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Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components. [ABSTRACT FROM AUTHOR]

Although natural disturbances such as wildfire, extreme weather events, and insect outbreaks play a key role in structuring ecosystems and watersheds worldwide, climate change has intensified many disturbance regimes, which can have compounding negative effects on ecosystem processes and services. Recent studies have highlighted the need to understand whether wildfire increases or decreases after large‐scale beetle outbreaks. However, observational studies have produced mixed results. To address this, we applied a coupled ecohydrologic‐fire regime‐beetle effects model (RHESSys‐WMFire‐Beetle) in a semiarid watershed in the western US. We found that in the red phase (0–5 years post‐outbreak), surface fire extent, burn probability, and surface and crown fire severity all decreased. In the gray phase (6–15 years post‐outbreak), both surface fire extent and surface and crown fire severity increased with increasing mortality. However, fire probability reached a plateau during high mortality levels (>50% in terms of carbon removed). In the old phase (one to several decades post‐outbreak), fire extent and severity still increased in all mortality levels. However, fire probability increased during low to medium mortality (≤50%) but decreased during high mortality levels (>50%). Wildfire responses also depended on the fire regime. In fuel‐limited locations, fire probability increased with increasing fuel loads, whereas in fuel‐abundant (flammability‐limited) systems, fire probability decreased due to decreases in fuel aridity from reduced plant water demand. This modeling framework can improve our understanding of the mechanisms driving wildfire responses and aid managers in predicting when and where fire hazards will increase. Plain Language Summary: Bark beetle outbreaks have impacted millions of hectares of forest in western North America. Beetle‐caused tree mortality can increase or decrease wildfire hazards by altering surface fuel loading and decreasing leaf moisture. Previous studies have observed increases in fire following beetle attacks. However, others have found no change or a decrease. Such discrepancies can result from several interacting factors, such as how much time has passed since an outbreak, the level of tree mortality, and pre‐outbreak fuel conditions. To examine how these factors influence surface and crown fire characteristics in a semiarid watershed, we used a model that simulates interactions among hydrology, vegetation, beetle effects, and fire. We found that in the first 5 years after attack, surface fire probability and extent decreased due to decreases in plant productivity and fuel loading. Surface and crown fire severity had similar responses as surface fire extent. Following that, fire responses were a function of two counteracting forces: increases in fuel loading from delayed needle‐ and snag‐fall and wetter fuels from reduced plant water demand. The dominant force depended on fuel conditions. In fuel‐limited locations, fire increased with more fuel loads, whereas in fuel‐abundant locations, fire normally decreased due to wetter fuels. This research provides a practical tool for managers to better predict when and where fire hazards will increase. Key Points: Five years after beetle outbreak, fire probability decreased due to reduced vegetation productivity and fuel loadingSix to fifteen years after outbreak, fire probability increased due to more fuel loading from snag‐fallFifteen years after outbreak, fire probability decreased due to lower fuel aridity when mortality level was higher than 50% [ABSTRACT FROM AUTHOR]

Context: Treeless areas in forested landscapes are an enduring ecological puzzle globally. A fire-mediated alternative stable state (FMASS) model has been proposed to explain mosaics of forest and sedgeland in Tasmania, whereby shifts in fire frequency change vegetation and soil patterns. This model is controversial, and a key step in proving it demands elucidating post-fire vegetation dynamics at landscape-scales and over historical timeframes. Objectives: To quantify historical changes in extent of a large sedgeland patch surrounded by tall Eucalyptus forest and evaluate post-fire recovery after a large wildfire burned the area in 2019. Methods: We mapped historical vegetation changes by analysing sequences of aerial photography from 1947 to 2010, and in 2020 used UAS LiDAR to geolocate the current forest boundary. Satellite imagery, field measures of char: canopy height ratio, and stem mortality were used to gauge fire severity, while vegetative and seedling regeneration were used to assess post-fire recovery. Results: Forest-sedgeland boundaries were stable over the 73-year period. Sedgeland and scrub communities experienced high severity fires, reflecting their low stature compared to taller forest, which suffered minimal canopy impact. The dominant sedge and tree species in the three communities were post-fire resprouters, resulting in rapid recovery of vegetation structure, with prolific seeding of species killed by the fire. Conclusion: Forest-sedgeland boundaries in western Tasmania are resilient to fire because of keystone resprouter species, and geographically stable in historical time frames, a finding inconsistent with the FMASS model. Our study contributes to global question of treeless area in climate zones suitable for forest. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high‐intensity wildfires. Satellite‐derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite‐inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per‐pixel basis within the cloud‐computing environment of Google Earth Engine. It constructs the post‐fire image from satellite data composited from all valid images (i.e., clear‐sky and snow‐free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired‐scene and composite approaches where the post‐fire time period is from the growing season 1 year after fire. Validation statistics based on field‐derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost‐effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.

