Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

[EN], The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.SIPortuguese Foundation for Science and Technolog

Hydrology and Fire History Drive Patterns in Post-Fire Recovery in Everglades Wetland Ecosystem

Although fire-adapted ecosystems in Everglades require regular burning to maintain wetland ecosystems, land management and climate-change have altered natural fire-regime. Due to changes in climate and hydrology, historical fire-regimes may become irrelevant. To understand changing fire return intervals, I look at patterns in ecosystem recovery, where fast recovery is indicative of resilience and adaption with an objective of understanding post-fire recovery time in Everglades. I evaluated how post-fire recovery rates were influenced by hydrology and fire-history (1948-2019) by measuring changes in normalized difference vegetation index following fires that burned between 2005-2019 within Everglades. Hydrology had stronger effect on post-fire recovery compared to fire history. Increasing water-levels by 10% across Everglades either shortened (sawgrass marl prairie) or prolonged (cattail marsh, graminoid marsh, graminoid prairie, halophytic herbaceous prairie and sawgrass marsh) post-fire recovery estimates. Fire return intervals for Everglades were dynamic and fire-management must develop novel approaches to manage fire-regimes

Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: a case study on a large fire in NW Spain

P. 512-520This study analyses the suitability of remote sensing data from different sources (Landsat 7 ETM+, MODIS and Meteosat) in evaluating the effect of fuel conditions on fire severity, using a megafire (11 891 ha) that occurred in a Mediterranean pine forest ecosystem (NW Spain) between 19 and 22 August 2012. Fire severity was measured via the delta Normalized Burn Ratio index. Fuel conditions were evaluated through biophysical variables of: (i) the Visible Atmospherically Resistant Index and mean actual evapotranspiration, as proxies of potential live fuel amount; and (ii) Land Surface Temperature and water deficit, as proxies of fuel moisture content. Relationships between fuel conditions and fire severity were evaluated using Random Forest models. Biophysical variables explained 40% of the variance. The Visible Atmospherically Resistant Index was the most important predictor, being positively associated with fire severity. Evapotranspiration also positively influenced severity, although its importance was conditioned by the data source. Live fuel amount, rather than fuel moisture content, primarily affected fire severity. Nevertheless, an increase in water deficit and land surface temperature was generally associated with greater fire severity. This study highlights that fuel conditions largely determine fire severity, providing useful information for defining pre-fire actions aimed at reducing fire effects

Efficiency of remote sensing tools for post-fire management along a climatic gradient

P. 553-562Forest managers require reliable tools to evaluate post-fire recovery across different geographic/climatic contexts and define management actions at the landscape scale, which might be highly resource-consuming in terms of data collection. In this sense, remote sensing techniques allow for gathering environmental data over large areas with low collection effort. We aim to assess the applicability of remote sensing tools in post-fire management within and across three mega-fires that occurred in pine fire-prone ecosystems located along an Atlantic-Transition-Mediterranean climatic gradient. Four years after the wildfires, we established 120 2x2m plots in each mega-fire site, where we evaluated: (1) density of pine seedlings, (2) percentage of woody species cover and (3) percentage of dead plant material cover. These variables were modeled following a Bayesian Model Averaging approach on the basis of spectral indices and texture features derived from WorldView-2 satellite imagery at 2 m spatial resolution. We assessed model interpolation and transferability within each mega-fire, as well as model extrapolation between mega-fires along the climatic gradient. Texture features were the predictors that contributed most in all cases. The woody species cover model had the best performance regarding spatial interpolation and transferability within the three study sites, with predictive errors lower than 25% for the two approaches. Model extrapolation between the Transition and Mediterranean sites had low levels of error (from 6% to 19%) for the three field variables, because the landscape in these areas is similar in structure and function and, therefore, in spectral characteristics. However, model extrapolation from the Atlantic site achieved the weakest results (error higher than 30%), due to the large ecological differences between this particular site and the others. This study demonstrates the potential of fine-grained satellite imagery for land managers to conduct post-fire recovery studies with a high degree of generality across different geographic/climatic contexts.S

Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems

P. 24-32The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.S

Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests

Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994?2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally.Ministerio de Ciencia, Innovación y UniversidadesMinisterio de Economía y Competitivida

The footprint of large wildfires on the multifunctionality of fire-prone pine ecosystems is driven by the interaction of fire regime attributes

