Evacuation Route Selection Based on Tree-Based Hazards Using Light Detection and Ranging and GIS
Summary (4 min read)
INTRODUCTION
- Wooded areas flank the primary and secondary road networks in many states.
- Besides disrupting traffic flow, such incidents endanger the lives of drivers and emergency crews, hinder service restoration, and impede effect ive emergency response.
- To avoid this, trees that may be a threat should be identified, and measures should be considered to prevent them from falling on overhead lines and thoroughfares.
- A manual approach to such a problem is not viable, because of the time and resources necessary for the init ial assessment and the need for periodic updates.
- In formation System (GIS) and Remote Sensing (RS), an automated hazard identification tool can be developed for the aforeme ntioned problem that is more economical, efficient, reliable, and safer than a manual approach.
BACKGROUND
- Hurricanes, other strong winds, and ice storms cause tremendous damage across the United States (US) including flooding, wind-based destruction of property, and blockage of major road networks from fallen objects.
- The blockage of roads due to fallen trees and utility lines during severe weather is a significant problem (Draper 2003).
- The problem of tree-based hazards is well recognized, and each year millions of dollars are spent annually to identify and cut or trim such trees (Act 2003).
- In most states, identificat ion of potentially hazardous tall objects has, to date, been done manually by road crews on an ad hoc basis.
- Light Detection And Ranging , a recent advancement in RS technologies, offers the potential of automated and expeditious data collection , and GIS provides a means to analyze and process such data.
LiDAR and Its Characteristics
- LiDAR is an active remote sensing technology that is used to collect topographic data (NOAA 2003).
- The difference in time is measured from when a laser pulse is emitted from a sensor to when the target objects in the path of the laser reflect back the pulse.
- The end product is accurate, geographically reg istered longitude, latitude, and elevation from the mean sea level (x, y, z) positions for every data point (Baltsavias 1999).
- As it does not provide feature informat ion, clever and efficient algorithms are required for feature identification depending upon the desired application.
GIS and Its Characteristics
- GIS can be defined as a computer-based tool set for collecting, storing, retriev ing, transforming, and displaying spatial data from a discipline-specific domain for a particular set of purposes (Burrough and McDonnell 1998).
- The true strength of GIS lies not its ability to store and process data but in its spatial representation and spatial analysis (Rasdorf et al. 2000).
- Spatial analysis involves examin ing the geographic patterns in data and observing relationships between geographical features.
- GIS-T refers to the principles and applications of applying geographic information technologies to transportation problems (Miller and Shaw 2001).
- For road safety hazard management, GIS can he lp to identify hazards posed by specific man-made or natural objects.
EXISTING HAZARDOUS TREE IDENTIFICATION METHODS
- Because of budget restrictions, across the US there is no generalized, method to identify hazardous trees, despite the acknowledged expenses that they generate.
- These operators are also asked to look for roadway hazards and to report them to the Highway Maintenance Supervisor.
- A survey of 20% of the DOTs, focusing mainly on states with large tree growth and severe weather problems, established that most states were highly reactive in their approach and did not have the resources to do more than respond to calls from the public o r from maintenance workers, who happened to notice a potential hazard (Table 1).
- Every 3 years, a computer program is used to randomly generate 3 segments (each 1/16 km in length) for a given section of highway, if it is less than 16.1 km long, in terms of its paving type and general characteristics.
- An alternate approach to casual visual observation to manually identify the heights of the tree is to use a hypsometer.
PROPOSED METHODOLOGY
- The research described herein adopts a unique technique to identify and assess potentially hazardous tall objects: (a) buffer calculation, (b) elevation characterization, (c) shortest distance determination, and (d) feature identificat ion.
- To achieve these four tasks, the proposed method employs a LiDAR dataset, a centerline GIS data set of the road network, and multi-spectral imagery.
Study Area, Data Characteristics and Software used
- The area was selected primarily due to the availability of all the required data for the project: LiDAR data, centerline road data, and mult ispectral aerial imagery.