The projected rise in fire activity due to climate change challenges forest conservation efforts worldwide. Current non-intervention approaches, which rely on natural processes for ecosystem conservation, often overlook palaeoecological data depicting long-term, local interactions between fire regime components, forest structure and composition, soil, aquatic environment and anthropogenic disturbance. To explore whether palaeoecological information supports non-intervention strategies in the Picea abies – Fagus sylvatica – Abies alba closed temperate mountain forest under future fire risks, we conducted a multi-proxy, retrospective analysis (charcoal morphology, pollen, plant macro-remains, sedimentology, palaeoclimate models) on a Holocene lacustrine archive located in the Bavarian Forest National Park, Germany. Results show that climate shifts directly induced vegetation reorganizations and fire regime changes around 11.1, 9.3 and 7–6 ka BP, and gradually allowed the expansion of closed mixed forests dominated by the late successional Picea abies – Fagus sylvatica - Abies alba under a remarkably stable, low-fire environment. We emphasize the possible role of shade-related traits of these species as key bottom-up drivers of biomass burning over the past 6000 years likely due to their effects on microclimate, offsetting the species-specific fire traits. Although these forests tolerate low-severity surface fire, more subtle impacts were identified at the level of soil cover and lacustrine environment. First, our data demonstrate that lake browning processes developed naturally since the Early Holocene with the progression of chemical weathering and podzol formation, but an ecological threshold due to browning intensification was crossed ca. 6000 years ago, under maximum forest density and in the absence of fire disturbance. Second, fire likely reduced lake water browning and soil podzolisation processes by mineral erosion and the decrease in litter and soil organic matter due to direct burning or indirect aeration. Soil and aquatic ecosystem components appeared highly interconnected and responded even to low disturbance severities. Consequently, these components may react more sensitively to the predicted increase in fire frequency and severity, with yet unknown ripple effects on the forest ecosystem resilience. Given the critical role of the Picea abies - Fagus sylvatica - Abies alba closed-canopy forests in shaping the microclimate and catchment-lake interactions, our study suggests that non-intervention strategies are suitable for preserving ecosystem stability in these types of forests. By promoting natural recovery of shade-tolerant tree species and minimizing direct anthropogenic impacts, this approach would protect the soil, regulate erosion and delivery of soil organic substances, and potentially mitigate the effects of climate change on fire hazard and on individual forest tree species. • Shade-related traits of late successional trees reduced local fire frequency and severity. • Fire regime and forest dynamics controlled soil erosion and lake browning. • Picea – Fagus – Abies closed temperate mountain forests tolerate low severity, surface fire. • Soil and lake environments are interconnected and may react sensitively to increased fire. • Promoting natural forest recovery could buffer the climate-related increase in fire hazard. [ABSTRACT FROM AUTHOR]

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions. • The BNDVI index allows to map and estimate potential soil losses after wildfire. • Soil burn severity need to be evaluated separately from vegetation burn severity. • A positive correlation exists between soil burn severity and BDNVI index. • The BNDVI index varies according to vegetation type. [ABSTRACT FROM AUTHOR]

Wildfires are predicted to increase in frequency and severity with climate change but detailed understanding of their influence on forest wildlife is lacking for many species. Wildfires vary in their extent and severity among landscapes, limiting generalisations about impacts from a single wildfire. We investigated post-wildfire persistence in the threatened exudivorous yellow-bellied glider (Petaurus australis) in south-eastern Australia. The widespread occurrence of wildfires in the 'Black Summer' of 2019–20 enabled populations in three landscapes (90–160 km apart) to be studied. This species has a slow life history (one young per year and >1 year to mature) so post-fire recovery is predicted to be slow. We conducted repeat surveys over 3 years post-fire in each landscape at 25–29 sites that were occupied pre-wildfire. All survey sites were burnt in two landscapes but only 40 % were burnt in the third. We estimated declines in occupancy of 13 %, 40 % and 66 % in burnt forest. The factors driving the variation in decline appear to be a combination of fire severity and the abundance of very large (≥80 cm) hollow (i.e. cavity) -bearing trees that serve as den sites. The implications of this study are that the impacts of wildfires on populations of tree-hollow dependent species can be expected to vary in different landscapes as a consequence of fire severity, rainfall deficit and logging history. Our study suggests the importance of ensuring populations are conserved across a wide geographic gradient, and the value of very large hollow-bearing trees rather than simply the number of any hollow-bearing trees. • Knowledge of the response of wildlife to wildfires is a global deficiency. • The Australian Black Summer fires allowed unique landscape replication of wildfires. • We investigated post-fire persistence in an iconic arboreal mammal. • We estimated declines in occupancy in burnt forest landscapes of 13 %, 40 % and 66 %. • Very large hollow-bearing trees appear to influence survival through wildfire. [ABSTRACT FROM AUTHOR]

• Compared artificial restoration and natural regeneration in the Hengduan Mountains. • Vegetation recovering well from low-severity fires through natural regeneration. • The effectiveness of artificial restoration increased with the higher fire severity. • In fire-prone areas, demand for artificial restoration rises with higher fire risks. The Hengduan Mountains in Southwest China are currently facing a recurring and escalating threat of forest fires, posing a significant challenge to vegetation restoration efforts given the ecological value of this region. However, the effects of artificial restoration compared to natural regeneration on vegetation recovery dynamics have been less explored. This study focused on two sites in the Hengduan Mountains, primarily composed of conifer forests. One site employed a mix of artificial restoration measures, including tree planting, aerial grass seeding, and fertilizer application. The other site, which is relatively drier and colder, relied on natural regeneration. Four fire events in these two sites were compared to examine the effects of artificial restoration on vegetation recovery using remotely sensed data from Landsat and MODIS. Fire severity was classified using the differenced Normalized Burn Ratio (dNBR) and validated with field samples. Vegetation greenness loss and recovery trajectory were quantified using the near-infrared reflectance of vegetation (NIRv). The effects of artificial restoration were further analyzed after employing unburnt controls to minimize the impact of climate fluctuations. Results showed that fire severity mapping achieved an overall accuracy of 82%. The average NIRv loss across the four fires was 30%, 49%, and 69% in areas of low, moderate, and high severity, respectively. In low severity areas, natural and artificial recovery rates were similar, at 21% and 26% respectively, three years post-fire. In moderate severity areas, artificial restoration achieved a 46% recovery rate compared to 31% for natural recovery. In high severity areas, artificial restoration resulted in a 63% recovery rate versus 45% for natural recovery. Although the differences in recovery patterns may also be attributed to inherent variations in the vegetation composition, physical environment, and their interactions, this study suggests that high severity areas benefit significantly from artificial intervention for rapid recovery. Future studies should include more controlled experiments at the plot scale to further elucidate the processes of vegetation response to different artificial restoration measures. [ABSTRACT FROM AUTHOR]

