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ArcGIS Pro

Modelling Fire Risk in ArcGIS Pro

Modelling Fire Risk in ArcGIS Pro

When wildfires occur, wildlife and their natural habitats are destroyed putting some animal and plant species at risk, even of extinction. Human settlements within affected areas also see losses in property, and risks to their lives and health. Even though a high percentage of wildfires arise from human activities, they can be exacerbated by environmental conditions. High temperatures, low humidity and strong winds are just some of the conditions that intensify wildfires.

According to the UN Environment Programme (UNEP), there is a continuous global increase of extreme fires, with predictions suggesting that by 2030, the risk of fire disasters will have increased by as much as 14%- and 30% by the end of 2050. This increase is mainly attributed to climate change, and changes in land use/land cover. In the same way that climate change intensifies fires, fire hazards can also make climate change worse by destroying vegetation rich ecosystems, resulting in rising temperatures. Proper fire management is therefore critical for preserving wildlife, mitigating climate change, and saving property and lives.

Modelling is a critical part of fire management practices as it helps with understanding the behavior of fire under various conditions. ArcGIS Pro provides capabilities for modelling fire risk, modelling flood risk, as well as other natural hazards. This tutorial gives a step-by-step guide on how to model fire risk in ArcGIS Pro. While there are many factors that affect the severity of fire risk, only the following factors are used for the purposes of demonstration: Slope, Rainfall, Land cover, and Aspect.

Step 1: Data Collection and Preparation

The datasets needed for this project are the DEM, Land use Land cover (LULC), and Rainfall rasters. The DEM can be downloaded from the USGS National Map; the LULC raster from ESRI’s Living Atlas; and the rainfall raster from CHRS data portal.

Once the data sets have been obtained, open ArcGIS Pro and start a new project. Then load the datasets by clicking on the ‘Add Data’ icon as shown below in the Layer group under the Map tab.

Figure 1: Adding data to ArcGIS Pro

The next task is clipping the area of interest. For this, add a shapefile or feature lass that defines your area of interest, and use it to clip the DEM, Rainfall, and LULC datasets, one after the other.

Clipping: In the Geoprocessing pane inside ArcGIS Pro, search “Clip Raster”. Then set the “Input Raster” to your DEM and “Output Extent” to the shapefile that defines your area of interest. Click “Run”. Repeat these steps to clip the LULC and Rainfall datasets as well.

Figure 2: Clipping Rasters in ArcGIS Pro

Step 2: Deriving Slope

Slope is the steepness of a surface. Low slopes are associated with a lower fire risk while steeper slopes have higher fire risk. This is because fires burn faster uphill. Slope can be derived from the DEM by the following procedure.

  1. Search “Slope” in the Geoprocessing pane of ArcGIS Pro
  2. Set “Input Raster” to the DEM you clipped in step 1 above
  3. Under “Output Measurement” choose “Percent rise”
  4. Click “Run”

Figure 3: Deriving slope from DEM

Step 3: Deriving Aspect

Aspect is the direction in which the slope is facing. Aspects that receive direct sunlight have higher temperatures, and higher fire risks than those facing away from the sun.

 To derive aspect from the DEM follow the following procedure:

  1. Search “Aspect” in the Geoprocessing pane and open it
  2. Set “Input Raster” to the same clipped DEM you used in step 2 above
  3. Click “Run”

Figure 4: Deriving Aspect from DEM

Step 4: Reclassification

This step is for defining a uniform classification criteria for the data inputs of your AOI. You can choose to use any number of classes depending on your needs. This tutorial uses 4 classes which are defined as follows:

1 – Low Fire Risk

2 – Moderate Fire Risk

3 – High Fire Risk

4 – Very High Fire Risk

While assigning classes to datasets, make sure the values correspond correctly with how the fire risk relates to the factor under consideration i.e. for rainfall the lowest values are assigned 4 and higher values assigned 1 since low rainfall areas have a higher fire risk, while higher rainfall areas have lower fire risks. Geographical and environmental knowledge should be put into practice while assigning classes to all the other three factors (Aspect, LULC, Slope) as well.

To reclassify, follow this procedure:

  1. Search “Reclassify” in the Geoprocessing pane and open it
  2. Set “Input raster” to the rainfall dataset
  3. Click on “Classify” and specify the number of classes to “4” and click OK
  4. Click on Reverse New Values to capture the right correlation between Rainfall and fire risk (Fire risk reduces with higher rainfall)
  5. Click “Run”

Figure 5: Reclassification

Step 5: Overlaying Weights

This step overlays the weights of the factors under consideration to model the fire risk for each cell. The weights are assigned according to the extent of influence that the factors have for a particular location. Each location will have different conditions and influences, so there are no universal weights for the factors. For this tutorial, the weights were assigned as follows: LULC – 35%, Slope – 25%, Rainfall – 25%, Aspect – 15%.

The weighted overlay tool is used to overlay weights:

  1. Search “Weighted Overlay” in the Geoprocessing pane and pen it
  2. Add all the reclassified rasters from step 4 above to the weighted overlay table
  3. Type in the percentage weight for each factor (the total should add to 100)
  4. Click “Run”

Figure 6: Overlaying weights

The result of the weighted overlay tool is a model that shows the fire risk for the AOI.

In order to make the result presentable and easier to read, right click on it, and open “Symbology”. Choose an appropriate colour scheme and rename the classes as follows: 1 – “Low Fire Risk”, 2 – “Moderate Fire Risk”, and 3 – “High Fire Risk”, and 4 – “Very High Fire Risk”.

Figure 7: Fire Risk Map

About the Author
I'm Daniel O'Donohue, the voice and creator behind The MapScaping Podcast ( A podcast for the geospatial community ). With a professional background as a geospatial specialist, I've spent years harnessing the power of spatial to unravel the complexities of our world, one layer at a time.