- The bare -earth data are required to have a vertical RMSE of 20-cm for coastal counties and 25- cm for inland counties, computed after discarding the worst 5% of the checkpoints to account for un -cleaned artifacts (NCFPM 2003).
- The centerline road data were collected and produced by NCDOT.
- The projection is NAD83, North Caro lina State Plane, and the original data units are in feet.
- The fo llowing provided functions were employed: Buffer Pointdistance Table Query ing and Manipulation Identify.
Buffer Calculation
- Airborne LiDAR systems are capable of producing extremely detailed informat ion of even small scanned areas (Baltsavias 1999), but to achieve a high level of detail requires substantial processing time.
- An effective measure to counter such resource intensiveness, while not losing the desired level of detail, is to reduce the dataset size.
- In order to select the relevant data points, a buffer zone can be created around the road network; should fringe lane data be available, instead of centerline data, an even more accurate evaluation is possible.
- Only points that lie within the defined buffer zone are selected.
- The criterion for a 120m buffer zone was based on the assumption that the maximum height of any tree is 60m; this parameter is easily changed to reflect local vegetative growth patterns.
Comparative Elevation Calculation
- Once a buffer zone is established, the LiDAR points within the buffer zone need to be analyzed.
- One dataset represents a reflective surface elevation model [also known as Digital Surface Model (DSM)], which contains elevation data from the ground, as well as from vegetation, tree canopy, and other features such as buildings.
- The other dataset represents “bare earth” ground points, which excludes additions to the terrain.
- Height informat ion above bare ground surface is important to determine the potential hazard posed by any tall object within the buffer zone.
- The height column is the elevation difference for LiDAR points 0001-0004, whereas the distance column reflects the respective shortest distance value to their nearest road segments.
Shortest Distance Calculation
- After the height calculation is completed, the shortest distances between the LiDAR points (within the buffer zone) and their adjoining road are calculated by subtracting half the road distance from the centerline position location(Table 2).
- GIS based applications can determine the shortest distance between a point feature and a line or polygon feature.
- ArcInfo TM from ESRI is used to perform the shortest-distance calculations.
- The distance between the LiDAR point and its adjoining road centerline is stored as attribute data for the corresponding LiDAR point (Table 2).
Vulnerability Analysis
- The objective of the vulnerability analysis determines those LiDAR points that represent potentially hazardous trees.
- If the elevation of any LiDAR point represents a height that is greater than its calculated, shortest distance to the adjacent road, the object representing the corresponding point may pose a threat to the road, should the tall object fall perpendicular to the road.
- Li, then object = hazard Table 3 shows that in this data subset only points A and D are at elevation values greater than their shortest distance values.
- The analysis is relatively complicated as it would have to be repeatedly done for every section, where a tree is present.
- Thus, the relatively simple approach presented here can be considered more economical.
Risk Assessment Prioritization
- Hazard identification is the first step to risk assessment prioritization.
- The percentage of the road pavement that might be impacted should actually be considered the sweep over which the tree may fa ll (eqn 4) [Figure 5b] subtracted by the non-pavement portion (eqn 4) to assess the full area of vulnerable pavement (eqn 5).
- The traffic volume could be considered, and whe ther or not alternate routes exist could be incorporated as part of the evaluation process.
- In order to identify the shape and dimension of objects, an algorithm is required.
- The approach uses buffering to group LiDAR points belonging to a hazard area.
RESULTS
- In December of 2003, two inspectors were sent to the study area to conduct visual hazard identificat ion assessments.
- Because of tree density, cluster identification was not possible.
- A reas with 1 or 2 trees were listed as hazardous as those with 6 or 8; further system refinement could include algorithms to identify how many hazardous items exist along a specified road length.
- As such procedures become routine and the cost of technology decreases, this automated approach may eventua lly serve as a cost effective method by itself.
- By integrating growth trends for local vegetation, the values for H i can be automatically augmented annually, thereby precluding the need for frequent flyovers for data collection.
CONCLUSIONS
- An automated tree-hazard pred iction methodology that utilizes GIS and high quality remotely sensed data offers substantial advantages over existing manual process in terms of safety, speed, and efficiency.