A global trend of increasing tree cover in savannas has been observed and ascribed to a range of possible causes, including CO2 levels, changing rainfall and fire frequency. We tested these explanations in the Australian tropical savanna, taking 96 savanna ‘cool burning’ projects from Australia’s emissions offset scheme as case studies. We obtained readings of tree cover and explanatory variables from published remote sensing or spatial data sources. These were analysed using time-series linear regression to obtain coefficients for the influence of severe fire occurrence, annual rainfall and prior percentage tree cover. Although statistically significant coefficients for the key variables were found in only half (severe fire) or one quarter (rainfall) of the individual project models, when comparing all the model coefficients across the rainfall gradient, ecologically coherent explanations emerge. No residual trend was observed, suggesting rising CO2 levels have not influenced tree cover over the study period. Our approach models tree cover change by separating ecological drivers from human-controlled factors such as fire management. This is an essential design feature of national emissions inventories and emissions offsets programs, where crediting must be additional to the expected baseline, and arise from human activity.

Abstract Although natural disturbances such as wildfire, extreme weather events, and insect outbreaks play a key role in structuring ecosystems and watersheds worldwide, climate change has intensified many disturbance regimes, which can have compounding negative effects on ecosystem processes and services. Recent studies have highlighted the need to understand whether wildfire increases or decreases after large‐scale beetle outbreaks. However, observational studies have produced mixed results. To address this, we applied a coupled ecohydrologic‐fire regime‐beetle effects model (RHESSys‐WMFire‐Beetle) in a semiarid watershed in the western US. We found that in the red phase (0–5 years post‐outbreak), surface fire extent, burn probability, and surface and crown fire severity all decreased. In the gray phase (6–15 years post‐outbreak), both surface fire extent and surface and crown fire severity increased with increasing mortality. However, fire probability reached a plateau during high mortality levels (>50% in terms of carbon removed). In the old phase (one to several decades post‐outbreak), fire extent and severity still increased in all mortality levels. However, fire probability increased during low to medium mortality (≤50%) but decreased during high mortality levels (>50%). Wildfire responses also depended on the fire regime. In fuel‐limited locations, fire probability increased with increasing fuel loads, whereas in fuel‐abundant (flammability‐limited) systems, fire probability decreased due to decreases in fuel aridity from reduced plant water demand. This modeling framework can improve our understanding of the mechanisms driving wildfire responses and aid managers in predicting when and where fire hazards will increase.

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolutionWorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classifiedWorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-offbetween accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer.

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation's development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio—RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations' vitality in low severity and moderate high severity appeared to be at pre-fire level. [ABSTRACT FROM AUTHOR]

Following the creation of a new organic layer after a forest fire, there is an initial build-up phase of overall biota. We studied soil nematode community development in a chronosequence of post-fire coniferous forest sites in relation to different fire severity. The taxonomic and functional composition of the soil nematode community was analyzed to detect immediate changes and levels of post-fire recovery in soil food web structures, i.e., 0, 1, 4, 8, 14, 20, 45, and 110 years after the event. Unburned forest sites served as controls. With small exceptions recorded immediately after the burn (mean nematode abundance, total biomass), the low severe wildfires had no impacts on the structures of nematode communities. The structures of nematode communities were found to be stable on sites affected by low severe wildfires, without considerable fluctuations in comparison to the unburned sites during chronosequence. On the contrary, nematode communities responded considerably to fires of high severity. The significant changes, i.e., a decrease of mean nematode abundance, plant parasites, omnivores and predators, species number, and nematode diversity, the values of CI, SI, MI, but an increase in the number of bacterivores and EI were recorded immediately after the fire. Such status, one year after a fire of high severity, has been observed. Full recovery of nematode communities 14 years after the disruption was found. Overall, our results showed that fire severity was a considerable element affecting soil nematode communities immediately after events, as well as the time needed to recover communities' structure during post-fire chronosequence. [ABSTRACT FROM AUTHOR]

This study describes the development of burn severity and vegetation regrowth classes using vegetation (NDVI) and bareland (NDBI) indices-based change vector analysis (VI-CVA) with a case study on the fire event that occurred at the Berbak National Park, Jambi Province, in 2015. The main objective was to determine the type and the severity level of change due to fire or vegetation regrowth, as summarised in CVA magnitude and direction images. The vegetation and bareland indices were derived from Landsat medium-resolution images to detect the degree of change caused by the forest fires. The study found that severity and vegetation regrowth could be classified into five classes: unburnt, very low, low, and moderate severity burn classes and a moderate regrowth class from bare land to oil palm plantation, and unburnt. It was also found that the performance of this CVA approach was superior to the delta normalized burn ratio (dNBR) method as indicated by its ability to detect five post-fire severity classes with 87.7% overall accuracy compared with dNBR, which detected four post-fire severity classes with 66.9% overall accuracy. This paper reports that burn severity and vegetation regrowth can be classified well using change vector analysis (CVA). CVA using an NDVI and NDBI approach was superior to the dNBR method as indicated by the accuracy difference between both methods. [ABSTRACT FROM AUTHOR]

