Mei-Po Kwan and Daniel M. Ransberger

1. Lidar assisted emergency response: Detection of transport network obstructions caused by major disasters Mei-Po Kwan and Daniel M. Ransberger Computers, Environment and Urban Systems 34 (2010) 179-188.

2. Background Information • According to 2006 World Disasters Report: • 427 Natural Disasters • Floods, droughts, tornadoes, tsunamis, hurricanes, landslides, and earthquakes • 297 Technical Disasters • Nuclear power failures, collapse bridges • As a result: • 33,733 People Killed • 1.43 Million People Injured or Suffered Economic Loss • Financial Loss of Hurricane Katrina Alone ~ $129 Billion • Emergency Management Plays a Crucial Role • This Includes Planning for, Responding to, and Mitigating against • In the U.S., the Federal Emergency Management Agency (FEMA) is Tasked with this Effort

3. Why GIS Can Assist • “In heavily damaged areas, such as hurricane-affected coasts or cities damaged by earthquakes, it is often difficult to assess precise locations as most buildings and landmarks have been destroyed. GPS, coupled with GIS and remote sensing data have been employed to assist in compiling quick damage estimates.” • Cutter, S.L. (2003). GI science, disasters, and Emergency Management Transactions in GIS, 7(4), 439-446. • Evidence from Hurricane Katrina • Common Requests: • Imagery of New Orleans’ Central Business District • Coordinates of Buildings for Stranded People • Obstacles to Flight Paths • Ex] Tower Locations • Flooded and Passable Roads

4. LiDAR • What is It? • Similar to Radar • Airborne Systems • Lasers and Sensors • “Opto-Mechanical Scanning Assembly” • Pulses Sent. Pulses Reflect. Pulses Received. • Accompanied With? • GPS • Determines X, Y, and Z • Inertial Measurement Unit (IMU) • Determines yaw, pitch, and roll (like a boat in water)

5. Historical LiDAR Uses • Past Studies used LiDAR to assist with emergency Preparedness, Mitigation, and Response • Identifying Least Hazardous Routes • Creation of Elevation Imagery in environments where other photogrammatic technology fails

6. LiDAR’s Case • Very High Resolution: 15 cm • Highly Automated Process • Acquires Data at High Elevations while maintaining Accuracy • LessDependent upon Weather • Data can be acquired Day or Night • Can Penetrate Smoke • Data is Geographically Referenced • No need to orthorectify imagery

7. Study Extent • “This study explores whether the use of LiDAR data in detecting transport network obstructions during emergency response shortens the time first responders reach disaster sites. We present a method for doing this using LiDAR data collected in New Orleans, Louisiana (USA) before and after Hurricane Katrina.”

8. Study Area • Post Hurricane Katrina New Orleans • Considerations: • Scale of Event • Number of People Affected • Considerable Existing Data • Vector (street centerlines, administrative boundaries, and parcels) • Remotely Sensed (LiDAR, aerial imagery, and satellite imagery)

9. New Orleans: 3D LiDAR Image

10. Data • Pre-Storm Data • Collected March 2003 • Resolution 0.14m • Basic LiDAR Attributes: X, Y, Z • No intensity data or other attributes • Coordinate System: UTM Zone 15 North • Datum: NAD83 • Vertical Datum: NAVD88 • Survey Units: Feet • Covers entire New Orleans area

11. Data Continued • Post-Storm Data • Collected September 2005 • Resolution 0.14m • Basic LiDAR Attributes: X, Y, Z • Intensity return data and other attributes (date and time of acquisition of each point) • Coordinate System: Latitude and Longitude • Datum: NAD83 • Vertical Datum: NAVD88 • Survey Units: meters • Limited Coverage • Southern shore of Lake Pontchartrain • Areas with Levees • Approximately one mile inland from the southern shore that runs the entire coastal area within the New Orleans area

