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Using Fuzzy Logic to Determine the Likely Locations of Bald Eagle Nesting Sites
Problem:
Biologists track and count the numbers of bald eagles in projects where the species has been reintroduced. Finding their nesting sites in forested rough terrain isn't always easy but knowing their behaviors makes it possible to find potential locations.
Analysis Procedures
In conducting a suitability site for a new business an analyst can use weighted suitability or even binary suitability because there are discrete boundaries involved. However, with many natural occurrences such as animal behavior, plant growth, impacts from weather events and other natural phenomena, there aren't threshold values to work with. Instead an analyst is dealing with variety of preferable conditions that are on a spectrum of values. Using raster imagery in fuzzy logic suitabilty studies provides locations where events are most likely to occur. In this analysis, I used ArcGIS Pro and raster imagery to determine the best locations to look for bald eagle nests in Big Bear Lake, California.
The first step in this process was to make raster images from the Human Disturbance and Lake polygon data. The land cover data was reclassified into tree cover data on a scale of 1 to 9.
The resulting images of reclassified land cover data, human disturbance and distance to water rasters were each run through the Fuzzy Membership tool to create individual rasters. The human disturbance raster and the water distance raster were combined using the Fuzzy Overlay tool. This resulted in a raster that was put back into the Fuzzy Overlay tool with the Fuzzy Tree Raster. The tool assigns values from zero to one for each pixel in the output image based upon the values of the input image. The highest value pixel(s) in this resulting raster was 0.777314. A red-yellow-green color scheme was applied to the image with green representing the highest value pixels or the locations where bald eagles are most likely to nest.
Results
Click images for a larger version
If Bald Eagles are nesting in the Big Bear Lake area they are most likely to be found in the green areas that ranked as high as 0.78 on a scale of 0 to 1.
Application and Reflection
Problem description:
Another big problem in California is the seasonal wildfires that are often followed by winter rains causing mudslides. Both events have a variety of conditions that trigger them to occur. Fuzzy logic analysis of data could help to predict these events.
Data needed:
Focusing on the wildfire problem, the two most important data sets would be satellite data to determine vegetation cover (potential fuel for fires). Secondly, rainfall data for as many locations as possible. Other supporting data that would help would be slope data to predict how the Santa Ana winds that often dry vegetation would move over the mountains and down canyons. Fire is also often driven by winds and tends to burn upward on slopes so this data might be beneficial after fires have started. Soil moisture data would also beneficial to analysis if available.
Analysis procedures:
The satellite data would first have to be reclassified into 3 categories of health based upon their IR signature. Higher values indicating healthier plants would be assigned the MS Large membership type so any plants over average health would receive a value over 0.5. The rainfall data would be converted from point data to a raster of values before being assigned fuzzy membership. In fuzzy membership this rainfall raster would be assigned values using the large values membership type to set a threshold minimum rainfall amount with values of zero. Any wind data would be point data and would need to be converted to a raster. A slope raster would be calculated from elevation data. Once fuzzy membership was completed on each of the raster images these tools would be merged in the the fuzzy overlay tool.
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