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We investigated the influence of fire severity, logging of burnt wood, local ecological factors and their interaction on the natural regeneration, survival and growth of maritime pine (Pinus pinaster Ait.), following a fire that took place in 2005. During the period 2006–2020, a sample of 1900 seedlings were monitored, in which three post-fire treatments were applied: (1) Early logging (before seedling emergence); (2) Delayed logging (after emergence); and (3) No management. Multivariate semi-parametric and non-parametric techniques were used to model seedling survival, estimated density and growth of natural pine regeneration. Seedling survival was 31% with a mean density of more than 2000 seedlings/ha at the end of the study period. Logging before seedling emergence was positively related with pine survival and density. Delayed logging resulted in the lowest seedling density and regeneration. Fire severity had a negative influence on regeneration density. The findings indicate that site conditions and fire severity have a stronger influence on natural regeneration of maritime pine than subsequent post-fire management treatments. In order to ensure the presence of maritime pine in pure or mixed stands, silvicultural work is required to control competition from other species and reduce the risk of new wildfires. [ABSTRACT FROM AUTHOR]

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries. [ABSTRACT FROM AUTHOR]

The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation. [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 FROM AUTHOR]

This study explored, for the first time, the drivers shaping large fire size and high severity of forest fires classified as level-2 in Spain, which pose a great danger to the wildland–urban interface. Specifically, we examined how bottom-up (fuel type and topography) and top-down (fire weather) controls shaped level-2 fire behavior through a Random Forest classifier at the regional scale in Galicia (NW Spain). We selected for this purpose 93 level-2 forest fires. The accuracy of the RF fire size and severity classifications was remarkably high (>80%). Fire weather overwhelmed bottom-up controls in controlling the fire size of level-2 forest fires. The likelihood of large level-2 forest fires increased sharply with the fire weather index, but plateaued at values above 40. Fire size strongly responded to minimum relative humidity at values below 30%. The most important variables explaining fire severity in level-2 forest fires were the same as in the fire size, as well as the pre-fire shrubland fraction. The high-fire-severity likelihood of level-2 forest fires increased exponentially for shrubland fractions in the landscape above 50%. Our results suggest that level-2 forest fires will pose an increasing danger to people and their property under predicted scenarios of extreme weather conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An analysis of fire characteristics in the boreal forests of Siberia (50–75° N, 60–140° E) was performed for the period 2002–2022. We found a positive trend in the proportion of high-intensity fires in dominant forest stands of Siberia based on long-term series of variations in the Fire Radiative Power (FRP) measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results showed that there was an increase in the proportion of areas of high-intensity fires over the past decade on about ~30% of the boreal forests of Siberia, including the Arctic zone. For the sample group of fires, the level of correlation (R2 = 0.80–0.94) between the fire impact, classified according to the NBR/dNBR technology, and the integral FRP values was revealed. The intensity of combustion in terms of FRP is associated with the volume of burned biomass and determines the dynamics of specific emissions values per unit area. The results suggest that further increase in fire emissions in Siberia will be determined not only by an increase of burned areas, but also by a redistribution of low- and high-intensity burning and an increase in specific emission values. Finally, we estimated that Siberian fires are responsible for about 5–20% of the total volume of greenhouse gas emissions in the Russian Federation, depending on the fire season scenario. The recurrence of extremely high emissions (296–350 Tg C/year) will make it possible to consider part of Siberian forests as a source of carbon in the nearest future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tree hollows are an important landscape resource used by fauna for shelter, nesting, and predator avoidance. In fire-prone landscapes, wildfire and climate may impact hollow dynamics; however, assessments of their concurrent impacts are rare. We conducted a field survey at 80 sites in the Sydney Basin bioregion (Australia) to understand how fire frequency, fire severity, mean annual temperature, and mean annual precipitation concurrently impacted the site-density of small- (<5 cm entry width), medium- (5–10 cm entry width) and large-size (>10 cm entry width) tree hollows and tree basal scars (which mediate hollow formation via invertebrate access to heartwood), when tree-size and dead/live status were considered. A unimodal relationship occurred between medium- and large-sized hollow densities and fire frequency and severity, respectively, with hollow densities greatest at intermediate frequencies/severities. Increases of 1.82, 1.43, and 1.17 hollows per site were observed between the 1 (reference) and 2, 2 and 3, and 3 and >3 fire frequency categories. Increases of 1.26, 1.75 and 0.75 hollows per site were observed between the low (reference) and moderate, moderate and high, and high and very high fire severity categories. Fire severity was also positively associated with basal scar density, with increases of 2.52, 8.15, and 8.47 trees per site between the low (reference) and moderate, moderate and high, and high and very high categories. A weak positive and stronger negative association was observed between mean annual temperature and small-sized hollow and basal scar density, respectively. Dead and medium-sized tree density was positively associated with medium-sized hollow and basal scar tree density, respectively. Collectively, our results suggest that wildfires, and in some cases climate, have diverse and size-specific impacts on tree hollow and basal scar density. Our results imply that fire regimes that allow for moderately severe wildfire will promote larger-sized tree hollows, which are a limiting resource for many fauna species. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area. [ABSTRACT FROM AUTHOR]

