Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Integrating plant physiology into simulation of fire behavior and effects.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Forest fuel type classification: Review of remote sensing techniques, constraints and future trends.

Improved forest management plans require a better understanding of wildfire risk and behavior to enhance the conservation of biodiversity and plan effective risk mitigation activities across the landscape. More particularly, for spatial fire hazard and risk assessing as well as fire intensity and growth modeling across a landscape, an adequate knowledge of the spatial distribution of key forest fuels attributes is required. Mapping fuel attributes is a challenging and complicated procedure because fuels are highly variable and complex. To simplify, classification schemes are used to summarize the large number of fuel attributes (e.g., height, density, continuity, arrangement, size, form, etc.) into fuel types which groups vegetation classes with a similar predicted fire behavior. Remote sensing is a cost-effective and objective technology that have been used to regularly map fuel types and have demonstrated greater success compared to traditional field surveys, especially with recent advancements in remote sensing data acquisition and fusion techniques. Thus, the main goal of this manuscript is to provide a comprehensive review of the recent remote sensing approaches used for fuel type classification. We build on findings from previous review manuscripts and focus on identifying the key challenges of different mapping approaches and the research gaps that still need to be filled in. To improve classification outcomes, more research into developing state-of-the-art deep learning algorithms with integrated remote sensing data sources is encouraged for future research. This review can be used as a guideline for practitioners, researchers, and decision-makers in the domain of fire management service.

Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires.

Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63-76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20-40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Plant-water sensitivity regulates wildfire vulnerability.

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R2 = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990-2010 than in other wildland-urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase.

Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of Mediterranean Europe.

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.

Climate change induced declines in fuel moisture may turn currently fire-free Pyrenean mountain forests into fire-prone ecosystems.

Fuel moisture limits the availability of fuel to wildfires in many forest areas worldwide, but the effects of climate change on moisture constraints remain largely unknown. Here we addressed how climate affects fuel moisture in pine stands from Catalonia, NE Spain, and the potential effects of increasing climate aridity on burned area in the Pyrenees, a mesic mountainous area where fire is currently rare. We first quantified variation in fuel moisture in six sites distributed across an altitudinal gradient where the long-term mean annual temperature and precipitation vary by 6-15 °C and 395-933 mm, respectively. We observed significant spatial variation in live (78-162%) and dead (10-15%) fuel moisture across sites. The pattern of variation was negatively linked (r = |0.6|-|0.9|) to increases in vapor pressure deficit (VPD) and in the Aridity Index. Using seasonal fire records over 2006-2020, we observed that summer burned area in the Mediterranean forests of Northeast Spain and Southern France was strongly dependent on VPD (r = 0.93), the major driver (and predictor) of dead fuel moisture content (DFMC) at our sites. Based on the difference between VPD thresholds associated with large wildfire seasons in the Mediterranean (3.6 kPa) and the maximum VPD observed in surrounding Pyrenean mountains (3.1 kPa), we quantified the "safety margin" for Pyrenean forests (difference between actual VPD and that associated with large wildfires) at 0.5 kPa. The effects of live fuel moisture content (LFMC) on burned area were not significant under current conditions, a situation that may change with projected increases in climate aridity. Overall, our results indicate that DFMC in currently fire-free areas in Europe, like the Pyrenees, with vast amounts of fuel in many forest stands, may reach critical dryness thresholds beyond the safety margin and experience large wildfires after only mild increases in VPD, although LFMC can modulate the response.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.