Optimal Areas for Backcountry Skiing in Maine

This analysis identifies optimal areas for backcountry skiing in Maine using the following six parameters: elevation, distance to roads, average annual snowfall, aspect, slope, and population density. The most suitable areas correlated well with locations that were ski areas in the past, were once considered for ski area development, or are currently used for ski touring. Introduction The recreation of backcountry skiing is becoming increasingly popular as people want to escape the crowds and prices of ski areas, explore new and interesting terrain, and get a better workout. From an environmental perspective, backcountry skiing also greatly reduces personal impact on nature, while still allowing people to enjoy the activity. Ski touring involves hiking unmanaged mountains for the purpose of backcountry skiing. With the huge amount of potential backcountry terrain in Maine in mind, we set out to determine the optimal areas to ski tour within conserved land using only geographic information systems (GIS) and analysis. We accounted for a variety of factors, including elevation, distance to roads, average annual snowfall, aspect and slope of the mountain, and population density, as indicators of an ideal location for skiing. Methods We used ArcGIS(ESRI) with data acquired from the Maine Office of GIS and the National Oceanic and Atmospheric Administration projected using NAD 1983 UTM Zone 19N to create our map. The digital elevation model (DEM) was created using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Using the DEM, we determined hillshade, aspect, and slope with their respective spatial analysis surface tools. We created another layer showing the distance of land from roads by Euclidian distance. Next, we converted a census block population density layer into a raster. In order to create the snowfall layer, we created a data set using average annual snowfall for over 350 towns in Maine and their respective geographic coordinates. This data was converted from individual points into a raster and then interpolated using inverse distance weighted technique. We then reclassified all data to a uniform scale and created weighted overlay, assigning an individual value to each of our variables out of 100 percent. This overlay of variables is designed to show a Suitability Index for skiing. Values were given based on importance to skiing quality and convenience to potential skiers. Results and Discussion