Assessing Wildfire Exposure to Communities and Protected Areas in Portugal

During the 2017 wildfire season in Portugal, unprecedented episodes burned 6% of the country’s area and underscored the need for a long-term comprehensive solution to mitigate future wildfire disasters. In this study, we built and calibrated a national-scale fire simulation system including the underlying fuels and weather data and used the system to quantify wildfire exposure to communities and natural areas. We simulated 10,000 fire season replicates under extreme weather to generate 1.6 million large wildfire perimeters and estimate annual burn probability and fire intensity at 100 m pixel resolution. These outputs were used to estimate wildfire exposure to buildings and natural areas. The results showed a fire exposure of 10,394 structures per year and that 30% of communities accounted for 82% of the total. The predicted burned area in natural sites was 18,257 ha yr−1, of which 9.8% was protected land where fuel management is not permitted. The main burn probability hotspots were in central and northern regions. We highlighted vital priorities to safeguard the most vulnerable communities and promote landscape management programs at the national level. The results can be useful to inform Portugal’s new national plan under implementation, where decision-making is based on a probabilistic methodology. The core strategies include protecting people and infrastructure and wildfire management. Finally, we discuss the next steps necessary to improve and operationalize the framework developed here. The wildfire simulation modeling approach presented in this study is extensible to other fire-prone Mediterranean regions where predicting catastrophic fires can help anticipate future disasters.

Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

Assessing the wildfire activity in protected areas of the Amazon basin

<p>Despite  growing concerns regarding the Amazonian wildfires, the magnitude of the problem is poorly understood. In this study, we assessed the wildfire activity in the  protected natural sites (n= 428) of Bolivia, Brazil, Colombia, Ecuador, French Guyana, Guyana, Peru, Suriname, and Venezuela, encompassing an area of 1.4 million km<sup>2 </sup>of the Amazon basin. A 250 m resolution spectroradiometer sensor imaging (MODIS) was used to obtain land-use/land-cover (MODIS land use land cover product) changes and derive the wildfire activity data (ignition locations and burned areas (MODIS active fire products)) from 2001 to 2018. First, we characterized the mean fire return interval, wildfire occurrence, and empiric burn probability. Then, we implemented a transmission analysis to assess the burned area from incoming fires. We used transmission analysis to characterize the land use and anthropic activities associated to fire ignition locations across the different countries. On average, 867 km <sup>2</sup> of natural forests were burned in protected natural sites annually, and about 85 incoming fires per year from neighboring areas accounted for 10.5% (9,128 ha) of the burned area. The most affected countries were Brazil (53%), Bolivia (24%), and Venezuela (16%).Considerable amount of fire ignition points were detected in open savannas (29%) and grasslands (41%) , where the fire is periodically used to clear extensive grazing properties. The incoming fires from savannas were responsible for burning the largest forest areas within protected sites, affecting as much as 9,800 ha in a single fire event. In conclusion, we  discuss the potential implications of the main socioeconomic factors and environmental policies that could explain increasing trends of burned areas. Wildfire risk mitigation strategies include the fire ignition prevention in developed areas, fire use regulation in rural communities, increased fuels management efforts in the buffer areas surrounding natural sites, and the early detection system that may facilitate a rapid and effective fire control response. Our analysis and quantitative outcomes describing the fire activity represent a sound science-based approach for an well defined wildfire management within the protected areas of the Amazonian basin.</p>

Model to Predict Probability of Significant Skin Burn Injury From a Directed-Energy Source

ABSTRACT Introduction Millimeter wave directed energy in the frequency regime of 94-95 GHz has potential for use in numerous military applications including crowd control and area denial. Although proven to be very safe, millimeter wave energy has the potential, because of accidental over exposure, to produce significant injuries. Currently, the Dynamic Thermal Model (DTM), developed by Beason and colleagues, is used to calculate the temperature profile in skin undergoing (millimeter wave) heating, using an all-or-nothing threshold of injury. Risk of significant injury (RSI) is determined by product of the probability of an injury outcome on a region of the body times the probability of that the injury will occur. Thus, a threshold injury determination may over predict burn probability and fail safety requirements. This work augments the DTM, replacing the current threshold value of injury with a probabilistic risk of injury to better quantify the risk of significant injury. Materials and Methods In this study, continuous probabilistic dose–response models using logistic regression analysis have been developed to account for mild second-degree, deep second-degree, and third-degree burn injuries based on a historic experimental burn dataset. Statistical analysis methods such as Hosmer–Lemeshow statistics, McFadden’s pseudo R2 and receiver operator characteristic were used to validate the models against an independent experimental burn dataset. Results Comparison of logistic models fit using damage coefficients from the literature showed that Mehta and Wong provided the best fits historic burn data, which was corroborated by the McFadden pseudo R2 statistic for mild second-degree, deep second-degree, and third-degree severity. Conclusion The dose–response models developed in this study are shown to be an excellent predictor of burn injury for each severity. The DTM was repackaged with the probabilistic burn models to more accurately determine the risk of significant burn injury.

Evaluating fire management effectiveness with a burn probability model in Daxing’anling, China

Fire is an important disturbance agent in the boreal forests of China. The aggressive fire suppression policy of China since 1988 has resulted in a large financial investment in support of fire brigade capabilities and the maintenance of fire management infrastructure. We developed a spatially explicit burn probability (BP) model to evaluate the effectiveness of improved fire management in Daxing’anling, China. The BP model can emulate the burn probability of the forest landscape by simulating daily wildfire occurrences, spread, and suppression for simulated years. Two scenarios were used for fire simulations in this study. The base scenario used the infrastructure data and parameters of fire suppression capability from the 1968–1987 period, and the intensive scenario used the data and parameters from the 1988–2012 period. The simulated annual burned areas for 1968–2012 showed a fluctuating trend similar to the historical fire records. Compared with the base scenario, the burn probability decreased by 73.6% under the intensive scenario, which suggests that improved fire management could significantly reduce the burn probability. This study shows that the BP model can model the effects of fire management activities on the forest landscape level and evaluate the effectiveness of fire management strategies or management measures.

Assembling and Customizing Multiple Fire Weather Forecasts for Burn Probability and Other Fire Management Applications in Ontario, Canada

Weather forecasts are needed in fire management to support risk-based decision-making that considers both the probability of an outcome and its potential impact. These decisions are complicated by the large amount of uncertainty surrounding many aspects of the decision, such as weather forecasts. Wildland fires in Ontario, Canada can burn and actively spread for days, weeks, or even months, or be naturally limited or extinguished by rain. Conventional fire weather forecasts have typically been a single scenario for a period of one to five days. These forecasts have two limitations: they are not long enough to inform some fire management decisions, and they do not convey any uncertainty to inform risk-based decision-making. We present an overview of a method for the assembly and customization of forecasts that (1) combines short-, medium-, and long-term forecasts of different types, (2) calculates Fire Weather Indices and Fire Behaviour Predictions, including modelling seasonal weather station start-up and shutdown, (3) resolves differing spatial resolutions, and (4) communicates forecasts. It is used for burn probability modelling and other fire management applications.