Wildfire severity is assumed to be an important driver of habitat availability and species' distributions in forest ecosystems. Many studies have focused on the immediate or short‐term effects of fire severity in fire‐prone ecosystems, with much less focus on how long the effect of fire severity may persist. We examined the effect of fire severity on the distribution of arboreal mammals at 3 and 10 years post‐fire, in a temperate forest ecosystem dominated by eucalypts that have strong resistance and resilience to low and high severity fire, respectively. The study took place within areas affected by the 2009 Kilmore East‐Murrindindi wildfires in south‐eastern Australia. Sites were established across a gradient of fire severity (unburnt through to canopy consumption) and surveyed at 3 and 10 years following fire. Owing to low detection rates for many species, analyses were limited to greater glider (Petauroides volans) occurrence and arboreal species richness. At both 3 and 10 years post‐fire, the greater glider was restricted to unburnt forest or sites affected only by understorey fire, which acted as fire refugia. Greater gliders were absent from sites affected by high severity, canopy‐consuming fires at both points in time. Greater glider occurrence was associated with high levels of canopy cover (>50%), which were typically those areas that were unburnt or burnt at low severity. The richness of arboreal mammals increased over time and was greater in unburnt or minimally affected forest. Our findings emphasize: (i) that fire severity is an important driver of the distribution of arboreal mammals; and (ii) the importance of recognizing longer‐term effects of fire severity in assessing species distributions, even in highly resilient forest communities. Recent advances in mapping fire severity provide new opportunities to assess the effects of fire severity on the distributional patterns of fauna in eucalypt forests. [ABSTRACT FROM AUTHOR]

Approximately 40% of earth's carbon (C) stored in land vegetation and soil is within the boreal region. This large C pool is subjected to substantial removals and transformations during periodic wildfire. Fire‐altered C, commonly known as pyrogenic carbon (PyC), plays a significant role in forest ecosystem functioning and composes a considerable fraction of C transport to limnic and oceanic sediments. While PyC stores are beginning to be quantified globally, knowledge is lacking regarding the drivers of their production and transport across ecosystems. This study used the chemo‐thermal oxidation at 375°C (CTO‐375) method to isolate a particularly refractory subset of PyC compounds, here called black carbon (BC), finding an average increase of 11.6 g BC m−2 at 1 year postfire in 50 separate wildfires occurring in Sweden during 2018. These increases could not be linked to proposed drivers, however BC storage in 50 additional nearby unburnt soils related strongly to soil mass while its proportion of the larger C pool related negatively to soil C:N. Fire approximately doubled BC stocks in the mineral layer but had no significant effect on BC in the organic layer where it was likely produced. Suppressed decomposition rates and low heating during fire in mineral subsoil relative to upper layers suggests potential removals of the doubled mineral layer BC are more likely transported out of the soil system than degraded in situ. Therefore, mineral soils are suggested to be an important storage pool for BC that can buffer short‐term (production in fire) and long‐term (cross‐ecosystem transport) BC cycling. Plain Language Summary: Northern forests store a substantial fraction of the earth's carbon. Monitoring these stores is crucial for understanding forest contribution to atmospheric CO2 levels as well as their health under a changing climate. Wildfire has a major impact on shifting forest carbon stores to the atmosphere via emissions due to burning. However, a fraction of burnt material is left in forests as pyrogenic carbon (PyC). PyC is known to have positive effects on forest health and is thought to have increased resistance to being emitted to the atmosphere. Little predictive knowledge exists regarding how much PyC is produced during wildfire and even less is known about how that PyC is transported into the larger environment. This study utilized a large area wildfire field sampling campaign to study PyC in northern forests. It found that the amount of PyC produced could not be predicted by forest or fire properties. However, it was discovered that a large fraction of wildfire produced PyC quickly moves downward in the soil profile where it will be protected from decomposition and future burning. This novel finding suggests that one of the first steps of PyC transport is also a protective one, conserving carbon and mitigating climate change. Key Points: Fifty separate summer 2018 boreal wildfires were sampled 1 year postfire for black carbon (BC) across the 57–67° latitudinal range within SwedenFire had an insignificant effect on total BC stocks in organic layers but approximately doubled stocks in mineral layersMineral soil may function to buffer short‐term (production) and long‐term (cross‐ecosystem transport) BC processes [ABSTRACT FROM AUTHOR]

Background: Wildfire smoke may dampen fire severity through effects on weather and the persistence of atmospheric inversions, but empirical work on the link between smoke and fire severity is scarce. Aims: To assess the influence of daily smoke characteristics on wildfire severity in complex terrain. Methods: A customised smoke detection algorithm based on MODIS imagery was integrated into statistical models of fire severity across 106 wildfires between 2002 and 2018 in the Klamath Mountains, USA. Key results: Smoke characteristics improved predictions of fire severity in non-reburn areas but not in reburns. Maximum daily smoke cover interacted with elevation, showing a strong dampening effect of high smoke cover on fire severity at low elevations consistent with prior work and a weaker amplifying effect on fire severity at middle elevations with low smoke cover. Conclusions: Feedbacks between smoke and atmospheric inversions dampen fire severity in valleys but may amplify fire severity at middle elevations above inversion layers. Implications: The influence of smoke on fire severity may strengthen in the future as large fires and extreme fire weather become more common, yet may also weaken as reburns become more prevalent. Smoke cover over valleys may reduce wildfire severity by affecting weather. We quantified the effect of smoke cover on fire severity for 106 fires in the Klamath Mountains, USA. Smoke reduced fire severity at low elevations as expected, yet side slopes just above smoke plumes experienced more severe fire effects. [ABSTRACT FROM AUTHOR]

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel's finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel's higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems. [ABSTRACT FROM AUTHOR]