Fire severity mapping can capture heterogeneous fire severity patterns over large spatial extents. Although numerous remote sensing approaches have been established, regional-scale fire severity mapping at fine spatial scales (<5 m) from high-resolution satellite images is challenging. The fire severity of a vast forest fire that occurred in Southwest China was mapped at 2 m spatial resolution by random forest models using Sentinel 2 and GF series remote sensing images. This study demonstrated that using the combination of Sentinel 2 and GF series satellite images showed some improvement (from 85% to 91%) in global classification accuracy compared to using only Sentinel 2 images. The classification accuracy of unburnt, moderate, and high severity classes was significantly higher (>85%) than the accuracy of low severity classes in both cases. Adding high-resolution GF series images to the training dataset reduced the probability of low severity being under-predicted and improved the accuracy of the low severity class from 54.55% to 72.73%. RdNBR was the most important feature, and the red edge bands of Sentinel 2 images had relatively high importance. Additional studies are needed to explore the sensitivity of different spatial scales satellite images for mapping fire severity at fine spatial scales across various ecosystems. [ABSTRACT FROM AUTHOR]

Fast and accurate fire severity mapping can provide an essential resource for fire management and studying fire-related ecological and climate change. Currently, mainstream fire severity mapping approaches are based only on pixel-wise spectral features. However, the landscape pattern of fire severity originates from variations in spatial dependence, which should be described by spatial features combined with spectral features. In this paper, we propose a morphological attribute profiles-based spectral–spatial approach, named Burn Attribute Profiles (BAP), to improve fire severity classification and mapping accuracy. Specifically, the BAP method uses principal component transformation and attributes with automatically determined thresholds to extract spatial features, which are integrated with spectral features to form spectral–spatial features for fire severity. We systematically tested and compared the BAP-based spectral–spatial features and spectral index features in the extremely randomized trees machine learning framework. Sentinel-2 imagery was used for seven fires in the Mediterranean region, while Landsat-8 imagery was used for another seven fires in the northwestern continental United States region. The results showed that, except for 2 fires (overall accuracy (OA) for EMSR213_P: 59.6%, EL: 59.5%), BAP performed well for the other 12 fires (OA for the 2 fires: 60–70%, 6 fires: 70–80%, 4 fires: >80%). Furthermore, compared with the spectral indices-based method, the BAP method showed OA improvement in all 14 fires (OA improvement in Mediterranean: 0.2–14.3%, US: 4.7–12.9%). Recall and Precision were also improved for each fire severity level in most fire events. Moreover, the BAP method improved the "salt-and-pepper" phenomenon in the homogeneous area, where the results are visually closest to the reference data. The above results suggest that the spectral–spatial method based on morphological attribute profiles can map fire severity more accurately. [ABSTRACT FROM AUTHOR]

The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation's (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes. [ABSTRACT FROM AUTHOR]

