Using Lidar Data for Mapping Trails
Lidar (light detecting and ranging) is a technology that uses lasers to reflect light off surfaces or objects to create digital three dimensional recreations them. It can model environments in stunning detail. The Oregon Department of Mineral Industries has a collection of high resolution Lidar data covering much of Oregon that is a available to the public. In creating modern cartography, Lidar data is a key source in visualizing and interpreting the landscape.
DOGAMI Bare Earth Lidar data has been compiled in a Digital Elevation Model that reveals the contours of the ground surface and can be processed into a Hillshade to create stunningly realistic representations of the environment. You can also create a Slope Analysis view that allows visualization of the slope of each data cell from 0 (flat ground) to 90 degrees (vertical cliff). For viewing Slope data I employ ESRI's "magma" color ramp that ranges from black at 0 through purples and oranges to near white yellow for vertical 90 degree. This makes flat areas readily apparent as darker, or deeper purple, and steeper, what would be more challenging terrain as oranger with very distinct views of where cliffs are. For instance, the in the banner above the long bands of hot yellow on the right of the image are the steep cliffs of Mississippi Head on Mt Hoods southwest slope.
Lidar derived Hillshade Terrain
Lidar derived Slope
Exploring the slope data is a great way to pick out geologic oddities, safer and less steep routes, and especially waterfalls. Water courses in hydrology are in general flat, so the most current channels are easier to identify on slope analysis, and waterfalls, are easily discerned as a bright perpendicular band across the flow. For example, here is a view of Disappointment Falls along Cast Creek, and the very distinct exposed cliff alongside of it. The waterfall is not a vertical drop but a steep slope, a lighter orange/purple band. Compare that to Apparition Falls further south on the creek, a steep vertical drop and bright orange band.
Hillshaded Terrain and Slope views are an excellent realistic visualization of terrain, and because of the high resolution of the data, it is easy to identify roads. A view of the National Forest Land reveals the roads in stark detail, and shows the history of timber access. Long since overgrown and decommissioned roads are visible from the Lidar data. I use these views to trace my Roads layers for maps instead of relying on available transportation data that is often more generalized and and not aligned with the actual road beds. Below is a view of Forest Roads 1828 and 1828-118, with Top Spur trailhead visible as a bulge just after the bend in the road on the far right side.
On trail overview maps with a large scales like the 1:19000 scale of the Timberline Map I am working on, the width of the line visualizing the trails location would be over 50 ft wide, so the smallest twists and turns of the trail aren't visible at such a scale. So why try to get the most accurate placement of trails? One of my goals has been to try and generate more accurate estimate of elevation gain/loss along the trail, as well as total distance, and this serves that end. Many applications that estimate elevation change like GaiaGPS or Alltrails use lower resolution DEMs and what can be more generalized trail placement from OpenStreetMap, so I hope to get a more accurate estimate by refining the trail and using lidar derived DEM to assess elevation change along it. In some areas, such as the switchbacks shown below, parts of the trail is easily traced in Aerial Imagery, but additional resources are needed once it enters the forest.
Trail Tracing utilizing Aerial Imagery
OSIP 2018 Aerial Imagery
Compared to current OpenStreetMap data
Trails are visible on Slope Analysis, but harder to discern than roads because of their smaller footprint. Some well established wide and well benched sections of trail are easily identified. For more narrow trails, especially on sloping terrain, the Slope analysis is a great tool for pinpointing their location where the canopy cover hides the trail from aerial imagery. The pixels in the slope analysis are a meter square, so the color variation is possible across a very small distance. The slope view and photo show a section of the Timberline Trail where the bench is visible as the darker pixels cutting through the canyon.
After research, in the field observations and GPS tracks provide a view of the current conditions and a generalized location of the trails. I use GaiaGPS on my phone to record tracks, which I can export as GPX files into ArcGISPro to use as references.
My GaiaGPS tracks around Mt Hood National Forest
This view of the Timberline Trail along the Zigzag River show how variable GPS tracks can be, where I have over a dozen tracks from the trail, but they all have variance.
Zoomed in with trail drawn over, easier to distinguish the corridor and especially switchback as changes in pixel color.
Data Overlay on 3D Terrain derived from the Bare Earth Lidar
Here's a 3D view looking into Heather Canyon where the Timberline Trail crosses, that shows the trail and a better alignment than on OpenStreetMap. I choose to map the crossing where the trail follows further past the top of the falls, where I've always found crossing to be easier and safer, and the trail naturally leads there now.
OSIP 2018 Aerial Imagery
The drawback of this process of manual manipulation of the trail is that it is quite time consuming to apply so many vertices along the minor undulations of the trail. Placing the trail also requires special attention and knowledge of the current conditions, because the Lidar data for the area was gathered from 2009-2015. This means, for instance, there was no bench for the 2017 reroute of the Timberline constructed after the Eliot Branch blew out the original crossing upstream. Aerial Imagery, trail GPS tracks, and referencing Google Earth'3d imagery help establish the route there.
Using Lidar in visualizing the Mountain
The Lidar data can create beautiful detailed visualizations of terrain to serve as the basemap for a map. In creating my Timberline map background, I have layered and blended over a dozen layers, including many that are derived from the DOGAMI lidar. Highest Hits lidar hillshaded data layers visualizes canopy cover to the level where you can identify individual trees, buildings or large boulders. By blending together many different lidar derived layers and elements like colorized polygon layers of vegetation I have created, and snow coverage extracted from imagery, I strive for an artistic background that balances modern data with a somewhat classic look, without excessive noise of aerial imagery. A background that is beautiful to look at and displays terrain in a way that is interpretive to what is encountered on the ground, inspiring users to have a better understanding of their place in the environment while hiking.
Fires and Windstorms!
Since the collection of the Lidar data, fires like the 2011 Dollar Lake Fire have reshaped the forest canopy around Mt Hood. Since many of the areas have trees still standing, I've blended grayed color polygons traced over present stands of burned area to indicate the burned areas on the map. Other areas, like the massive windstorm damage caused by the Labor Day 2020 storms transformed vast swaths of forest, for which I chose to utilize photoshop and edit the basemap I created to better represent the current conditions, and help hikers visualize the terrain as they may see it today from across the Muddy Fork on Bald Mountain.
Basic Lidar derived Highest Hits Hillshade
Edited, Stylized Basemap
Cleaning up Lidar.
Since Lidar data is collected by different projects, it is mosaicked together, and along the borders there can be artifacts such as long lines that appear where they mesh. These lines where datasets meet are easily identifiable (you can find them in GoogleMaps for example, which also uses DOGAMI lidar as it's terrain hillshade. To remove these from the basemap was a task I accomplished in photoshop by editing the pixels along the lines to blend them in. Below is a before and after of cleaning the diagonal line across the terrain.
Bringing things together.
A view of the north side of Mt Hood.
A map preview
Here's a snippet of my map (still a work in progress) with all my data layers combined looking at the Eliot Glacier and Cloud Cap Saddle area.