Aim: To estimate loss of above‐ground carbon (AGC) and conversion of live carbon to dead carbon following understorey and canopy fire. Location: South‐eastern Australia. Time period: 2019–2020. Major taxa studied: Four widespread resprouting eucalypt forests. Methods: Above‐ground carbon was measured in 15 plots in each of four forest types one‐year post‐fire. We also assessed topkill, that is, trees subject to canopy loss that failed to resprout epicormically. Results: While canopy fire was associated with greater declines in AGC than understorey fire, this was only statistically significant for only one forest type, where AGC declined from 154 to 85 Mg C ha−1 following canopy fire. Significant post‐fire increases in dead AGC were observed in one forest type, where dead carbon increased from 22 to 60% after canopy fire. Topkill of trees following canopy fire (48–78% of stems) was higher than topkill after understorey fire (36–53% of stems) and in unburnt forest (12–55%). Topkill occurred primarily in small‐diameter stems. Consequently, there was no effect of fire on the proportion of dead AGC in trees, with the exception of the forest with lowest productivity (i.e., lowest biomass) and lowest annual rainfall, where dead tree carbon increased from 8% in unburnt forest to 13 and 53% after understorey and canopy fire, respectively. AGC in understorey vegetation and coarse woody debris was similar or lower in burnt compared with unburnt forest. Litter carbon was significantly lower and pyrogenic carbon significantly higher in burnt forest, with no difference between understorey and canopy fire. Main conclusions While increased fire severity was associated with increased changes to carbon stocks, there were differences among forest types. Specifically, the driest forest type had the highest rates of topkill following canopy fire. These results highlight the importance of spatial variability in fire severity and forest type in determining the effects of fire on carbon stocks. [ABSTRACT FROM AUTHOR]

Aim: The aims were: (1) to identify the environmental drivers of interannual variation in wildfire extent and severity; (2) to examine temporal trends in climatic potential for large and severe wildfires; and (3) to assess whether environmental conditions experienced during the 2019–2020 mega‐fire season were anomalous. Location: South‐eastern Australia. Time period: 1953–2020. Major taxa studied: Temperate forests. Methods: We used satellite‐derived fire severity mapping from 1988 to 2020 to model the effects of drought, weather and fuels on the annual area burned and the proportion of the area burned that was impacted by high‐severity fire across four bioregions. Trends in wildfire extent and severity were then estimated from 1953 to 2020 using these derived models and gridded climate data to assess changes in climatic potential for large and severe wildfires. Estimates of wildfire extent and severity for the 2019–2020 fire season were then assessed against prior seasons (1953–2019). Results: Annual area burned was positively related to the severity of seasonal drought and frequency of fire weather conditions that promote substantial daily fire growth. Wildfire severity was elevated in years with severe fire weather and increased with increasing antecedent drought in years without severe fire weather. Fuels had a lesser effect on wildfire extent and severity than climate. Potential fire extent and severity have increased over time in response to an increased severity of drought and worsening fire weather conditions. Estimates of wildfire extent and severity during the 2019–2020 fire season approached the upper extreme within each bioregion, owing to widespread extreme climatic conditions. Main conclusions: The climatic potential for large and severe forest fires has increased across south‐eastern Australia since the 1950s, probably because of anthropogenic climate change. The magnitude and severity of the 2019–2020 fires reflected climatic conditions that are driving an increase in the size and severity of wildfires. [ABSTRACT FROM AUTHOR]

Aim: Wildfire burned area, fire size, fire severity and the ecological and socio‐economic impacts of fire have been increasing rapidly in California in recent decades. We summarize the record‐breaking 2020 wildfire season in California statistically, evaluate the drivers of high‐severity burning in the 2020 fires and consider implications for fire and resource management. Location: California, USA. Time period: 2020, with consideration of long‐term trends in many variables. Major taxa studied: Humans, vegetation and wildlife. Methods: We statistically summarize the record‐breaking 2020 fire year in California and outline the salient ecological and socio‐economic impacts. Then we fit two statistical models to determine how a suite of weather‐ and fuel‐related variables influenced high‐severity burning in different vegetation types and in different fire events during the 2020 fire season. Results: In 2020, 1.74 million ha burned in California, 2.2 times more than the previous historical record but only average when compared with pre‐Euroamerican conditions. Economic losses exceeded $19 billion, and 33 people were killed directly by fire. Vegetation type and recent fire history had important effects on burning. Variability in high‐severity burning among vegetation types was driven principally by vapour pressure deficit and wind speed; variability among fire events was related principally to time since the last fire (a surrogate for fuel loading). Main conclusions: The 2020 fires were part of an accelerating decades‐long trend of increasing burned area, fire size, fire severity and socio‐ecological costs in California. In fire‐prone forests, the management emphasis on reducing burned area should be replaced by a focus on reducing the severity of burning and restoring key ecosystem functions after fire. There have been positive developments in California vis‐à‐vis collaborative action and increased pace and scale of fuel management and pre‐ and postfire restoration, but the warming climate and other factors are rapidly constraining our options. [ABSTRACT FROM AUTHOR]

The tropical peatland ecosystems of Indonesia provide direct economic benefits to local communities and act to maintain local weather patterns. The impact of burning tropical peat swamp forests of land clearing for palm oil plantations can have significant consequences on the change in the characteristics of peat soil. The aim of this study was to determine the physical, chemical, and biological properties of peat soils by field and laboratory testing and analysis to understand changes in the nature and characteristics of peatlands at four locations in the Pelalawan Regency of Riau Province. The results showed that the effect of burning peat swamp forests can lead to a change in the physical, chemical, and biological properties of the peat soils. Soil permeability and the soil microbial population can significantly decrease with increasing fire severity. The effect of different fire severities on the characteristics of peat soil is verified to contribute to advanced management of the tropical peatland in the future. [ABSTRACT FROM AUTHOR]

