurrence of the disaster, and can help in
planning and management.

20. Role of GIS in Drought
DISASTER MITIGATION PREPAREDNESS RECOVERY RESCUE SATELLITES USED
DROUGHT Risk modelling;
vulnerability
analysis;
land and water
management
planning.
Weather
forecasting;
vegetation
monitoring;
crop water
requirement
mapping;
early warning.
Monitoring
vegetation;
damage
assessment.
Informing
drought
mitigation.
FEWS NET;
AVHRR;
MODIS; SPOT
NDVI (is calculated from the visible and near-infrared light reflected
by vegetation . Healthy vegetation absorbs most of the visible light
that hits it, and reflects a large portion of the near-infrared light.
Unhealthy or sparse vegetation reflects more visible light and less
near-infrared light

21. Calculations of NDVI for a given pixel always result in a number that
ranges from minus one (-1) to plus one (+1); however, no green leaves
gives a value close to zero. A zero means no vegetation and close to +1
(0.8 - 0.9) indicates the highest possible density of green leaves.
NDVI= (NIR+RED)/(NIR-RED)
where:
NIR= reflectance in near
infrared band
RED= reflectance in red band

22. Fig: Pictures showing difference between densely vegetation
area and drought areas

23. ROLE OF Remote sensing in earthquake-
MITIGATION PREPAREDNESS RESCUE RECOVERY SATELLITES USED
Building stock
assessment;
hazard mapping.
Measuring strain
accumulation.
Planning routes for
search and rescue;
damage assessment;
evacuation planning;
deformation
mapping.
Damage assessment;
identifying sites for
rehabilitation.
PALSAR;
IKONOS 2;
InSAR; SPOT; IRS

25. Picture showing predicted Tsunami wave amplitude
Picture showing collapsed building