Geo-spatial technologies in disaster management

The use of geospatial technology in disaster management is a natural fit because almost every aspect of a disaster is referenced by location.

Geospatial Technology is defined as "a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information." That is, data identified according to location. It also includes the procedures, operating personnel, and spatial data that go into the system. The power of a geospatial technology comes from the ability to relate different information in a spatial context and to reach a conclusion about this relationship.

The following technologies fall in the ambit of geo-spatial technology

1. GIS

2. GPS

3. Remote sensing

Geo-spatial technologies help in disaster mitigation and management in the following ways:

The use of geospatial information for managing disasters is a key area in which earth observation already plays an important role, and in which we can also see the use of many different types of geospatial data.

Earth observation satellites have demonstrated their utility in providing data for a wide range of applications in disaster risk management. Pre-disaster uses include risk analysis and mapping; disaster warning, such as cyclone tracking, drought monitoring, the extent of damage due to volcanic eruptions, oil spills, forest fires and the spread of desertification; and disaster assessment, including flood monitoring and assessment, estimation of crop and forestry damages, and monitoring of land use/change in the aftermath of disasters.

Remotely sensed data also provide a historical database from which hazard maps can be compiled, indicating which areas are potentially vulnerable. Information from satellites is often combined with other relevant data in geographic information systems (GIS) in order to carry out risk analysis and assessment. GIS can be used to model various hazard and risk scenarios for the future planning and the development of an area.

A proposed concept of a geo-space system for prediction and monitoring earthquakes and other natural and man-made catastrophes, which is based on a system capable of monitoring precursors of earthquakes in the ionosphere and magnetosphere of the Earth and using these precursors to make short-term forecast of earthquakes. Investigations on the interaction between ionosphere’s F layer variations and different variations occurring in circumterrestrial environment (atmosphere, ionosphere and magnetosphere) associated with seismic activity, and detected by means of ground base and satellite monitoring. This method and others like GPS measurements for long distances are providing useful parameters for earthquake forecasting. Remotely sensed data can help greatly in disaster risk management by studying and recommending the implementation of an integrated operational global system, especially through international cooperation, to manage natural disaster mitigation, relief and prevention efforts through Earth observation, communications and other space related services, making maximum use of existing capabilities and filling gaps in worldwide coverage.

United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN SPIDER) by promoting effective application of space technology in disaster reduction and management at the global level, and in developing countries in particular.

Another area of disaster management is flooding. Airborne interferometric SAR (IfSAR) is particularly suitable for this purpose, giving an economic data source to cover large areas, and is often complemented by airborne LiDAR data to give more detail in critical areas. Satellite data is widely used for monitoring flooding after it has taken place and can also be used to predict flooding by providing data to input to hydrological models. 

Determination of the potential earthquake centers, the path of the wave-energy must can be modeled. In case of an earthquake, the geological structure transports the various waves. In the event of a tsunami, the bathymetric conditions, the vertical water column and the run-up-path are of interest. Geological and hydrological data build the basic layers in a geodatabase.

Remotely sensed data can assist to detect significant changes from the air or the orbit. Radar data can monitor even very small changes in the terrain that indicate stress in the geological structures. Hyper-spectral sensors can assist to detect anomalies in the environment.

For modeling tsunamis, terrain models of the seafloor, the shore and the coastline must be established. Besides classical hydrological methods e.g. via echo sounder, LIDAR technologies, using water penetrating laser, assist in the off-shore areas for bathymetric measurements. DTM (digital terrain models) and DSM (Digital Surface Models) which include artificial structures are important to compute reliable hydrodynamic runup Simulations. This is very important for tsunami Modeling.

Aerial surveys use airborne cameras and/or airborne LIDAR sensors and are able to deliver a high dense DTM and DSM. In combination with land use data, risk estimations and generalization of the city into certain risk-levels can be done.