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GIS Emergency Management

Emergency Management & GIS

In addition to having a presence in three-dimensional geographic space, natural disasters like floods and fires are time series events. They often take place rapidly, and it is crucial to monitor their continuously evolving conditions; remote sensing and geospatial information systems equip users with the tools to locate and categorize these events in both space and time. 

Why Do We Need GIS for EMS?

National and local first responders require information about building footprints and layouts, evacuation routes, and digital terrain models to help plan their responses. Stormwater and electrical systems need to be accurately mapped to monitor for burst pipes, leaks, and outages. The more equipped these essential personnel can be with information about the community, the environment, and the severity of the hazards, the quicker they can make decisions, mitigate damage, and save lives. 

The Federal Emergency Management Agency (FEMA) cites GIS as being a critical tool for identifying hazards and mitigating the risks presented to infrastructure and residents. Their Federal Disaster Mitigation Act of 2000 (DMA2K) outlines requirements for data collection and the development of muti-hazard mitigation plans necessary to maintain eligibility for federal funding in a disaster. These plans help localites prepare and organize for potential disasters, and ultimately help make FEMA’s assistance more effective and seamless.

According to the FEMA course materials for Applications of GIS for Emergency Management, first responders rely on city and county GIS data, including administrative boundaries, addresses, roads, and other infrastructure. “This information can be linked to assessors’ data to provide building value, square footage, and building use (residential, commercial, etc.) to calculate which assets are at greatest risk.

GIS can also be used to locate and analyze the community’s essential facilities including Community Lifelines, schools, police, fire departments, medical care facilities, and emergency operations centers.

Specifically, GIS software enables users to conduct queries for specific types of at-risk buildings or neighborhoods, identify the land owners or administrative parties responsible for certain jurisdictions on the fly, and observe dynamic conditions such as traffic patterns. Of course, GIS is also a cartographic and statistical tool, which means visualizations of all kinds can be generated and distributed to wide audiences; maps can depict proximity to utilities, number of residences in a given boundary, or evacuation routes. 

After several devastating hurricanes in Alabama in 2019, the Alabama Emergency Management Agency equipped their first responders with maps that helped them prioritize the search and rescue effort. They were able to create an impact summary, calculate the affected population, and identify how many residences were located within the scope of the storm. To prepare for damage control and repair, FEMA generated an automated damage assessment model using aerial imagery and wind speed data.

GIS Data and Emergency Response

In addition to point, line, and polygon vectors, along with the associated attribution necessary for understanding their significance to the community, the emergency response analysis package would not be complete without imagery and multispectral data.  In fact, the process of identifying, quantifying, and monitoring ground conditions often begins with satellite imagery. Landsat, MODIS, and Sentinel-2 imagery is all vital for first responders and geospatial experts to visualize the circumstances from above, but when seeing with the naked eye is inadequate (i.e. smoke, haze, cloud cover), SAR brings additional information into the picture. 

According to NASA, synthetic aperture radar, or SAR, is a type of “active data collection” where a sensor records how much radiofrequency (RF) energy is reflected back up from the Earth’s surface. Unlike regular optical imagery, the SAR signal “is instead responsive to surface characteristics like structure and moisture.”

So what’s key about using SAR for emergency management is that these satellites can pick up on changes that can’t be seen (like soil saturation) and penetrate atmospheric contamination like clouds, haze, smoke, and fog. These conditions make traditional observation much more challenging, but during a natural disaster, they are all the more likely to be present. 

Identifying an event such as a flood in the moment is not sufficient. As necessary as it is to locate a natural disaster once it has taken place, and provide detailed information about the intensity of the resulting damage, it is even more powerful and useful to be able to predict future events. This is true for emergency responders, stakeholders on the ground, governments, and even insurance companies. 

Who in Geospatial Responds to Disasters? 

ICEYE is a SAR satellite company, equipped with a constellation of 14 small cubesat satellites (less than 100kg in mass each), which are utilized to respond to and monitor flooding events. Shay Strong is their Vice President.

Strong explains that with cross-domain collaboration (i.e., a team of meteorologists, geospatial/imagery analysts, and machine learning experts), ICEYE aims to get ahead of the storm. Their crew develops situational awareness about the likelihood of a flood event, tasks their satellites to capture SAR data during the predicted event, and then produces metrics within the first 24 hours – which is the most crucial time frame. 

Want to hear more about the work ICEYE does, and how SAR works? Listen to Flood Monitoring From Space with Dr. Shay Strong on the Mapscaping Podcast

ESRI showcases a number of technical solutions to emergency response situations on their website, ranging from flood prediction and prevention to protection against extreme heat. ESRI also offers free support to organizations actively responding to disasters. Geospatial machine learning has even been used to reduce the risk of avalanches, which are the deadliest natural hazard in the state of Colorado. Although the Colorado Avalanche Information Center (CAIC) is accustomed to mapping avalanche zones using ArcGIS, they also found that simple location markers were no longer sufficient; like floods, avalanches are a time series event, and it is much more powerful to be able to predict future disasters than to simply locate current ones

Using historic avalanche data, elevation, degree of slopes, and dynamic weather and climate conditions, CAIC has built geospatial models to predict where and when avalanches were most likely to occur. 

“Our Python machine learning algorithms dig into existing and newly collected field data,” said Mike Cooperstein, a CAIC avalanche forecaster. “We can add much more data about the topography of the path and weather conditions that affect each path’s own avalanche behaviors. Machine learning algorithms explore individual path data and calculate predictions.” 

The team also uses mobile applications to map and quantify the results of avalanches, which helps get the data out to road crews, who can then map out where and when they need to clear snow. 

Geospatial information systems aren’t just for behind the scenes. Maps are some of our most important tools in a crisis; when they’re well maintained, they can help locate life-saving resources and at-risk infrastructure. They can characterize ever-changing conditions and warn the public about incoming dangers. Most importantly, using a combination of dynamic raster layers, satellite imagery, and government-maintained vectors, maps can help us come together and find, and plan for, safety during emergencies. 

About the Author
I'm Daniel O'Donohue, the voice and creator behind The MapScaping Podcast ( A podcast for the geospatial community ). With a professional background as a geospatial specialist, I've spent years harnessing the power of spatial to unravel the complexities of our world, one layer at a time.