A Least-Cost Algorithm Approach to Trail Design ...

A Least-Cost Algorithm Approach to Trail Design Using GIS Ioannis Kokkinidis1,2*, Beth R. Stein3, Jayashree Surendrababu2, Taylor Seigler1, Won Hoi Hwang3, Laura Lorentz3, Catherine Howey1

Photogrammetric Engineering & Remote Sensing (PE&RS) 1

Department of Geography College of Natural Resources and Environment, Virginia Tech 2

Department of Crop & Soil Environmental Sciences College of Agriculture and Life Sciences, Virginia Tech 3

Department of Forest Resources & Environmental Conservation College of Natural Resources and Environment, Virginia Tech *Corresponding author

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Abstract The growing interest in outdoor recreation increases the demand for cost-effective trails that meet user needs and preserve the natural environment. The objective of this project is to develop and implement a geographic information system (GIS) tool to assist the Boy Scouts of America in trail design for the Philmont Scout Ranch in New Mexico. Our Trail Design Tool generates, maps, and parameterizes trails using the least-cost path algorithm. With significant variations in the algorithm across the literature, we developed a novel approach that optimizes trail connectivity, natural resource preservation, safety, usability, cost, and scenic appeal. We differentially weighted the following variables in our cost surface: slope, landmarks, path suitability, and land cover/vegetation biomass. User-specified difficulty level, desired points of interest and use, and start and end points determine the final path. The tool produces a highquality map document depicting the new trail and a geospatial file containing the trail path and attributes. We demonstrate the tool’s functionality through the design of a new multipurpose trail between Black Mountain and Hunting Lodge. The Trail Design Tool is widely adaptable to suit a variety of trail design parameters for the Boy Scouts of America and other trail managers.

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1. Introduction Recreational trails offer outdoor enthusiasts an enjoyable way to view and experience an outdoor setting (Xiang, 1996). Trails provide opportunities to hike, bike, run, ride All-Terrain Vehicles (ATVs), and horseback ride. In 2011, nearly 50 percent of Americans participated in one of these activities, indicating a growing trend in outdoor recreation (Outdoor Foundation, 2012). For the Boy Scouts of America (BSA), trails also provide a venue for environmental education, development of new skills, character development, and fellowship (BSA – Philmont, 2013). The practical design of suitable trails is important for safety, recreation, minimizing environmental impact, and cost. However, trail design is a time-intensive and methodical process involving many different variables in order to determine the optimal location and layout (Xiang, 1996). One organization estimates trail planning to be about 2 percent of trail cost per mile, which could be several thousand dollars (NIRPC, 2010). With such steep expenses and the limited budgets in today’s economy, it is crucial to constrain costs when possible. Geographic Information Systems (GIS) can greatly improve the effectiveness and efficiency of trail design. GIS offers the ability to combine and visualize data layers and to quickly conduct geospatial analyses. While many software packages offer the ability to implement the least-cost path algorithm, there is currently no standard algorithm or tool for trail development.

2. Project Objectives The goal of this project is to design and implement a cost-effective tool for the Boy Scouts of America that can generate, map, and parameterize trails using the least-cost path algorithm. Specifically, we aim to develop a new multi-purpose recreation trail for the Philmont

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Scout Ranch in a way that maximizes trail connectivity, natural resource preservation, safety, usability, and scenic appeal, while minimizing cost. The final products of the project are (1) a trail design algorithm and tool; and (2) a digital map which visualizes and provides information on the new trail.

3. Methods 3.1 Project Area Our study area is the Philmont Scout Ranch, a BSA High Adventure Base for outdoor recreation, environmental education, and land management. Philmont covers 55,000 ha in Colfax County of northern New Mexico (Figure 1), in the Sangre de Cristo range of the Rocky Mountains. The elevation ranges from 2,000 to 3,792 m, and consists primarily of forest and prairie ecosystems. Throughout the Ranch, there are 34 staffed camps and 55 trail camps (BSA – Philmont, 2013).

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Figure 1. Map of the Philmont Scout Ranch, a Boy Scouts of America High Adventure Base, depicting the Ranch boundary, existing trails, and points of interest, overlaid on a 61cm orthophoto.

