For this lab assignment we were given the task to find a missing hiker who had gotten lost somewhere along the Pacific Crest Trail. This hiker happened to be the actress Reese Witherspoon. Reese had planned to be back from her backpacking expedition on an earlier date, and had pre arranged a pick up from her known end point, however after she did not show up to her pick up point the searching began. This is where we began the process of trying to find Reese through spatial analysis.

The first thing that needed to happen was to obtain the necessary data to help the search. Using the University of Oregon’s GIS data server we were able to obtain a Digital Elevation Model (DEM) of Lane County in Oregon. This file had a particular nae to it, lanedem_10m. This name meant that the resolution of this particular raster data is 10 meters. Each 10×10 meter cell in this DEM has its own associated weighted value, and we used this large file to help construct a Cost distance surface later on in the lab. Other files included KML files of the known starting and ending points of Reeses route, which we had to use the KML to layer tool inside ArcMap to convert. Also fire lookout points provided by the United States Forest Service, and a polygon file that represented the various bodies of water in the area.

Below is a very basic base map that gives the area that Reese had been lost in. This basic map would not be very useful in helping the search for Reese. However with the use of various GIS applications and processes we would later be able to pin pint where she could be.

Now that we have our data we could then start to perform some GIS analysis to try and find our lost hiker. In order to narrow down the search we trimmed the DEM and water bodies polygon to only the area that we knew Reese was in. After we had finished up with the DEM and water bodies, we then had to create a map of Reese’s relative cost of traveling through this harsh terrain, this is called creating a Cost Surface Map. We used ArcMaps slope tool to calculate the slope of this particular search area. Using this tool we were able to reclassify the landscape into nine distinct levels of difficulty in terms of elevation. Using this cost distance tool and the water bodies polygon we were able to find the best route of travel for Reese with the least amount of exertion. The water bodies polygon was also converted into raster form and was reclassified with weight that was correlated to the elevation of the particular water body. During our least cost analysis the idea was that the water bodies would have a greater weight of traveling than the mountain landscape because it would be much harder to swim across a lake than hike through some mountain terrain. I believe we could have also used a series of land cover data or forest coverage data to help create a cost surface as an alternative to the one that we conducted in class. Also we could have found data on the locations or density of known mountain predators in the area, and weighted the higher than areas where there we not many reports of these animals.

Below is a map of the Cost Distance Surface of travel for a lost hiker.

The next step in our search for Reese was to implement the lookout point data that we got from teh USFS. Using these points on the DEM raster data, ArcMap was able to tell us what part of the landscape was visible from the particular lookout points. Inside the attribute table of our observed file we found out which lookout point was capable of viewing what part of the landscape in a raster form of analysis.

After observing the data that we analyzed through ArcMap I would have to say that the Forest Service should focus on the area that lookout point 1 can observe. In the map below you can see that the areas on the map that are colored yellow are the spots on the map that are observed by lookout number one. The yellow markings on the map are also close in proximity to a large section of the least cost distance analysis that we performed earlier. So I would advise the Forest Service to use lookout point one for the search.

To find Reese I think it would be easiest to start the search near the end point on the map and work your west towards the lookout point 1. This is because this area directly intersects with the least cost path that we found using the Cost surface analysis. Also if you started near the end point you would have great visibility once you reached the lookout point 1.