Lab 4 - Fire Model

This week's lab involved creating surface fire models for La Cañada-Flintridge, CA, where the Station Fire broke out around summer to fall 2009.  My fire hazard model focuses on land cover and slope in relation to fuel hazard. By gathering data digital elevation model (DEM) data, land cover/vegetation data and perimeter data from USGS Seamless Server and the California Department of Forestry and Fire Protection (FRAP), I was able to design a map for slope within the perimeter (bottom right; using the exercise model as a reference for hazard), land cover (bottom left; after re-categorizing hazard based off of dryness criteria), combined hazard of land cover and slope (top left; depicting the fires that have the least to greatest likelihood overall) and Los Angeles County (top right; to show where the Station Fire was relative to the rest of Los Angeles).

In the Wildland/Urban Interface exercise fire model, we worked with raster images for vegetation type, fuel and slope. We used ArcGIS’s spatial technology to create classification systems for forest fuels and topography that gauge fire hazard. Referencing the National Fire Protection Association’s NFPA 1144 (Standard for Protection of Life and Property from Wildfire), we learned the relationship between slope and fuels while using Digital Elevation Models to determine risk. These skills ultimately help with preventing wildfires that may potentially occur around homes and addressing ones that pose the greatest probable risks overall. 

With that said, the exercise greatly helped with creating my own model. I first had to download the DEM file for Los Angeles County. I then converted the projected coordinate system of that file to UTM (NAD 1983 Zone 11N). I then downloaded a land cover file of Los Angeles County from the CA FRAP website. This is where the work became more tedious. The file had over 200 rows of data in its 'Attribute Table'. Using what I learned from the exercise, I had to determine how to reclassify the data according to the vegetation type’s likelihood to be fuel for a fire. This ultimately came down to 6 categories in which I have shown below. The table shows the types of land cover and vegetation that the values under the 'Land Cover' map's legend reflects.

Additionally, I determined the slope of the DEM and land cover files and created a Hillshade in which I used to transparently overlay on top of my slope and land cover maps. I added major roads in my combined map to help visualize the difficulty of accessing these hazardous areas. The steps that I faced the most difficulties in this lab were related to reclassifying and layout. Aside from the time it would take to determine my own legitimate criteria, the Spatial Analyst tools were also always malfunctioning due to: file names that were too long, forgetting to check certain boxes, issues with setting perimeters and reclassifications not appearing properly. Because of my perfectionism, laying out the right color schemes, sizes of the maps and proper legends also consumed a lot of time. Ultimately, the lab helped determine that the location of the Station Fire, a typical mixed shrub and forest environment that was not only dry, but was also at a higher elevation then the rest of the surrounding area, explains the frequent occurrences of fires in this area.