1. The DEM layer shows elevation of the area of interest. The cell size is 32.79768284, 32.79768284. The cell size is important because helps identify x and y coordinates to locate information.
2. It is helpful to reclassify this way because water is either there or it isn’t. There is no intermediate value for the presence of a lake or a stream. If an area has water present (value of 100), the resulting cost surface will be higher in that area. If there is no water present (value 0) the cost surface will be less.
3. Land cover could also be used to calculate cost distance. Including this information would create a more thorough and accurate representation of what is actually there than the slope/water cost surface we have generated. Adding terrain type or land cover to the analysis would make the representation more realistic.
4. The attribute table for the Observer Points raster layer tells us whether the area the mushroom hunter may be in is visible from the lookouts. It gives this information as binary 1 and 0 for yes or no. Cells assigned a 1 are visible and cells assigned a 0 are not visible.
5. I recommend the forest service use lookout 1 for their search. More area is visible from lookout 1, including the known planned final destination of the mushroom hunter.
6.
Looking at the basemap, I would prioritize searching in the area between the start point and Nash Lake that have lower elevations and easy access to water. Since the mushroom hunter is lost, they are likely looking for water and staying low. On the other hand, it may be beneficial to search at higher elevations if the mushroom hunter decided to go to higher ground for a potentially better view of where they are. In reference to the basemap, this would be in areas just southwest of the startpoint or closer to Nash Lake and the immediate surrounding area.
The least cost distance map shows the level of difficulty to cross or maneuver an area with lighter hues representing an easier level of difficulty and darker hues representing more difficulty. This layer was generated using slope and water body information. In creating this layer, I encountered an error in reclassifying the slope and too many classes were the result, making my map of the cost distance more difficult to interpret. Instead of showing clear areas with different difficulty levels, mine shows a gradual gradient of difficulty, which makes it more difficult to determine which areas are harder to maneuver than others. Unfortunately, because of this my map wouldn’t be as helpful in finding the lost mushroom hunter.
The least cost distance map also includes the least cost path. This would be an appropriate area to search for the hunter if it is determined that they chose to remain at lower elevation. Adding a buffer to the path would help in searching for the hunter as well, in order to broaden the extent of the search with some direction and constraints. This would make searching the wilderness less daunting for the forest service while being able to search a large area.
Finally, the map showing the lookouts and the visible areas from them shows that the search party should look in the areas that are not visible by either of the lookouts. This would be the area west of the start point and not immediately around Nash Lake. This approach coincides with the leas cost path, as it is an area with relatively lower elevation. However, if somebody sees something of interest from Lookout 1, the searchers should look in the area surrounding Nash Lake, since that is the only area visible from Lookout 1. The searchers should use Lookout 1 for their search almost exclusively at first. If they are unsuccessful in finding the hunter from there, they should move their looking to Lookout 2 to cover a larger area that is of lower elevation.
While this method of evaluating the level of difficulty involved in maneuvering an area is useful to an extent, there are other factors that would make a better analysis. For example, including land cover, vegetation type, and terrain type would provide a much more extensive and perhaps accurate representation of “cost”. Another limitation of this method is the way water bodies are dealt with. In this analysis, water was assigned a class system that simply indicates whether or not it is present. Depth and dimensions of the water bodies were not taken into account in calculating the cost. This would be difficult to do, since water bodies change over time and are not necessarily discrete in their nature. This kind of information would be very helpful in calculating a more accurate cost if it were possible to obtain.