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3.2 Design Priorities Our tool aims to achieve the following trail design goals: usability, connectivity, preservation, safety, minimal cost, and scenic appeal. Usability is perhaps the most important requirement for a trail due to the need to support a number of visitors. Soil conditions, slope, and the location of preexisting trails are good indicators of trail sustainability (Coleman, 1981; MA DCR, 2012; Olive and Marion, 2009). Usability also depends on connectivity, or the state of trail linkages to access points and/or other trails within the area. In general, trail use is likely to rise with increased connectivity (Gordon et al., 2004). Trail designers can take into account connectivity by choosing appropriate start and end points, as well as considering the locations of preexisting trails. Natural resource preservation is essential to maintain an ecologically sustainable trail system. Trail construction can cause many adverse ecological impacts, including vegetation loss, change in vegetation composition, trail widening, unintended trail development, erosion, and soil loss (Olive and Marion, 2009). For these reasons, we used biomass and land cover data to minimize vegetation disturbance, and slope and soil suitability data to reduce soil loss through erosion. Desirable views help retain trail visitors in designated areas. Safety is an important concern because it affects Philmont’s reputation, visitor enjoyment, and park expenses (Jorgensen et al., 2002; Schroeder and Anderson, 1984). In consideration of visitor safety, we included slope, soil suitability, waterbodies, and proximity to staffed camps as variables. Steep trail grades and certain types of soils tend to erode and introduce greater risk, thus decreasing user safety (Snyder et al., 2008). Waterbodies and their surrounding floodplains also present additional risks to visitors, particularly under severe weather conditions. In the case of an emergency, visitor proximity to staffed camps increases safety

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through access to personnel, facilities, communication, and medical equipment. They also tend to offer more hospitable terrain than their surroundings. Trail costs depend on the removal of vegetation and the construction of built features (i.e. bridges, boardwalks, and erosion controls). We minimized costs by considering vegetation type, biomass, slope, and waterbodies. Similarly, construction costs per mile depend on the soil suitability, slope, and preexisting trails (Hesselbarth et al., 2007). Scenic appeal is the view of natural features and proximity to landmarks. Trails with interesting views and features attract more visitors and garner more value than those without landmarks. They help people navigate, prepare for what lies ahead, and appreciate their journey (Kaplan et al., 1998). Mountains and water features serve as excellent views, fulfilling many of the above functions. As visitors often leave trails to seek vistas or features not accessible by trail (Park et al., 2008), scenic appeal is closely related to safety, preservation, and maintenance costs.

3.3 Tool Development Using the trail design priorities, we developed a novel algorithm (Figure 2). First, we compiled GIS data layers for each of the main variables that influence trail placement (Table 1, Figure 3). These variables include slope, soil suitability, land cover, and proximity and visibility of preferred landmarks to generate our cost surface raster. Next, we processed the variables and converted the vector files to rasters, as necessary. We reclassified all rasters according to their relative weight, a subjective valuation of their properties to which we arrived after trial and error, and user inputs; the relative weight estimates how “costly” it is to cross the pixel (Figure 4). Then, we added together the resulting “friction surfaces” to create a “generalized cost surface.” The cost surface for the Trail Design Tool uses the following formula:

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Cost Surface = Slope (60%) + Soil Suitability (10%) + Vegetation (20%) – Landmarks (10%)

This generalized cost surface, together with the desired start and end points, creates an individualized cost surface. After summing the costs on a per-pixel basis, we selected the path with the lowest cost (Figure 4). We snapped all rasters to a 2m Digital Elevation Model (DEM) of Philmont. We explain the variable selection, weighting, and processing methods below. Tables 2 and 3 present our variable reclassification scheme for the final cost surface.

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Figure 2. Least-cost path algorithm flow chart. The basic steps to generate our cost surface are data acquisition, data processing, reclassification, and cost summation. The least cost algorithm then chooses the least-cost path as the optimal trail.

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Table 1. Variables for the Boy Scouts of America tool for trail design. Each variable addresses one or more of the specific trail design priorities. Priority

Variables

Connectivity

Preexisting Trails

Preservation

Slope, Soil Suitability, Land Cover, Viewsheds

Safety

Slope, Soil Suitability, Staffed Camps, Waterbodies

Cost

Slope, Soil Suitability, Land Cover

Usability

Slope, Soil Suitability

Scenic Appeal

Points of Interest, Viewsheds

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Figure 3. Geographic information system (GIS) data layers of the selected variables for input into the least-cost path algorithm at Philmont Scout Ranch: (a) 2006 National Land Cover Dataset, (b) Digital Elevation Model-derived slope, (c) lidar-derived biomass, (d) soil series, (e) points of interest, (f) waterbodies, and (g) existing trails.

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3.3.1 Slope Due to its importance among the design priorities, we allocated 60 percent of the value of the cost surface to slope. Tool users have the opportunity to choose an easy, moderate, or difficult trail (Grimwade et al., 2009). We calculated percent slope from the DEM, and encouraged the algorithm to choose routes with lower slopes by differentially weighting the slope values based on the user-specified difficulty level. Table 2 shows the slope raster reclassification values. The algorithm assigns less desirable values to all pixels with slopes over a particular threshold; the threshold depends on difficulty.