Boreal peatland ecosystems are generally resilient to low severity wildfire. However, climate change may increase wildfire intensity and frequency, potentially shifting wetlands to less wildfire resistant states. Peatlands formed by infilling generally undergo a succession from open water to grounded peat, with spatially complex intermediate states which may impact wildfire resistance. We explored the relationship between wetland successional states and fire severity following a > 11,000 ha wildfire in Ontario’s Boreal Shield landscape. We digitized 144 wetlands of varying successional states from aerial imagery and assessed fire severity using the Relative differenced Normalized Burn Ratio calculated from Sentinel-2A satellite imagery. Completely peat-filled wetlands were small in area (0.7 ± 2 ha) but were most frequent on the landscape (n = 99/144) compared to a smaller number (n = 8) of large (19 ± 2 ha) spatially complex wetlands that comprised > 30% of the total wetland area. In wetlands covered completely, or nearly completely, by peat, fire severity was significantly higher compared to wetlands with interspersed patches of shallow open water. Moreover, > 90% of fire resistant wetlands with open water were associated with beaver dams. Wetlands with more complex surface cover experienced lower mean fire severity but greater variability in fire severity suggesting that variable fuel configuration (i.e., spatially heterogeneous surface cover) in complex wetlands limit wildfire propagation across the wetland surface. Our findings are important for landscape conservation and wildfire management, as spatially complex wetlands host a diverse array of habitats for at-risk species and may offer protection from severe wildfires as fire refugia. [ABSTRACT FROM AUTHOR]

Globally, many species and ecosystems are experiencing landscape-scale wildfires ('megafires') and these events are predicted to increase in frequency and severity as the climate warms. Consequently, the capability to rapidly assess the likely impacts of such large fires and identify potential risks they pose to the persistence of species and ecosystems is vital for effective conservation management. In this review, we propose novel frameworks to identify which plant species and ecosystems are most in need of management actions as a result of megafires. We do this by assessing the impacts of a fire event on plants and ecosystems in the context of the whole fire regime (current fire event combined with recent fire history) and its interactions with other threatening processes, rather than simply considering the amount of habitat burnt. The frameworks are based on a combination of key species' traits related to mechanisms of decline, components of the fire regime that are most likely to have adverse impacts on species or ecosystem recovery, and biotic and environmental factors that may amplify fire impacts or pose barriers to post-fire recovery. We applied these frameworks to guide management priorities and responses following the extensive 2019/2020 fires in southern Australia, and we illustrate their application here via a series of worked examples that highlight the various mechanisms of post-fire decline the frameworks address. The frameworks should be applicable to a broader range of fire-prone biomes worldwide. Our approach will (1) promote the development of foundational national datasets for assessing megafire impacts on biodiversity, (2) identify targeted priority actions for conservation, (3) inform planning for future fires (both prescribed burning and wildfire suppression), and (4) build awareness and understanding of the potential breadth of factors that threaten plants and ecosystems under changing fire regimes. The occurrence of large-scale fires is increasing globally. Adverse impacts of these fires on plants and ecosystems can be significant without rapid management responses. We developed novel predictive frameworks to identify the species and vegetation types in most urgent need of post-fire intervention to promote successful recovery after such megafires. The frameworks described here are applicable to major fire events in a diverse range of biomes across the world. [ABSTRACT FROM AUTHOR]

This paper studied the characteristics of geomorphology, fire behavior, unconsolidated source material volume, runoff and sediment production related to post-fire debris flow formations, field investigation in the burned area and rainfall simulation experiment were adopted to study the post-fire debris flow in Huayanzi gully, Mianning County, Sichuan Province occurred on 4 July 2019. The associated ash layer, water repellency and permeability experiments were also conducted. The results suggest that (1) the runoff charge shows a positive correlation with water repellency in burned area. (2) the sediment yields in severely burned area are conspicuously larger than that in moderate and slight burned areas. (3) the water repellency in burned area is approximately positively correlated with fire severity, while the permeability presents the negative correlation. This study revealed the evolution process of post-fire debris flow, and provided theoretical basis for the prevention and early warning of post-fire debris flows.

Climate warming and changing fire regimes in the North American boreal zone have the capacity to alter the hydrology and ecology of the landscape with long term consequences to peatland ecosystems and their traditional role as carbon sinks. It is important to understand how peatlands are affected by wildfire in relation to both extent of burn and severity of burn to the organic soil (peat) layers where most of the C is stored. Peatlands cover more than 75% of the landscape in the southern Northwest Territories, Canada where extreme drought led to widespread wildfires in 2014–2015. To assess the wildfire effects across a 14.6 million ha study area including 136 wildfire events, we used an integration of field data collection, land cover mapping of peatland and upland ecotypes, Landsat-8-based mapping of burn severity to the soil organic layers, and MODIS-hotspot mapping of fire progression for season of burning. The intersection of these geospatial products allows for a broadscale assessment of wildfire effects across gradients of ecotype, ecoregions, seasons, and year of burn. Using a series of chi-squared goodness of fit tests, we found that peatlands are more susceptible to wildfire on the Taiga shield where they are smaller and hydrologically isolated by the rocky landscape. There burning affected proportionally larger peat areas with an evenness of burn severity to the organic soil layers which may lead to less spatial diversity in post-fire recovery, making the landscape less resilient to future fire. The most resilient peatlands are expected to be hydrologically well-connected to both ground water systems and larger peatland complexes such as those on the Taiga plains which exhibited large unburned and singed patches across the landscape, and greater variability in burn severity across seasons and ecotypes. Understanding the tipping point of drought conditions at which the landscape becomes connected, and peatlands are susceptible to wildfire with deeper burning of the organic soil layers is important for understanding the potential future effects of climate change and projected increases in wildfire on peatlands. This is critical for C accounting and climate mitigation strategies.