- This method avoids the extensive use of labor in surveying long lengths of highways.
- Since the identification process is automated and computationally efficient, the process is many times faster than manual approach and can be achieved in a systematic and automated fashion.
- Thus, the deployed method demonstrates the capability of 3D LiDAR data combined with GIS-based software to identify potentially hazardous tall objects adjacent to major roads.
- Such a system can render substantial help to optimize evacuation route selection.
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Citations
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...LiDAR is being used in civil engineering applications most significantly in transportation for road modeling (Cai and Rasdorf, 2008; Tsai et al., 2009), sign inventorying (Wang et al., 2010), road defect identification (Zhang and Elaksher, 2011), and disaster planning (Laefer and Pradhan, 2006)....
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...…functionality from laser scanning and other remote sensing data including three-dimensional (3D) volume estimation for mining (Mukherji 2011), road documentation (Dong et al. 2007) structural identification (Shan and Lee 2005; Zhang et al. 2011), and emergency planning (Laefer and Pradhan 2006)....
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...The method outlined in this article builds upon recent research on the application of LiDAR technology in disaster management (e.g., Firchau & Wiechert, 2005; Laefer & Pradhan, 2006)....
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...One study developed a methodology for determining preferable disaster and hurricane evacuation routes in North Carolina (Laefer & Pradhan, 2006)....
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...…been used in many areas of applications such as 1 geographic information system GIS content generation; 2 disaster response and damage assessment Laefer and Pradhan 2006 ; 3 flood plain mapping Hollaus et al. 2005 ; 4 forestry Andersen et al. 2005 ; 5 urban mod- 1Doctoral Candidate, School of…...
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References
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...The extent of LiDAR point den sity is dependent on fly ing height and system dependent factors such as platform velocity, sampling frequency, and field of view (Axelsson 1999)....
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...For instance, to generate three-dimensional (3D) reconstructions of buildings in urban areas, Haala and Brenner (1999) proposed the combination of multispectral imagery and laser altimeter data (LiDAR) in an integrated classification....
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Frequently Asked Questions (10)
Q2. What causes the blockage of major roads?
other strong winds, and ice storms cause tremendous damage across the United States (US) including flooding, wind-based destruction of property, and blockage of major road networks from fallen objects.
Q3. What is the definition of a computer-based tool set for collecting, storing, retriev?
GIS can be defined as a computer-based tool set for collecting, storing, retriev ing, transforming, and displaying spatial data from a discipline-specific domain for a particular set of purposes (Burrough and McDonnell 1998).
Q4. What is the way to achieve the high level of detail?
Airborne LiDAR systems are capable of producing extremely detailed informat ion of even small scanned areas (Baltsavias 1999), but to achieve a high level of detail requires substantial processing time.
Q5. What is the cost of tree debris removal in North Carolina?
According to the North Carolina Division of Emergency Management, after hurricane Isabel, Bert ie County alone required tree debris removal from all roads in the county except three, totaling 780 km of road from which 52,865 cubic meter or 43,245.27 kg of debris was removed, at a cost of $1.6 million, over the course of 84 days (Canty 2004).
Q6. What is the main objective of the aforementioned algorithms?
The main objective of all of the aforementioned algorithms is to extract features, especially those related to geometry, from the LiDAR points.
Q7. What is the way to reduce the size of the dataset?
An effective measure to counter such resource intensiveness, while not losing the desired level of detail, is to reduce the dataset size.
Q8. How is the distance between the LiDAR points and their adjoining road calculated?
After the height calculation is completed, the shortest distances between the LiDAR points (within the buffer zone) and their adjoining road are calculated by subtracting half the road distance from the centerline position location(Table 2).
Q9. Why is there no generalized method to identify hazardous trees?
Because of budget restrictions, across the US there is no generalized, method to identify hazardous trees, despite the acknowledged expenses that they generate.
Q10. How many data points were reduced in this example?
In this simple example, the number of data points was reduced by 83%, which decreased computation time by 67% compared to the time needed to process the non-buffered data.