12. Coverage of Post Storm LiDAR Data

13. Pre-Analysis Data Preparation • Different Geographic Coordinate Systems • Post Data transformed to UTM • Quick Terrain Modeler software optimized for UTM • Pre Data survey units transformed to meters • Extracted Points within Transport Links • Both pre and post data • 30 foot buffer for highways • 20 foot butter for residential and other streets

14. Transport Network Obstruction Detection • Digital Elevation Model creation • Both pre and post • Data imported into QT Modeler • Change Detection between pre and post elevation data • Range: -5m to 5m • Markers Placed at locations with a 2.5-5m increase spanning the width of road

15. Routing Analysis • Street Network from City of New Orleans GIS • Network Analyst: ArcGIS • Street files contain all attributes • Shortest Path Calculation • City of New Orleans Fire Department • 30 Randomly Selected Locations within study area • 3 Routes Considered • Pre-Storm with no blockages: Normal • Post-Storm: Naïve Blockage Re-routing • Post-Storm: LiDAR Assisted Blockage Re-routing • Assumption • “Free-flow Traffic”

16. Study Area Location of the City of New Orleans Fire Station and 30 randomly selected locations.

17. Road Network Blockages The 86 identified post-storm network obstructions within the study area.

18. Results • Normal vs. Naïve • Increase in Average Route Time- 1:58 to 3:52. • 96% increase in response time • Increase in Average Route Distance: 1.1 mile to 2.5 • 127.3% increase in average distance • Normal vs. LiDAR Assisted • Increase in Average Route Time- 1:58 to 2:31 • 45.5% increase in response time • Increase in Average Route Distance: 1.1 mile to 1.6 mile • 28.0% increase in response distance

19. Results Cont. • Naïve vs. LiDAR Assisted • 30.2% Reduction in Average Route Time • 31.5% Reduction in Average Route Distance • LiDARAssisted outperformed Naïve for all but 4 Locations • Maximum Reduction: • 3 minutes and 16 seconds • 2.7 miles • Minimum Reduction: • The 4 Locations with No Route Blockages

20. Result Analysis • Paired t-tests • Significant between Pre and Post Storm Response Times • At the p < 0.0001 level • Significant between Pre and Post Storm Response Distances • At the p < 0.0001 level • Significant between Naïve and LiDAR Assisted Response Times • At the p < 0.0001 level • Significant between Naïve and LiDAR Assisted Response Distances • At the p < 0.0001 level

21. Author Recommendations • Further Method to Improve Identification of Blockages • Include decreases in elevation • Include volumetric changes • Complement LiDAR Data with other Remotely Sensed Data • High Resolution Satellite Imagery • Aerial Photography

22. Author Conclusions • LiDAR Data Improves the Speed of Emergency Response • LiDAR’s Usefulness is Positively Correlated to Number of Blocked Roads • Institutional Obstacles • Who will capture, process, analyze, and disseminate data? • Should be organized by users, FEMA and local agencies, ahead of time • Hazardous Flying Conditions • Availability of Data Collectors • Integration of LiDAR Data into Comprehensive GIS Database • With other data such as flood data • Different coordinate systems, data models, space-time resolution, data formats, scales, and inaccuracies etc… • Spatial Data Infrastructure • Assists in the dissemination of data to multiple users • Emergency managers, Red Cross, and damage assessment teams etc..

23. My Thoughts • Limited Study • Any Real Life Applications since? • If so, what were results? • Maps of Study Area • Would have liked fire stations overlayedwith road network blockages to conceptualize the relationship between the two. • Authors’ “Free-flow” Traffic Assumption • Fair Assumption (measuring differences) • Authors’ Conclusions: a Key Take-Away • Institutional Obstacles- much work to be done • Important for Integration of Multiple Technologies • Advantages Diminished without Pre-planning • Lacking Definite Collection Requirements • Ex] Average: 1 square mile per hour • Use in Response to Other Disasters • Earthquakes, sandstorms • LiDAR= Exciting Technology & Many Possibilities

24. Questions and Comments…