Fire severity, which quantifies the degree of organic matter consumption, is an important component of the fire regime. High-severity fires have major ecological implications, affecting carbon uptake, storage and emissions, soil nutrients, and plant regeneration, among other ecosystem services. Accordingly, spatially explicit maps of the fire severity are required to develop improved tools to manage and restore the most damaged areas. The aim of this study is to develop spatially explicit maps of the field-based fire severity (composite burn index—CBI) from different spectral indices derived from Sentinel 2A images and using several regression models. The study areas are two recent large fires that occurred in Tunisia in the summer of 2021. We employed different spectral severity indices derived from the normalized burn ratio (NBR): differenced NBR (dNBR), relative differenced NBR (RdNBR), and relativized burn Ratio (RBR). In addition, we calculated the burned area index for Sentinel 2 (BAIS2) and the thermal anomaly index (TAI). Different tree decision models (i.e., the recursive partitioning regression method [RPART], bagging regression trees [Bagging], and boosted regression trees [BRT]), as well as a generalized additive model [GAM]), were applied to predict the CBI. The main results indicated that RBR, followed by dNBR, were the most important spectral severity indices for predicting the field-based CBI. Moreover, BRT was the best regression model, explaining 92% of the CBI variance using the training set of points and 88% when using the validation set. These results suggested the adequacy of RBR index derived from Sentinel 2A for assessing and mapping forest fire severity in Mediterranean forests. These spatially explicit maps of field-based CBI could help improve post-fire recovery and restoration efforts. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Fire is a concern for the sustainability of dry forests such as those of the Mediterranean region, especially under warming climate and high human use. We used data derived from Landsat and MODIS sensors to assess forest changes in the Talassemtane National Park (TNP) in North Africa from 2003–2018. The Talassemtane National Park is a protected area in northern Morocco, a biodiverse, mountainous region with endemic species of concern such as the Moroccan fir (Abies marocana) and Barbary macaque (Macaca sylvanus). To help the managers of the TNP better understand how the forest has been impacted by fire vs. other disturbances, we combined information from remotely derived datasets. The Hansen Global Forest Change (GFC) data are a global resource providing annual forest change, but without specifying the causes of change. We compared the GFC data to MODIS wildfire data from Andela's Global Fire Atlas (GFA), a new global tool to identify fire locations and progression. We also analyzed surface reflectance-corrected Landsat imagery to calculate fire severity and vegetation death using Relative Differenced Normalized Burn Ratio analysis (RdNBR). In the park, GFC data showed a net loss of 1695 ha over 16 years, corresponding to an approximately 0.3% annual loss of forest. The GFA identified nine large fires that covered 4440 ha in the study period, coinciding with 833 ha of forest loss in the same period. Within these fires, detailed image analysis showed that GFA fire boundaries were approximately correct, providing the first quantitative test of GFA accuracy outside North America. High-severity fire, as determined by RdNBR analysis, made up about 32% of burned area. Overall, the GFA was validated as a useful management tool with only one non-detected wildfire in the study period; wildfires were linked to approximately 49% of the forest loss. This information helps managers develop conservation strategies based on reliable data about forest threats. [ABSTRACT FROM AUTHOR]

Communities looking to improve fire protection may consider incorporating landscape features that 'buffer' the effects of a fire between developed and undeveloped lands. While landscapes such as golf courses, vineyards, or agriculture are already being considered part of this buffer zone, few empirical studies demonstrate causally how well these different landscape features operate as a fire buffer. This research selects golf courses as an example of a possible buffer landscape and proposes methods to test if this buffer alters fire severity and limits fire spread. Using propensity score matching and multiple linear regression, we demonstrate golf courses that burned in California between 1986 and 2020 had a predicted 49% reduction in fire severity relative to otherwise similar vegetated land. This reduction in fire severity is regionally dependent, with the effect of golf course buffering landscapes most pronounced in the North Bay region. For limiting fire spread, golf courses function similarly to hardscaped land uses such as airports, suggesting that irrigation and vegetation management can be effective in creating desired buffering qualities. These methods suggest that artificially created irrigated green zones act as effective buffers, providing de facto fuel breaks around communities, and can be reproduced for other potential buffering landscape features. This study does not advocate for the use of any specific anthropogenic landscape feature, but rather highlights that community-based fire hazard reduction goals could be attained through considering landscape features beyond fuel reduction manipulations. [ABSTRACT FROM AUTHOR]

Studies pertaining to fire severity in commercially thinned versus unthinned forests are based on a comparison of tree mortality between the two categories. Commercial thinning is widely conducted on public and private forestlands as a fire management approach designed to reduce fire severity and associated tree mortality. However, tree mortality from thinning itself, prior to the occurrence of the wildfire, is generally not taken into account, which leaves a potentially important source of tree loss, with its associated forest carbon loss and carbon emissions, unreported. This study investigated the "cumulative severity" of commercially thinned and unthinned forests in a large 2021 wildfire, the Antelope fire, occurring within mixed-conifer forests on public lands in northern California, USA. Using published data regarding the percent basal area mortality for each commercial thinning unit that burned in the Antelope fire, combined with percent basal area mortality due to the fire itself from post-fire satellite imagery, it was found that commercial thinning was associated with significantly higher overall tree mortality levels (cumulative severity). More research is needed, in other large forest fires, to determine whether the finding, that commercial thinning killed more trees than it prevented from being killed, is common elsewhere. [ABSTRACT FROM AUTHOR]