[EN], Background Mediterranean ecosystems dominated by Pinus pinaster Ait. (maritime pine) are subject to a shift from fuel-limited to drought-driven fire regimes, characterized by an increasing wildfire extent, recurrence, and severity. Previous studies have not addressed the interacting effects of fire recurrence and severity on the ecosystem multifunctionality (EMF) of maritime pine forests, although complex relationships between such fire regime attributes are expected. Here, we evaluated the medium-term effects of fire recurrence and severity on the EMF response of unmanaged, native pine ecosystems dominated by Pinus pinaster in the western Mediterranean Basin. We considered four key ecosystem functions computed from functional indicators (carbon regulation, decomposition, soil fertility, and plant production), which were pooled into an EMF construct. The fire regime effects on the trade-offs and synergies between the considered ecosystem functions were also analyzed. Results Multiple ecosystem functions responded differentially to fire recurrence and severity. Fire recurrence had a strong effect on soil fertility, decomposition, and plant production functions. No significant effects of fire severity on any of the individual functions were detected. However, both fire regime attributes interacted to determine soil fertility and decomposition functions, suggesting that their performance is only impaired by fire severity when fire recurrence is low. The differing responses to the fire regime attributes among ecosystem functions fostered a significant EMF response to fire severity and its interaction with fire recurrence, indicating that the effect of fire severity on EMF was stronger under low fire recurrence scenarios, even when relationships between individual functions and fire severity were weak. Fire recurrence caused significant trade-offs between functions to emerge. However, these trade-offs were not strong enough to differ significantly from the intrinsic trade-offs (i.e., regardless of the fire regime) of maritime pine ecosystems. Conclusions Our results indicated the need to use an integrative approach to assess the response of ecosystem functioning to the fire regime in maritime pine ecosystems. Adaptive management responses are necessary towards the minimization of repeated burnings and the reduction of the fuel load in unmanaged maritime pine stands of the western Mediterranean Basin with similar characteristics to those analyzed in this study.[ES], Antecedentes Los ecosistemas mediterráneos dominados por pino marítimo (Pinus pinaster Ait.) están sujetos a cambios en regímenes de fuego limitados por el combustible hacia regímenes conducidos por la sequía, y caracterizados por un incremento en la extensión, recurrencia y severidad de los incendios. Estudios previos no han abordado los efectos interactivos de la recurrencia y severidad del fuego en la multifuncionalidad de los ecosistemas (EMF) en bosques de pino marítimo, aunque cabe esperar relaciones complejas entre estos atributos del regimen de fuego. En este trabajo, evaluamos los efectos a medio plazo de la recurrencia y severidad en la respuesta de la multifuncionalidad de los ecosistemas (EMF) de bosques nativos dominados por pino marítimo no gestionados en la cuenca Mediterránea occidental. Consideramos cuatro funciones clave calculadas a partir de indicadores funcionales (regulación del carbono, descomposición, fertilidad del suelo, y producción egetal) los cuales fueron agrupados en un constructo EMF. Los efectos del régimen de fuego sobre las sinergias y contrapartidas entre las funciones ecosistémicas también fueron analizados. Resultados Múltiples funciones ecosistémicas respondieron diferencialmente a la recurrencia y severidad. La recurrencia del fuego tuvo un efecto muy fuerte en la fertilidad del suelo, en la descomposición y en las funciones de producción. Ningún efecto significativo de la severidad del fuego fue detectado en ninguna de las funciones individuales. Sin embargo, los atributos de ambos regímenes de fuego interactuaron para determinar las funciones de fertilidad y descomposición, sugiriendo que su rendimiento es afectado por la severidad solo cuando la recurrencia del fuego es baja. Las diferentes respuestas a los atributos de los regímenes de fuego entre las funciones ecosistémicas promueven una respuesta significativa de la EMF a la severidad del fuego y su interacción con la recurrencia, indicando que el efecto de la severidad sobre la EMF fue más fuerte bajo escenarios de baja recurrencia, aun cuando las relaciones entre funciones individuales y la severidad fueran débiles. La recurrencia del fuego causó la aparición de ontrapartidas significativas entre funciones. Obviamente, estas contrapartidas no fueron lo suficientemente fuertes para diferir significativamente de aquellas intrínsecas (i.e., independientemente del régimen de fuego) en los ecosistemas de pino marítimo. Conclusiones Nuestros resultados indican la necesidad de usar una aproximación integrada para determinar la respuesta del funcionamiento al régimen de fuego en ecosistemas de pino marítimo. Respuestas de manejo adaptativo son necesarias para la minimización de quemas repetidas y la reducción de la carga de combustible en rodales de pino marítimo no gestionados en la cuenca Mediterránea, con características similares a aquellos analizados en este estudio.SIAEIBritish Ecological SocietyPortuguese Foundation for Science and Technolog

Local forest structure variability increases resilience to wildfire in dry western U.S. coniferous forests.

A 'resilient' forest endures disturbance and is likely to persist. Resilience to wildfire may arise from feedback between fire behaviour and forest structure in dry forest systems. Frequent fire creates fine-scale variability in forest structure, which may then interrupt fuel continuity and prevent future fires from killing overstorey trees. Testing the generality and scale of this phenomenon is challenging for vast, long-lived forest ecosystems. We quantify forest structural variability and fire severity across >30 years and >1000 wildfires in California's Sierra Nevada. We find that greater variability in forest structure increases resilience by reducing rates of fire-induced tree mortality and that the scale of this effect is local, manifesting at the smallest spatial extent of forest structure tested (90 × 90 m). Resilience of these forests is likely compromised by structural homogenisation from a century of fire suppression, but could be restored with management that increases forest structural variability