Wildfire risk modeling relies on multiple data sources to understand where fires may start, how they could spread, and which areas may face greater exposure. These models combine environmental, geographic, weather, historical, and human activity information to develop a detailed picture of wildfire conditions. Satellite imagery can provide information about vegetation and land cover, while weather records help identify temperature, wind, humidity, and drought conditions. Terrain data contributes information about elevation and slope, which can influence fire behavior. Historical wildfire records also help identify patterns over time.
Weather and climate information is an important component of wildfire risk modeling because atmospheric conditions can influence fuel dryness and fire behavior. Models may use historical observations as well as near real time or forecast information, depending on their purpose.
Common weather and climate variables include:
Weather stations, climate databases, remote sensing systems, and numerical weather models can provide these datasets. Different sources may have different spatial and temporal resolutions, so model developers often process the information before incorporating it into a risk assessment.
Vegetation provides information about the fuels available for wildfire. The type, density, distribution, and condition of vegetation can affect how a fire may develop and spread. Land cover datasets help distinguish forests, grasslands, shrublands, agricultural areas, and developed locations.
Important vegetation related datasets include:
Satellite imagery is frequently used to monitor vegetation conditions across large areas. Changes in vegetation caused by drought, seasonal cycles, land management, or disturbances can also be incorporated into some modeling approaches.
Terrain characteristics can influence wildfire movement and are therefore commonly represented in spatial wildfire models. Geographic information systems and elevation datasets can provide detailed information about the physical landscape.
Common terrain variables include elevation, slope, aspect, and landscape shape. Digital elevation models are often used to derive these characteristics. Geographic datasets can also identify roads, rivers, buildings, administrative boundaries, and other landscape features that may be relevant when interpreting wildfire exposure.
Historical wildfire records provide information about where fires have occurred in the past and how frequently certain areas have experienced fire activity. These records can be used to identify patterns and develop datasets for statistical or machine learning models.
Satellite systems add another important source of information by detecting active fires, burned areas, and changes in land conditions. Depending on the satellite and sensor, imagery can provide observations at different spatial and temporal resolutions. Historical records and satellite observations can complement each other when researchers examine wildfire patterns over large geographic areas.
Human activity can also be relevant to wildfire risk because some fires originate from human activities or infrastructure. Models may therefore incorporate information about population distribution, roads, settlements, power infrastructure, agricultural activity, and land use.
These datasets can help distinguish areas with different levels of human exposure and activity. However, the availability and quality of human activity data can vary between regions, and model developers need to account for differences in data coverage.
Wildfire risk models generally combine these different sources to create spatial or statistical representations of potential risk. No individual dataset can describe every factor affecting wildfire behavior. Weather conditions can change rapidly, vegetation can vary across short distances, and historical fire records may contain gaps. Consequently, understanding the origin, limitations, resolution, and time period of each dataset is important when interpreting model results.