3.3.2 Soil/Path Suitability We minimized cost by situating the trail in areas that are not prone to erosion and by privileging the use of preexisting trails. We assigned a weight of 10 percent of the total cost surface to soil erodibility and conformity to trails. Although relevant to nearly all of the design priorities, the lack of suitable soils within Philmont and our goal of creating a new trail lowered its relative weight. We used the Soil Survey Geographic Database (SSURGO) to assess soil suitability for trail establishment. The Natural Resources Conservation Service (NRCS), U.S. Department of Agriculture, categorized the soils within Philmont as “not limited”, “somewhat limited,” “severely limited,” and “not rated,” based on soil properties that affect trafficability and erodibility (NRCS, 2012). Table 2 depicts the raster cost surface assignments by soil class. We also subtracted the preexisting trails, with a 2m buffer, from the ensuing layer.

3.3.3 Vegetation Biomass and Land Cover

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We allocated 20 percent of the total cost raster to vegetation biomass and land cover, due to the high cost of vegetation removal and our desire to preserve the native vegetation. The biomass data provides a realistic estimate of the vegetation quantity in the area. We determined biomass for Philmont using the lidar data. After extracting the first returns, we created a canopy height model from the DEM and calculated the above-ground vegetation biomass (AB) per pixel using the following formula: AB = 0.378 * MCH2 ,

where MCH is the Mean Canopy Height (Lefsky et al., 2002). As lidar data is only available for the southern part of Philmont, we supplemented the data with the NLCD to provide a land cover classification for the remaining areas. We used a high resolution orthophoto to evaluate the quality of both our biomass layer and the NLCD. The biomass layer is capable of distinguishing between bare ground, shrub and forest and has a very small pixel scale. On the other hand, the NLCD is much coarser (30m) and does not successfully distinguish shrubs from bare ground and forest. The hindrance and cost of vegetation biomass depends on the assigned trail use. Hikers are hindered by low-lying vegetation, but can harmlessly pass under large tree canopies. On the other hand, horses and ATVs need a wide berth and thus require clearing all vegetation through which they pass. For this reason, we used a differential scale for raster reclassification, dependent on trail use (Table 3). Within the lidar-flown region, we generally characterized areas with a biomass of less than 1 Mg ha-1 as bare ground, between 1 and 10 Mg ha-1 as shrub, and greater than 10 Mg ha-1 as forest.

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Due to the inaccuracies of the NLCD, we lacked the confidence to differentially weight the land cover classes by trail use in the remaining areas. Instead, we discouraged the use of forests and water features for trails, using the hydrography dataset.

3.3.4 Landmarks We advantageously weighted mountain and water feature visibility, as well as camp proximity, by assigning them 10 percent of the total cost surface. We used Philmont “Points of Interest” and water features layers to generate three rasters showing the following locations: 50m proximity to camps, mountain visibility, and water visibility. We also created rasters for each combination of selected features. Based on the user-inputs, our tool will select the appropriate raster and subtract the values from the cost surface. If the user does not prefer a particular feature, the algorithm will retain the current cost surface.

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Figure 4. Final reclassified rasters for cost surface generation in the least-cost path algorithm: (a) Land Cover, (b) Water Viewshed, (c) Hiking Biomass Removal, (d) ATV Biomass Removal, (e) Path Suitability, (f) Easy Slope, (g) Medium Slope, and (h) Difficult Slope. Multiple viewshed rasters account for all other user options.

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Table 2. Raster reclassification scheme to generate the cost surface for the least-cost path algorithm. Variable reclassification is according to its relative weight, a subjective valuation of its properties, and the user inputs. Less desirable classes have higher reclassification values to decrease their probability of selection by the algorithm. The tool sums the values of all rasters on a pixel-by-pixel basis to determine the least-cost path. Raster Variable

Weight (%)

Reclassification Values

Slope

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A = 5% for easy, 8% for medium, 15% for difficult Slope ≤ A = Slope A < Slope ≤ 30% = 2 x Slope Slope > 30% = 60

Land Cover and Biomass

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See Tables 3 and 4

Landmarks

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Pixel not in desired viewshed = 0 Pixel in desired viewshed = 10

Soil Suitability

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Not limited or preexisting trail = 0 Somewhat limited or unrated = 5 Severely limited = 10

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Table 3. Reclassification values for the lidar-derived biomass (Mg/ha) according to trail use. Biomass was calculated using the mean canopy height of the lidar first returns (Lefsky et al., 2002).

Biomass (Mg/ha)

Reclassification Value by Trail Use Hiking and Biking

Equestrian and ATV

x