Disaster Management using LiDAR

Fig. 1

Emergency responders can analyze LiDAR data in E3De software to extract 3D features and locate damaged structures or plan response efforts. Imagery and LiDAR data courtesy of the RIT Haiti Mission for the World Bank.

Fig. 2

Emergency responders take results from E3De and export them to ENVI image analysis software, where they are fused with other image data to enhance road extraction efforts. Imagery and LiDAR data courtesy of the RIT Haiti Mission for the World Bank.

Fig. 3

Government officials exploit LiDAR data for disaster response planning and mitigation. The 3D viewer in E3De can be used to inventory assets such as buildings, roads and power lines. Imagery and LiDAR data courtesy of the RIT Haiti Mission for the World Bank.

Senior Technical Solutions Engineer
Exelis Visual Information Solutions
Boulder, Colo.

Today, professionals across industries use geospatial data for up-to-date, accurate information about geographic areas of interest. Crucial information can be extracted from geospatial data using advanced analysis software to address challenges ranging from monitoring the effects of development to selecting optimal mining locations. Traditionally, most geospatial information has come from two-dimensional imagery and data. However, professionals involved in a variety of industries and applications (like disaster management) are increasingly utilizing three-dimensional sources of information (like LiDAR) to create photorealistic 3D visualizations, extract 3D features and export products to geospatial tools to help them understand the world around them.

Collecting LiDAR Data

LiDAR (Light Detection and Ranging) is a remote sensing technology that uses light pulses to measure the distance between a sensor and reflecting objects such as the earth’s surface, buildings and trees. The result is a collection of data points called a ‘point cloud’ that is used to precisely render 3D shapes and accurately locate features in a scene. The accuracy of LiDAR technology has made it possible to map large areas with levels of detail that were previously possible only with time-consuming and expensive ground surveys. These benefits and others have led disaster management organizations to use LiDAR data as a source of information when mapping and making critical decisions.

Related stories on LiDAR in this issue include QA/QC Challenges and LiDAR Software Updates.

The wide variety of applications for LiDAR data has resulted in the development of more advanced LiDAR sensors. These sensors have increased pulse rates that improve the quality and accuracy of their data. However, LiDAR datasets generated by these sensors are often so large that they can’t be processed using traditional software methods. In order to effectively analyze large LiDAR datasets, users need a solution specifically designed to address this data format.

Advances in LiDAR Processing and Analysis

Because organizations involved in disaster management and other applications are now using LiDAR data more frequently to solve problems, there has been an increased demand for LiDAR processing and analysis software that can perform complex tasks like 3D feature extraction. This demand has led to powerful new LiDAR solutions, like E3De (from Exelis Visual Information Solutions, formerly ITT Visual Information Solutions), which can utilize large datasets to create realistic 3D visualizations, extract 3D features from a scene and produce 3D products and layers. Information extracted from LiDAR data using advanced technology can be included in geospatial analysis products for mapping fire fuels, creating forest inventories, monitoring vegetation encroachment and completing right-of-way analyses.

New LiDAR processing software streamlines the overall geospatial workflow by enabling users to export results to a variety of geospatial tools, such as ENVI image analysis software or the ArcGIS platform. In ENVI, users can fuse results with 2D data, such as multispectral and hyperspectral imagery, for further analyses or to create a variety of geospatial products. For example, a disaster management professional may want to fuse timely elevation information from LiDAR data with other geospatial imagery in ENVI to monitor changes in terrain related to volcanic activity. Similarly, advanced LiDAR processing software makes it easy to export results directly to the ArcGIS platform for mapping applications and additional decision making.

Advances in LiDAR processing and analysis software and sensor technology have led disaster management professionals to rely on LiDAR data for situational awareness and for critical decisions. Disaster response planning and management is one of the most significant applications of LiDAR data, when situational awareness and accurate decisions are vitally important. LiDAR use in this realm encompasses everything from mapping vegetation that is prone to wildfires, to creating an inventory of assets, to monitoring structural damage after earthquakes.

LiDAR for Disaster Management

Disaster management professionals look to LiDAR data and processing and analysis software to enhance their understanding of disaster scenes and to make critical decisions. Knowledge extracted from LiDAR data helps emergency responders efficiently plan disaster relief efforts to prevent property loss, reduce injuries, save lives and restore crucial utilities like water and electricity to affected communities.

Ideally, soon after a disaster, aircraft equipped with LiDAR sensors fly over affected areas to collect current data that is invaluable when planning an appropriate response. Once these data are processed, emergency responders use them for two main purposes: creating a visual map of the disaster area, and extracting important information about the area, such as determining safe locations for shelters, identifying downed power lines and locating areas that have a high risk of mudslides or flooding.

LiDAR data and geospatial imagery can be fused through a variety of software solutions that support both data types or, more commonly, that support the exported vector or raster results of LiDAR analysis. The benefit of fusing LiDAR data with geospatial imagery is the inclusion of accurate elevation and three-dimensional features in geospatial analyses or a GIS.

Visualizing Earthquake Damage

Emergency responders use advanced LiDAR processing software like E3De to visualize areas damaged by earthquakes. Gaining an accurate understanding of damage on the ground is paramount to an effective response. Creating 3D visualizations from LiDAR data helps emergency management teams detect and measure objects like collapsed buildings and standing structures. Because LiDAR data provides three-dimensional representations of manmade and natural objects, it is better suited than two-dimensional imagery for assessing damage to buildings and locating downed trees. The flatness of two-dimensional images makes it hard for viewers to identify areas of minor damage, whereas three-dimensional visualizations allow responders to get a more accurate, dynamic understanding of conditions on the ground. See Figure 1.

