Filed under: Climbing, Mountain | Tags: biking, eldora, eldorado canyon, ski
Here are a few more local adventures I’ve come up with that don’t require a vehicle. The first is climbing in Eldorado Canyon, which happens to be one of America’s premiere rock climbing destinations and is only a relatively flat 6 miles away from my front door. An obviously bikeable trip, but finding a climbing partner with the same opinion was not easy.
And now that we live in a place with a real winter, I’ve had to broaden my pursuits to activities that take advantage of the snow. Like skiing! I skied off-and-on while living in Davis, but now that we are in Boulder I decided to actually buy a pair of skis. Thanks to the awesome Boulder Sports Recycler, I got a light pair of telemark skis for $55. The boots, well they were a bit more expensive, but with $100 of gift credit at REI, it only took another $200 to get a new pair of Scarpa T4s. With the convenience of the Boulder-Eldora bus, I was able to get out and ski up Jenny Creek to the Arestua Hut without ever getting the car out of the garage.
Filed under: cartography, Mountain | Tags: digital elevation model, elevation data, geographic information science, geomorphology, geomorphometry, GIS, image analysis
As I continue to think and learn about terrain analysis and geomorphology (see these posts), I find that the issues are complicated. My first project was to develop an algorithm to extract ridge lines from a digital elevation model (DEM). The first ingredient for such an algorithm is a clear definition of what a ridge is and I came to realize that the way I identify a ridge is rather ambiguous. At first I thought that a ridge would be any place with a certain convexity, but as you can see in the image below, two terrain features with the same convexity might be classified as different things.
The iconographic ridge is long, straight and sharp at its apex, like many ridges of the High Sierra. As the ridge becomes more broad and rounded, at some point the landform will be identified as a hill rather than a ridge. But what is that point? This issue has been considered by others. I recently found a book, “Geographic Information Science and Mountain Germorphology”, in which a chapter was co-authored by a philosopher and dealt with the ontology of topography and how we as humans identify objects such as mountains or canyons that don’t have a clear boundary.
Despite all that, I still did my best to write some code that would isolate what I would identify as a ridge in some mountain terrain. Here’s the best I’ve come up with so far with the “ridges” being the white lines or points:
On the left is the raw elevation data. On the right is roughly how I would identify the ridges by hand and in the middle is the result from the computer identification. You can see that the main features are there, but there seem to be many extraneous points and some of the main ridges are fragmented.
One landform that is not ambiguous is peak. Mathematically, a peak is a local maximum, it’s the point with the highest elevation within some neighboring area. Below is an image of all the local maxima from the elevation data shown above. Now you can start to ask questions about why the peaks are organized the way they are, why are there as many as there are and so on. I found that this type of analysis is done in the field of geomorphometry and a new book, “Geomorphometry: Concepts, Software, Applications”, has been my introduction. There is much more to learn.
Filed under: Mountain | Tags: biking, Hiking, longs peak, meeker ridge, mountain, mt meeker, rocky mountain national park
I continued with the theme of using a bike to get into the mountains, but I took it to the next level. While living in Davis, I had contemplated biking to and hiking Pyramid Peak, near Lake Tahoe, but the whole experience would have been a massive bike ride with a little hiking tossed in. Now in Boulder, there are far more options for an excursion where the biking and climbing are more evenly split. The nearest high mountains are in the Indian Peaks Wilderness, but it seemed that there were better biking conditions a little north on highway 7 and I had still not been into Rocky Mountain National Park. So I settled on biking up to the Peak-to-Peak highway, ascending Meeker Ridge on Mt. Meeker, traversing over to Longs Peak and then reversing the route back home. It was incredibly satisfying to leave our driveway on a bike, touch the top of Longs Peak and then roll back home about 36 hours later. Here is a photo sequence that captures the experience.
One of the best parts about living in Boulder is the proximity to the outdoors. Going climbing doesn’t involve 4 hours of round-trip driving, it’s a mere bike ride away. The same goes for a hike up a moderate peak. Before moving to Boulder, I was becoming uncomfortable with the amount of driving required for a trip to the mountains. Part of my discomfort came from the fact that as a person in love with the wilds, I’m part of a community that agonizes over damaging shrubs at the base of a cliff or dropping a wrapper on the trail, and yet there is no mention of the gallons of gas burnt on the way to the trailhead. The other reason for my unease was the realization that some climbing trips were in fact driving trips with a little climbing thrown in.
So, I began thinking about ways to shift the balance away from the car. The eastside tour was an experiment with that idea, combining a bike tour with hiking and climbing along the way, but in reality turned out to be mostly a biking and driving trip with a tiny amount of climbing and hiking:
Here in Boulder, it’s much more practical to leave the car at home. So here is a graphical summary of three local adventures without the assistance of a car. The size of the colored bars represent my subjective measure of the weight of each component, whether bike, bus, walk or climb. And not only is a car not required but they can all be completed in the morning before work! Ah, Boulder…
Filed under: cartography, Mountain | Tags: geomorphology, GIS, image analysis, mountain
My first steps into geographic analysis were to experiment with GIS and get the geographic data into a format I understood and could use. Now the fun starts.
I have a keen interest in the ridge lines of alpine terrain, which I’m sure is influenced by my passion for climbing. So my first idea was to develop an algorithm and computational routine to identify the ridge lines in an elevation map. I started with a small elevation map, shown below, covering the area immediately around Mt. Whitney. Lighter colors correspond with higher elevation (the summit of Mt. Whitney is at the center). Just looking at the image, it’s not difficult to identify the major ridge lines, like the ones I outlined in red. Telling a computer how to do the same thing is not easy.