Biomass combustion is a major biogeochemical process, but uncertain in magnitude. We examined multiple levels of organization (twigs, branches, trees, stands, and landscapes) in large, severe forest fires to see how combustion rates for live aboveground woody parts varied with tree species, size, and fire severity in Ponderosa pine (Pinus ponderosa Dougl. ex Laws.) and mixed conifer-dominated forests of the Sierra Nevada, California, USA. In high severity fire patches, most combustion loss was from branches < 2 cm diameter; in low to moderate severity patches, most was from bole charring. Combustion rates decreased as fire severity declined and with increasing tree size. Pinus species had little branch combustion, leading them to have ≈50% the combustion rate of other taxa. Combustion rates could be 100% for small branch segments and up to 57% for small tree aboveground woody biomass in high severity fire patches. However, combustion rates are very low overall at the stand (0.1%–3.2%) and landscape level (0.6%–1.8%), because large trees with low combustion rates comprise the majority of biomass, and high severity fire patches are less than half of the area burned. Our findings of low live wood combustion rates have important implications for policies related to wildfire emissions and forest management. [ABSTRACT FROM AUTHOR]

Fire is a powerful natural disturbance influencing vegetation patterns across landscapes. Recent transitions from mixed-species forests to post-fire shrublands after severe wildfire is an increasingly prevalent phenomenon in pine-oak and conifer forest ecosystems in southwestern North America. However, we know little about how variation in fire severity influences other common forest types in the region. In this study, we evaluated fire-induced changes in woody plant community composition and forest structure in Chiricahua Mountains in southeastern Arizona in the United States that hosts a diverse set of vegetation types. Cluster analysis of the pre-fire vegetation data identified three dominant pre-fire vegetation types including juniper woodland, piñon forest, and pine-oak forest. All vegetation types experienced significant tree mortality across a wide range of size classes and species, from forests to shrublands. The magnitude of change within sample plots varied with fire severity, which was mediated by topography. Significant shifts in dominance away from coniferous obligate seeder trees to resprouting hardwoods and other shrubs occurred across all vegetation types in response to the fire. Regeneration from seed can be episodic, but projected increases in aridity and fire frequency may promote continued dominance by hardwoods and fireand drought-resistant shrub communities, which is a regional forest management concern as wildfire size and severity continue to increase throughout the southwestern USA. [ABSTRACT FROM AUTHOR]

Australian montane sclerophyll shrubland vegetation is widely considered to be resilient to infrequent severe fire, but this may not be the case in Tasmania. Here, we report on the vegetative and seedling regeneration response of a Tasmanian non-coniferous woody montane shrubland following a severe fire, which burned much of the Great Pine Tier in the Central Plateau Conservation Area during the 2018-2019 fire season when a historically anomalously large area was burned in central Tasmania. Our field survey of a representative area burned by severe crown fire revealed that more than 99% of the shrubland plants were top-killed, with only 5% of the burnt plants resprouting one year following the fire. Such a low resprouting rate means the resilience of the shrubland depends on seedling regeneration from aerial and soil seedbanks or colonization from plants outside the burned area. Woody species' seedling densities were variable but generally low (25 m-2). The low number of resprouters, and reliance on seedlings for recovery, suggest the shrubland may not be as resilient to fire as mainland Australian montane shrubland, particularly given a warming climate and likely increase in fire frequency. [ABSTRACT FROM AUTHOR]

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-off between 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. [ABSTRACT FROM AUTHOR]