The impacts of fires on society and the environment are wide-ranging, and examining such fire incidents is of critical importance to communities, governments, and scientists. Satellite imagery analysis enables the monitoring of burned scars and can help us better understand the social, ecological, and economic consequences of fires in more detail. However, most satellite studies measuring fire area and impacts focus on large areas in natural landscapes, typically forests. We highlight the need to develop methods for fire detection and analysis in human-dominated landscapes, specifically, such vulnerable populations as those found in refugee camps globally. We calculate a suite of spectral indices (SI’s) developed from Sentinel-2 data with a random forest algorithm to quantify the fire impact on the Rohingya refugee camps in Teknaf, Bangladesh. For this study, we developed a method called the Fractional Charred Index (FCI) using the field plots within the burn areas inside the refugee camp settlements and compared the FCI with differenced Spectral Indices (dSI’s) to quantitively assess the burn severity at four levels: (i) high severity, (ii) moderate severity, (iii) low severity and (iv) unburned camp. Our study identified 140 acres of burn area, damaging over 8,000 refugee shelters, 1,760 of which were severely damaged and 3,452 of which suffered moderate damage. The FCI-dSIs relationship was measured by a simple linear model which indicated that the differenced Normalized Burned Index (dNBR) and the differenced Char Soil Index (dCSI) have the strongest positive correlation and can be used interchangeably to assess the scale and intensity of the fire-caused damage and fire severity assessment in this and similar types of case studies. The novel method presented here to determine fire severity in human-dominated landscapes, specifically refugee camps, may be quickly deployed and is easily interpretable and computationally inexpensive. Therefore, the proposed method of identifying burned settlements in refugee camps has significant potential for impact monitoring, disaster management, and recovery efforts after such fire events.

Understanding how fire severity affects resprouting plants during post-fire regeneration is key to anticipating Mediterranean vegetation vulnerability in a context of increasingly large fires with high intensity and severity due to climate change. Here, we monitored the water status, leaf gas exchange, and plant growth in holm oaks (Quercus ilex) of central Spain burned with different fire severity throughout the first post-fire year. The Q. ilex burned with high severity (HB+) showed higher water potential and shoot growth than those burned with low severity (LB+) or unburned (B−), especially during spring and summer. In summer, resprouting HB+ and LB+ plants exhibited higher carbon assimilation than unburned ones. Moreover, we also found that plants with higher water availability and growth, i.e., HB+ individuals, had higher specific leaf area and lower water use efficiency. Overall, our study shows that holm oak forests exhibit high plasticity to fire and that Q. ilex burned with high severity have a faster short-term regeneration than those burned with low severity. However, this rapid regeneration is based on a less conservative water-use strategy, which could jeopardize their populations in case of extreme drought events increasingly common in the current context of climate change.

Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure.

It is a widespread assumption that burned area and severity are increasing worldwide due to climate change. This issue has motivated former analysis based on satellite imagery, revealing a decreasing trend in global burned areas. However, few studies have addressed burn severity trends, rarely relating them to climate variables, and none of them at the global scale. Within this context, we characterized the spatiotemporal patterns of burned area and severity by biomes and continents and we analyzed their relationships with climate over 17 years. African flooded and non-flooded grasslands and savannas were the most fire-prone biomes on Earth, whereas taiga and tundra exhibited the highest burn severity. Our temporal analysis updated the evidence of a decreasing trend in the global burned area (−1.50% year−1; p < 0.01) and revealed increases in the fraction of burned area affected by high severity (0.95% year−1; p < 0.05). Likewise, the regions with significant increases in mean burn severity, and burned areas at high severity outnumbered those with significant decreases. Among them, increases in severely burned areas in the temperate broadleaf and mixed forests of South America and tropical moist broadleaf forests of Australia were particularly intense. Although the spatial patterns of burned area and severity are clearly driven by climate, we did not find climate warming to increase burned area and burn severity over time, suggesting other factors as the primary drivers of current shifts in fire regimes at the planetary scale.

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Fire can change gallery forest vegetation structure, thereby altering nutrient fluxes between terrestrial and aquatic ecosystems. In Brazilian savannas, which are fire‐prone ecosystems, the fire regime is changing due to human activities such as converting native vegetation to farmland and urban areas. Uncontrolled wildfires in these savannas can reach gallery forests, which are more sensitive to fire impacts, leading to concerns about the effects of fire on gallery forest vegetation structure and freshwater ecosystems. We analysed the relationships between fire severity (the degree to which the fire has changed an area), percentage of burned area, variation in precipitation with gallery forest and savanna vegetation structure recovery and input of nutrients (NH4, NO2−, NO3− and PO43) to streams for 16 months after a fire event in five watersheds associated with small streams. One year after the fire, vegetation recovery (NDVI) was lower in gallery forest areas than in savanna woodlands, despite the more severe fires in savannas. The short‐term effects of fire on gallery forest vegetation included increased nitrate concentrations in streams, which were also influenced by increased precipitation and the extent of the burned area. The nutrient inputs into the stream stabilized within 1 year. However, gallery forest vegetation did not fully recover at that time and may continue to alter the functioning of the aquatic ecosystem. Together, these results demonstrate the need for an integrated fire management plan that considers both gallery forests and the surrounding savannas in the landscape to address consequences to aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Mediterranean Basin forest ecosystems are recurrently affected by wildfires. The occurrence of insect pests following fire may be a critical factor affecting tree survival and forest recovery. Although ambrosia beetles are viewed with increasing concern, information about the host selection and colonization behavior of these beetles in Mediterranean broadleaf forests is very scarce and inexistent in areas affected by wildfires. After a forest fire in Portugal, we selected 841 burned and unburned cork oak trees and assessed the presence and intensity of ambrosia beetle attacks, as well as other tree characteristics, and used generalized linear models to investigate the factors driving host selection. In contrast with burned oaks, where beetle attacks were frequent, the unburned trees were little affected. Attacks in the burned forest were more frequent in larger trees that suffered higher fire severity, in trees being exploited for cork (but with thicker bark), and in trees that were closer to the unburned area. A contagious effect to neighboring unburned trees was not observed, and cork harvesting in subsequent years following fire also did not increase the probability of beetle attacks. These results help us to understand the risk of ambrosia beetle attacks and improve forest management in cork oak forests. [ABSTRACT FROM AUTHOR]

