Other data types, such as vegetation/fuel can be integrated with elevation data to dynamically display areas with dense and highly flammable vegetation.

Vegetation/Fuel types

Next, multiple operations are performed utilizing the Visual Basic for Application (VBA) to create a fire behavior index. The analyst creates a module that defines dangerous topographic features, such as ridgelines, using the hydrological modeling functions. The module will run through a series of classifications and mathematical overlays to define the fire behavior index. Combining elevation, dangerous topographic features, slope, aspect, and fuel types into one raster data set will accurately classify the danger of wildfire hazards in the area.

Using the highly customizable VBA environment, a script is implemented through a user interface control that reclassifies elevation, dangerous topographic features, slope, aspect, and fuel types. They are then combined into one fire behavior index for displaying the areas that are most susceptible to fire damage.

Occurrences

ArcGIS Geostatistical Analyst is used to create a probability surface of the likelihood of lightning strikes to further analyze areas that have the highest risk to wildfires. Before creating a probability surface for the occurrence of lightning strikes, an analyst utilizes a variety of Exploratory Spatial Data Analysis (ESDA) tools in ArcGIS Geostatistical Analyst to effectively identify trends and outliers in the data that may play a role in the final surface created. These trends and outliers must be accounted for to create a statistically sound surface. In this case there is a noticeable trend of higher magnitude events when going from the west to the eastern plains.

Using a variety of ESDA tools, such as trend analysis, the analyst identifies a trend of higher magnitude events when going from the west to the eastern plains.

After identifying the easterly trend and thoroughly exploring the data, the analyst uses the geostatistical wizard to create an optimal surface for this phenomenon. A variety of reliable defaults are included in the wizard. However, the adjustment of these options in accordance with the known phenomenon is recommended to ensure that the most optimal surface is created.

For this phenomenon, the probability kriging method is used to create a probability map of positive polarity events. Positive polarity events are most likely to ignite a fire. This probability map statistically determines the likelihood that any given area will experience these positive lightning strikes under similar weather conditions. The anisotropy option is selected to account for the unknown easterly trend in the data. The lag size, number of lags, major range, minor range, direction, partial sill, and nugget are also adjusted for this specific phenomenon to create an optimal surface. Finally, the geostatistical layer created in ArcGIS Geostatistical Analyst is converted to a raster for analysis with the fire behavior index.

Using the geostatistical wizard, the analyst creates an optimal probability map of positive polarity lightning strikes in Boulder County.

Community Risk Assessment

After the probability surface was created, a final risk assessment map was generated showing the areas of wildfire concern for Boulder County. To do this, the analyst utilizes a custom command in VBA to combine values, hazards, and occurrences. Alternatively, the raster calculator in ArcGIS Spatial Analyst could have been used to combine these grids.

Community risk assessment map of Boulder County considers variables such as elevation, high ridges, slope, aspect, building density, vegetation/fuel types, and lightning strikes.

In this study the analyst uses the raster geoprocessing tools from ArcGIS Spatial Analyst and the analytical tools in ArcGIS Geostatistical Analyst to perform a community risk assessment. By analyzing the behavior fire index grid and combining this effort with building density and a lightning strike probability map, the analyst is able to effectively visualize areas with the highest likelihood of fire by creating a final community risk assessment map. These same techniques can be applied to suitability models, environmental models, and many other analytical tasks.

ArcGIS 3D Analyst

ArcGIS 3D Analyst is used to visualize the community risk assessment map in a real-world environment. To do this, the base height for the risk assessment map is adjusted to be the same as the elevation to gain a real-world perspective of the phenomenon.

Three-dimensional view of the fire risk assessment grid created from ArcGIS Spatial Analyst and ArcGIS Geostatistical Analyst based on the Z values of the elevation grid

ArcGIS Spatial Analyst, ArcGIS Geostatistical Analyst, and ArcGIS 3D Analyst Working Together

ArcGIS Spatial Analyst, ArcGIS Geostatistical Analyst, and ArcGIS 3D Analyst all have unique functionality within each extension. However, when these extensions work together, it is possible to take GIS analysis to an entirely new level. In the previous examples

• ArcGIS Spatial Analyst is used to create a reclassified grid for a fire behavior index.
• ArcGIS Geostatistical Analyst is used to create a probability map of areas that are most susceptible to fires because of lightning strikes.
• ArcGIS Spatial Analyst is used to combine building density, the fire behavior index map, and the lightning strike probability map for a final risk assessment map.
• This map is three-dimensionally displayed in ArcGIS 3D Analyst to ultimately visualize the data with a real-world perspective based on elevation.

With this new perspective the user can dynamically explore the data and gain new insights about areas with a high likelihood of fire damage. By working with all three extensions within one geospatial environment an analyst can gain a much better understanding of the range of factors that play a role in wildfires within Boulder County.

The data is provided by the Boulder County Land Use Department, the Wildfire Interface Group, and Global Atmospherics.