The powerful, photorealistic 3D vis-ualizations produced by advanced software like E3De make it easy to perform a quick visual analysis of an area to get an understanding of damage. E3De enables users to manipulate visualizations so that they can analyze an area from different perspectives. By being able to view an area from every angle, responders have a much better understanding of the scope of the disaster. Once they visualize buildings, debris, terrain and other features, they can determine where to focus their efforts and identify areas of interest that require further data analysis.

Analyzing Earthquake Damage

Once disaster responders have visualized LiDAR data, they hone in on areas that require further analysis in order to increase their understanding of the damage and plan response efforts according to the most urgent needs. One of the main ways emergency responders analyze LiDAR data is by extracting features.

After an earthquake, emergency responders might need to extract buildings, power lines, debris, roads and topography. This vital information helps them to target and ensure the efficiency of rescue efforts, such as identifying collapsed structures that have the potential for trapped citizens. To make this process as easy as possible, technology like E3De allows users to find and extract features of interest through an automated feature identification tool and to use quality assurance tools to manually edit and identify features as they explore the data in a 3D visualization. Users can perform feature identification on an entire point cloud scene, a defined subset of a scene or multiple files simultaneously.

Another way emergency responders identify features using LiDAR data is with their height. LiDAR data provides extremely accurate elevation information that enables users of image analysis software, like ENVI, to classify features by height. When setting up a classification, ENVI users could set road extraction at heights below 1 meter, debris extraction at a height of 1-3 meters, and structures still standing at 3 meters and above.

Because 3D feature extraction results often need to be refined to ensure that individual features are accurately identified, it is important to have a software solution that allows for easy editing of results. E3De allows users to fly through a photorealistic visualization and look for specific data points or individual features that need editing. They can also refine results to include individual features such as the shape of structures, the number of trees and the location of power poles. The ability to fix inaccuracies quickly during analysis makes it easier to make timely decisions.

Once emergency responders have extracted features from LiDAR data such as roads and debris, they can map an intact road network to support the routing of ground teams and supplies. The identification of these routes saves critical time and manpower during a disaster response. In addition to conducting a network analysis, emergency responders may also need to identify the best helicopter landing zones to drop off and collect people and supplies. Ideal helicopter landing zones are relatively free of debris and other vertical obstructions, and take into consideration an area’s size, slope and proximity to targets; all of this can be done using LiDAR during a geospatial analysis.

Another way officials analyze LiDAR data for disaster response efforts is by comparing data collected over an area prior to a disaster with that collected afterwards. For example, emergency responders can compare before and after conditions of roof and structural components of buildings to assess damage. This work can be done visually in E3De or by exporting results to image analysis tools. Additional analyses made by emergency responders after a disaster utilizing LiDAR data include assessing areas that are flooded or could potentially flood, and looking for downed power lines or lines with potential hazards like encroaching trees.

Fusing LiDAR Data with Other Geospatial Data

Fusing LiDAR data with other geospatial data provides a more complete understanding of an area of interest. Data fusion combines two or more data modalities so that users can take advantage of the strengths of the different data types and have more information to make decisions. LiDAR data and geospatial imagery can be fused through a variety of software solutions that support both data types or, more commonly, that support the exported vector or raster results of LiDAR analysis. The benefit of fusing LiDAR data with geospatial imagery is the inclusion of accurate elevation and three-dimensional features in geospatial analyses or a GIS.

When professionals bring LiDAR products into advanced image analysis software like ENVI, they add more geospatial information to their analysis, which enhances their understanding of areas of interest. ENVI users can combine LiDAR data with other geospatial data and imagery to more accurately perform tasks like image classification, feature extraction, change detection and a variety of other image analysis techniques. For example, emergency responders take results from E3De and fuse them with other image data in ENVI to enhance road extraction efforts. They also take buildings’ vector layers from E3De and overlay them on imagery to produce an inventory of buildings. This can be done with data gathered before and after a disaster to show which buildings have been damaged or destroyed due to the disaster. See Figure 2.

Similarly, disaster responders incorp-orate data extracted from LiDAR with other data in a GIS to inform important tasks. For example, responders export extracted debris and roads into ArcGIS Network Analyst to support ground team routing. Additionally, they can take topographic information extracted from LiDAR data and incorporate it in a GIS to determine flooding risks to disaster affected areas. Since responders use a GIS to map and coordinate plans of attack, it is important that all information, such as valuable LiDAR data, is included.

LiDAR for Disaster Planning and Mitigation

In addition to using LiDAR for disaster management, government officials also use LiDAR data for disaster response planning and mitigation. In this way, they use LiDAR to assess risks, reduce vulnerabilities and create appropriate action plans before disasters strike. For example, emergency management professionals use detailed elevation data from LiDAR to understand low-lying areas that are prone to flooding and to identify areas at risk for landslides and mudflows. Officials also create inventor-ies of assets such as buildings, roads and power lines so that, if and when a disaster occurs, they are prepared. See Figure 3.

LiDAR data provides critical geospatial information about areas of interest and is a valued complement to two-dimensional imagery and data. The accuracy of LiDAR data and the ability of advanced processing and analysis tools to visualize and extract important pieces of information have led to the adoption of LiDAR by professionals across industries. LiDAR technology is being used for more and more applications, but perhaps no application is more important than planning for disaster response or reacting after a disaster has struck.

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