The ash produced by forest fires is a complex mixture of organic and inorganic particles with many properties. Amounts of ash and char are used to roughly evaluate the impacts of a fire on nutrient cycling and ecosystem recovery. Numerous studies have suggested that fire severity can be assessed by measuring changes in ash characteristics. Traditional methods to determine fire severity are based on in situ observations, and visual approximation of changes in the forest floor and soil which are both laborious and subjective. These measures primarily reflect the level of consumption of organic layers, the deposition of ash, particularly its depth and color, and fire-induced changes in the soil. Recent studies suggested adding remote sensing techniques to the field observations and using machine learning and spectral indices to assess the effects of fires on ecosystems. While index thresholding can be easily implemented, its effectiveness over large areas is limited to pattern coverage of forest type and fire regimes. Machine learning algorithms, on the other hand, allow multivariate classifications, but learning is complex and time-consuming when analyzing space-time series. Therefore, there is currently no consensus regarding a quantitative index of fire severity. Considering that wildfires play a major role in controlling forest carbon storage and cycling in fire-suppressed forests, this study examines the use of low-cost multispectral imagery across visible and near-infrared regions collected by unmanned aerial systems to determine fire severity according to the color and chemical properties of vegetation ash. The use of multispectral imagery data might reduce the lack of precision that is part of manual color matching and produce a vast and accurate spatio-temporal severity map. The suggested severity map is based on spectral information used to evaluate chemical/mineralogical changes by deep learning algorithms. These methods quantify total carbon content and assess the corresponding fire intensity that is required to form a particular residue. By designing three learning algorithms (PLS-DA, ANN, and 1-D CNN) for two datasets (RGB images and Munsell color versus Unmanned Aerial System (UAS)-based multispectral imagery) the multispectral prediction results were excellent. Therefore, deep network-based near-infrared remote sensing technology has the potential to become an alternative reliable method to assess fire severity. [ABSTRACT FROM AUTHOR]

Context: Several plant traits are associated with resistance to fire, thus fire-resistant species may give rise to more fire-resistant landscapes. However, up-scaling from plant traits to landscape- and regional-scale fire effects remains a challenge. Objectives: We test two hypotheses: (1) forests composed of fire-resistant species experience lower fire severity than forests composed of less fire-resistant species; and (2) wildfires affecting forests with greater fire resistance experience smaller patches of high-severity fire. Methods: We used a predictive map of existing forest types (major tree species dominating forest composition) and a trait-based map of fire resistance. We examined large-scale spatial patterns of fire severity derived from Landsat imagery in 611 wildfires across the range of western larch in the Inland Northwest USA (1985–2014). We then applied structural equation modeling to study complex relationships between fire resistance and high-severity fire in each wildfire. Results: Forest types dominated by fire-resister species (e.g., ponderosa pine) experienced lower fire severity than forest types dominated by non-resister species such as lodgepole pine (fire-embracer) and subalpine fir (fire-avoider). We found a strong negative correlation between the fire resistance index and average values of the relative differenced normalized burn ratio, as well as an indirect relationship between fire resistance and high-severity patch size. Conclusions: The large-scale differences in fire severity among forest types generally reflect the degree of fire resistance that fire-related traits confer to individual trees species, providing evidence that incorporating plant traits has the potential to assist in assessing fire resistance at large spatial scales. [ABSTRACT FROM AUTHOR]

Early and ongoing monitoring of post-fire recovery dynamics is critical for understanding the ecological resilience of forests. The unprecedented scale of Australia's Black Summer of 2019-2020 poses a significant challenge for post-fire recovery monitoring. Remote sensing is the only practical way to undertake broad-scale analysis of post-fire recovery dynamics. We used Sentinel 2 satellite imagery to compare pre-fire and 1-year post-fire Normalised Burn Ratio (NBR) values. A strong positive trend in the return of vegetation was indicated where post-fire values met or exceeded 80% of the pre-fire NBR value. Our assessment highlighted locations where early recovery responses were divergent from trends in the surrounding areas. Overall, there was a strong spectral recovery across the 2019-2020 south-east Australia fire extent, with more than 50% of the total burned area showing greater than 80% spectral recovery after 1 year. However, several areas where ecosystem resilience may be threatened were identified, including fire-sensitive ecosystems and severely drought-impacted regions. Our results demonstrated biogeographic variation in the effect of fire severity on post-fire spectral recovery, with little effect of severity on spectral recovery observed in the north-east, but much stronger effects in the south. Our study highlights further research for prioritisation as part of adaptive land management programs. [ABSTRACT FROM AUTHOR]

Debate remains about the effectiveness of commercial thinning as a wildfire management strategy, with some studies reporting somewhat lower severity in thinned forests, and some reporting higher severity, during wildfires. However, while vegetation severity is a measure of basal area tree mortality, research on this question generally omits tree mortality from thinning itself. We investigated whether cumulative tree mortality, or cumulative severity, from commercial thinning and wildfire was different between thinned and unthinned forests in the Caldor Fire of 2021 in the northern Sierra Nevada mountains of California, USA. We found significantly higher cumulative severity in commercial thinning areas compared to unthinned forests. More research is needed to determine whether cumulative severity is higher in commercially thinned forests in other large western US wildfires. [ABSTRACT FROM AUTHOR]