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This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.

The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of automatic wildfire detection methods. Approaches based on long-distance imagery from satellites or watchtowers encounter limitations, such as restricted visibility, which results in delayed response times. To address and overcome these challenges, research interest has grown in the implementation of ground-based Internet of Things (IoT) sensing systems for early wildfire detection. However, research on energy consumption, detection latency, and detection accuracy of IoT sensing systems, as well as the performance of various anomaly detection algorithms when evaluated using these metrics, is lacking. Therefore, in this article, we present an overview of current IoT ground sensing systems for early wildfire detection. Camera and environmental sensing technologies suitable for early wildfire detection are discussed, as well as vision-based detection algorithms and detection algorithms for environmental sensing. Challenges related to the development and implementation of IoT ground sensing systems for early wildfire detection and the future research directions important for creating a robust detection system to combat the growing threat of wildfires worldwide are discussed.

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable’s scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities.

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumption

Wildfires are becoming an increasing threat to many communities worldwide. There has been substantial progress towards understanding the proximal causes of increased fire activity in recent years at regional and national scales. However, subcontinental scale examinations of the commonalities and differences in the drivers of fire activity across different regions are rare in the Mediterranean zone of the European Union (EUMed). Here, we first develop a new classification of EUMed pyroregions, based on grouping different ecoregions with similar seasonal patterns of burned area. We then examine the thresholds associated with fire activity in response to different drivers related to fuel moisture, surface meteorology and atmospheric stability. We document an overarching role for variation in dead fuel moisture content (FMd), or its atmospheric proxy of vapor pressure deficit (VPD), as the major driver of fire activity. A proxy for live fuel moisture content (EVI), wind speed (WS) and the Continuous Haines Index (CH) played secondary, albeit important, roles. There were minor differences in the actual threshold values of FMd (10–12%), EVI (0.29–0.36) and CH (4.9–5.5) associated with the onset of fire activity across pyroregions with peak fire seasons in summer and fall, despite very marked differences in mean annual burned area and fire size range. The average size of fire events increased with the number of drivers exceeding critical thresholds and reaching increasingly extreme values of a driver led to disproportionate increases in the likelihood of a fire becoming a large fire. For instance, the percentage of fires >500 ha increased from 2% to 25% as FMd changed from the wettest to the driest quantile. Our study is among the first to jointly address the roles of fuel moisture, surface meteorology and atmospheric stability on fire activity in EUMed and provides novel insights on the interactions across fire activity triggers., Peer Reviewed, Postprint (author's final draft)

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study pre- sents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi- source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE =34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE =32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property.