FIRE MAP

The map of the 2009 station fire in Los Angeles County required that we use raster data to create a slope map, a vegetation map, and a final raster calculation of both features. It also required that we needed to find data from outside sources not given to us from the class website. The methods used in the tutorial were useful in my personal lab application. The process of data acquisition a challenging steps. The methods of raster analysis masking and the reclassification of layers helped greatly to understand and create the final map. From previous knowledge, I decided to search the USGS seamless server and seamless viewer page to obtain the DEM information which I can then use to analyze my slope and create a hill shade. The raster masking tool that I used to clip off the unwanted parts of the DEM so that it could focus on just the LA county perimeter proved unnecessary because I ended up zooming into the fire parameters so the masking could not be noticed. The DEM was used to make the hill shade and slope map. The slope data needed to be reclassified accordingly. I also used the information and data from LA GIS Enterprise to get the fire parameters that wreaked havoc in the region on the Angeles National Forest in 2009. I was able to obtain the full extent of the fire information with the amount and area of vegetation burned during a specific time ranging from August to September. I also went to the Census.gov to obtain a tiger file shape file of data to overlap it onto my final map to show which cities and districts bordered the 2009 station fire. They included San Fernando Valley, Pasadena, and South Antelope Valley. The Los Angeles County shape file used for the inset map was obtained from UCLA GIS as a polygon from the website. The FRAP website also gave very relevant information on certain vegetation types that resided in the area. The given information with the vegetation was reclassified according to the ‘new values’ given in the tutorial from the Geog 169 class website so that the similar burn hazard vegetation can be classified together. The vegetation was classified to the appropriate new value level is so it can be classified from a high to low danger rating.
Problems that I encountered was the fact that the FRAP website could easily confuse a user in downloading the wrong type of information. The difference between the tutorial and my own lab tended to confuse me. The different data between the tutorial and within my own lab made it difficult to put together particular methods for solving certain problems. One step in the tutorial stated that you needed to click a radio button to link the layer to an AVI file tended to confuse me when I was doing my own lab because I couldn’t find the buttons. I later realized that it was not necessary. I had trouble confirming layer formats, cell sizes and changes in extent coverage. By working it, I was able to better understand the processes behind the final production and the methods used. I also had trouble with the slope values because the percentage was projected in millions of percent. I had a huge problem trying to convert the projection of the raster using ‘project raster’ because my USB drive ran out of memory. Another thing that I learned is that I need a bigger memory drive since a lot of GIS projects contain a lot of information.
Other useful operations with this kind of spatial analysis could be extended to other places or hazards like floods. Some of the techniques for processing raster data were difficult at times, but many valuable lessons and objectives could be achieved with it. Hazard maps are just one small portion of the potential that raster data manipulation can offer. This can be a useful tool in real life applications and can help save lives by looking at raster data of potential floods that can occur just like the fire map that helps indicate vegetation burn index. The possibilities are vast and I am excited to learn this useful tool in spatial analysis.