In my first attempt, I wrote a short octave script that would find the locations where the elevation sloped downward in both east and west, or north and south (if it slopes downward in all four directions, than it’s a peak). The image below displays a white pixel wherever a ridge was identified. The result is mediocre. Of course there are minor ridges identified (like the ones on the west slope of Whitney, which are truly there), but the bigger problem is that even the major ridge lines are discontinuous.
Excitingly, I have found current research being done in this area.:
Taking a hint from the paper above and this group in Zurich, I think I should use some strategy like the Watershed algorithm. Here’s a peak at what I’m working on:
I recently discovered that airborne lidar data is freely available for a handful of US locations. And one of those locations: Yosemite Valley. That’s right, as a climber, I couldn’t have been happier. Visualizing the data is not a simple task. Airborne lidar data is produced by flying a plane low over the region of interest with a laser scanning over the surface of the earth. At each spot that the laser reflects off the ground, the elevation and position of the ground is recorded. The result is huge collection of x,y,z points blanketing the earth. It takes special software to manage the lidar data, primarily because a laser scan can easily produce millions of points. One piece of the Yosemite Valley lidar data covers the area around El Capitan with about 8.6 million points.
Thankfully there is academic work being done on lidar data and analysis and the software that has been developed is also freely available. Coincidently, the Institute for Data Analysis and Visualization here at UC Davis, is one of the organizations working on this topic and I used their LidarViewer and VRUI to visualize the El Cap lidar data. In addition, LASTools, developed at the University of North Carolina, provide a number of useful software tools to inspect and manipulate the lidar data (which has the file ending .las, hence the name LASTools).
The result of all of this is a cool 3D model of El Capitan.
There are holes in the data which I think were caused by overhanging sections of rock where the airplane mounted laser was not able to reach. I should also point out, that although the surface looks continuous in some places, it is actually composed of discrete points (about 8.6 million of them). You can zoom in quite close and see that the points reveal the fine detail in the cliff.
This is a close up view of the Alcove and Footstool
This one shows Mammoth Terraces and the Half Dollar
Cartography is firmly in the digital age and has expanded into the field known as GIS, geographical information systems. Not a very exciting name, but it has revolutionized the way geographic information is presented and analyzed. Anything that can be related to geographic space can be presented on maps and analyzed for statistics or relationships. Want to know how the density of fast food restaurants correlates with political party distribution? I’m sure the answer is just a few mouse clicks away. Or how does highway and road density correlate with topography? Now that sounds interesting. Maybe someday, when I am more skilled with GIS software, I will answer that question.
As an initial foray into GIS, I decided to look at the topography around Mt. Whitney in the Sierra Nevada. In my science work, I manage and analyze spatial data and many of the same concepts apply to topographic data despite the radically different scales (nanometers versus meters). The difficulty was reconciling the different softwares and file formats used in molecular dynamics versus GIS. Once that was worked out, sort of, I started to have fun.
An image of the elevation on the 7.5 minute Mt. Whitney map (higher elevations are lighter), produced with qgis.
The image below shows the slope over the same area. The white regions are very steep and cliffy and the flat lakes are black. For climbers, images like this provide interesting research material. Although you can find cliffs on a topographic map by looking for places where the contours converge, I think it is even clearer in this presentation.
A 3D model of the east face of Mt. Whitney, produced with octave. I’m afraid it’s actually reflected north to south, but the idea is the same.
Lastly, a 3D model rendered in the molecular viewing program vmd. The surface is a sheet of balls because most molecules are represented as a collection of atoms, depicted as spheres.
I’m fascinated by the crest of the Sierra Nevada. The Sierra Nevada, especially the southern High Sierra, are mountains of sharply defined ridges, jagged peaks and steep walls. As I alluded to in the post about aesthetics and climbing, there is something pure about the true crest of the Sierra. A rain drop on one side ends up in the Pacific, and a drop on the other winds up in the Great Basin. It’s mathematically well defined as well. Physical chemists talk about chemical reactions using the language of mountains, with it’s troughs and basins and saddles. While scientists are not usually interested in the peaks and ridges of an energy landscape, they are just as well defined.
To satisfy my curiosity, I drew a map of the Sierra crest, from Yosemite to Mt. Whitney. I tried to depict the crest as accurately as possible, with it’s connecting ridge lines, but the ultimate goal is not navigation, it’s inspiration. There is so much more that can be done with this subject: analyze it mathematically, depict it digitally, sketch it differently, traverse it…
Filed under: Mountain
I’ve been scheming this adventure for the past couple years. The idea was sparked by reading Galen Rowell’s accounts of ski traversing two Great Basin mountain chains, the White Mountains of California and the Ruby Mountains of Nevada. In his fantastic book, “High and Wild”, he exposed the wildness, adventure and austere beauty of these desert ranges through his typically excellent writing and photos. I was inspired. But the White and Ruby Mountains are more significant endeavors than I’m prepared for. They involve on the order of 50 miles of traversing and peaks over 13,000 feet. I began searching for an alternative that had the same high desert flavor, but would fit my extended weekend time budget and novice skiing ability. The Sweetwater Mountains just north of Bridgeport, CA, fit the bill. An infrequently visited cluster of mountains, they would involve about 10 miles of traversing, 4 peaks, the highest being 11,200, and only about 4 hours driving from Davis. The possibility of spending one night in a hut sealed the deal. I was joined by 3 friends and we embarked on the 4 day adventure.
Of course, a necessary ingredient of any adventure is the unknown, and we discovered, upon arriving at the base of the Sweetwaters, that the two northern most peaks carried so little snow, that the trip would have become 50% hiking. Plan B: ski directly to the cabin (which turned out to be much further than I estimated for my companions and involved much skiing in the dark), and day trip up to the higher elevations. The highlights were the cabin with its blazing wood stove and the day skiing to the higher elevations which produced the best backcountry skiing I’ve experienced.
