Forecasting not only the likelihood but also intensity of fire is important for both industry, safety, and better understanding climate change. In this regard, satellite systems are vital for observation of developing fires, where they can be used to monitor conditions which can then feed that information to forecast the likelihood of fires using developed models.
Recently, NASA has developed the Global Fire Weather Database (GFWED), which provides wind, temperature, and humidity data that can then be used with GIS or remote sensing software to forecast where fires could begin. It also used satellite-based precipitation measurements made in near real-time that are applied to forecast the probability that a fire could begin in a given area. Effectively, the model provides a type of fire score that shows areas where fires are more likely where the model uses past patterns of fire events. For example, it is not just how dry a place is but also the fact that wind speeds may have picked up that could affect one given area. Combing the different factors that lead to fires is how the tool provides a better regional understanding of fire occurrence.
Global Map of Fire Weather Index
Using Machine Learning to Predict and Map Likelihood of Fires
Other techniques have used machine learning methods such as random forest and genetic algorithm techniques that look at different landscape and land use variables to estimate likelihood of fires using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this case, topography and land cover were seen as among the most important factors. The best assessment was nearly 85% accurate in forecasting the likelihood of fire in China.
There are, however, other approaches that have been developed that utilize both human and natural factors in assessing fire risk. For instance, using MODIS data, a learning algorithm using expectation-maximum methods was used to evaluate a series of inputs from different regions. Bayesian networks and GIS were used to evaluate regional factors of different inputs influencing fires from historical data. This was applied in Swaziland (now known as eSwatini) and it was found that both land tenure and land cover conditions were shown to be major reasons for fire, where the model was able to have accuracy of over 93% in forecasting fires. The satellite data can be used to train the model which can then use the previous data to forecast future conditions that might be similar to past occurrences.
Using Landsat Data with Fire Modeling
Other research has applied Landsat data, as these provide current and even long-term dataset for which forecasting models can be build. Using burn history with fire events, data on slope, aspect, and weather conditions can be applied with Landsat vegetation data to create forecasting models that determine the likelihood of fire. While current imagery does provide better resolution and better band coverage, older data could also be useful for creating long-term models or models with deeper datasets that can potentially better predict variations in fires that may differ from more recent events.
Understanding Human Factors and Using Remote Sensing and GIS for Fire Risk
Other methods have tried to combine GIS, remote sensing and interviews with local experts to better understand fires. In these cases, it has been observed in some regions, such as the Mediterranean area, a high percentage of fires are started by human factors. Understanding the right conditions from earth observation techniques and GIS could be improved by also knowing how these relate to human factors, such as reasons why deliberate actions have been done to lead to large-scale fires. This helps to produce an accurate understanding of human-environment risk factors in fire occurrence.
Earth observation techniques have been used to forecast fires since at least the 1960s and the early days of satellite-based remote sensing. What is different now is that models at global and regional scale are being developed that have been increasing in accuracy. With climate change now becoming a real risk for fires, new techniques are also incorporating land-based data along with temperature, precipitation and other weather factors in developing more accurate models.
 For more on fire forecasting using NASA’s GFWED, see: https://earthobservatory.nasa.gov/images/92367/forecasting-fire.
 For more on the use of machine learning techniques and fire forecasting using satellite data, see: Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.-X., & Xu, C. (2018). Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of The Total Environment,630, 1044–1056. https://doi.org/10.1016/j.scitotenv.2018.02.278
 For more on using MODIS and learning algorithms for forecasting fires, see: Dlamini, W. M. (2011). Application of Bayesian networks for fire risk mapping using GIS and remote sensing data. GeoJournal,76(3), 283–296. https://doi.org/10.1007/s10708-010-9362-x.
 For more on Landsat data in fire models, see: Milne, A. K. (1986). The use of remote sensing in mapping and monitoring vegetational change associated with bushfire events in Eastern Australia. Geocarto International, 1(1), 25–32. https://doi.org/10.1080/10106048609354022.
 For more on understanding human factors and using remote sensing and GIS for fire risk understanding, see: Leone, V., Lovreglio, R., Martín, M. P., Martínez, J., & Vilar, L. (2009). Human Factors of Fire Occurrence in the Mediterranean. In E. Chuvieco (Ed.), Earth Observation of Wildland Fires in Mediterranean Ecosystems(pp. 149–170). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-01754-4_11.