Severity of wildfires is a critical component of the fire regime and plays an important role in determining forest ecosystem response to fire disturbance. Predicting spatial distribution of potential fire severity can be valuable in guiding fire and fuel management planning. Spatial controls on fire severity patterns have attracted growing interest, but few studies have attempted to predict potential fire severity in fire-prone Eurasian boreal forests. Furthermore, the influences of fire weather variation on spatial heterogeneity of fire severity remain poorly understood at fine scales. We assessed the relative importance and influence of pre-fire vegetation, topography, and surface moisture availability (SMA) on fire severity in 21 lightning-ignited fires occurring in two different fire years (3 fires in 2000, 18 fires in 2010) of the Great Xing'an Mountains with an ensemble modeling approach of boosted regression tree (BRT). SMA was derived from 8-day moderate resolution imaging spectroradiometer (MODIS) evapotranspiration products. We predicted the potential distribution of fire severity in two fire years and evaluated the prediction accuracies. BRT modeling revealed that vegetation, topography, and SMA explained more than 70% of variations in fire severity (mean 83.0% for 2000, mean 73.8% for 2010). Our analysis showed that evergreen coniferous forests were more likely to experience higher severity fires than the dominant deciduous larch forests of this region, and deciduous broadleaf forests and shrublands usually burned at a significantly lower fire severity. High-severity fires tended to occur in gentle and well-drained slopes at high altitudes, especially those with north-facing aspects. SMA exhibited notable and consistent negative association with severity. Predicted fire severity from our model exhibited strong agreement with the observed fire severity (mean r² = 0.795 for 2000, 0.618 for 2010). Our results verified that spatial variation of fire severity within a burned patch is predictable at the landscape scale, and the prediction of potential fire severity could be improved by incorporating remotely sensed biophysical variables related to weather conditions. [ABSTRACT FROM AUTHOR]

Planted and invading non-native plant species can alter fire regimes through changes in fuel loads and in the structure and continuity of fuels, potentially modifying the flammability of native plant communities. Such changes are not easily predicted and deserve system-specific studies. In several regions of the southern hemisphere, exotic pines have been extensively planted in native treeless areas for forestry purposes and have subsequently invaded the native environments. However, studies evaluating alterations in flammability caused by pines in Patagonia are scarce. In the forest-steppe ecotone of northwestern Patagonia, we evaluated fine fuels structure and simulated fire behavior in the native shrubby steppe, pine plantations, pine invasions, and mechanically removed invasions to establish the relative ecological vulnerability of these forestry and invasion scenarios to fire. We found that pine plantations and their subsequent invasion in the Patagonian shrubby steppe produced sharp changes in fine fuel amount and its vertical and horizontal continuity. These changes in fuel properties have the potential to affect fire behavior, increasing fire intensity by almost 30 times. Pruning of basal branches in plantations may substantially reduce fire hazard by lowering the probability of fire crowning, and mechanical removal of invasion seems effective in restoring original fuel structure in the native community. The current expansion of pine plantations and subsequent invasions acting synergistically with climate warming and increased human ignitions warrant a highly vulnerable landscape in the near future for northwestern Patagonia if no management actions are undertaken. [ABSTRACT FROM AUTHOR]

Many studies have outlined the benefits for growth and reproduction resulting from thinning extremely crowded young forests regenerating after stand replacing wildfires ("thickets"). However, scarce information is available on how thinning may influence fire severity and vegetation regeneration in case a new fire occurs. We investigated the relationship between thinning and fire severity in P. halepensis thickets, and the effects on the establishment of pine seedlings and resprouting vigour in resprouter species the year after the fire. Our results show a positive relationship between forest basal area and fire severity, and thus reserved pines in thinned stands suffered less fire damage than those in un-thinned sites (respectively, 2.02 ± 0.13 vs. 2.93 ± 0.15 in a scale from 0 to 4). Ultimately, differences in fire severity influenced post-fire regeneration. Resprouting vigour varied depending on the species and the size of individuals but it was consistently higher in thinned stands. Concerning P. halepensis, the proportion of cones surviving the fire decreased with fire severity. However, this could not compensate the much lower pine density in thinned stands and thus the overall seed crop was higher in un-thinned areas. Establishment of pine seedlings was negatively affected by the slope and positively driven by the number of cones and thus it was higher in un-thinned than in thinned stands (respectively, 2581 ± 649 vs. 898 ± 325 seedlings · ha-1). Thinning decreases fire intensity, and thus it may facilitate fire suppression tasks, but retaining a higher density of pines would be necessary to ensure P. halepensis regeneration after a new fire event. [ABSTRACT FROM AUTHOR]

Many fire-adapted ecosystems in the northeastern U.S. are converting to fire-intolerant vegetation communities due to fire suppression in the 20th century. Prescribed fire and other vegetation management activities that increase resilience and resistance to global changes are increasingly being implemented, particularly on public lands. For many fire-dependent communities, there is little quantitative data describing historical fire regime attributes such as frequency, severity, and seasonality, or how these varied through time. Where available, fire-scarred live and remnant trees, including stumps and snags, offer valuable insights into historical fire regimes through tree-ring and fire-scar analyses. In this study, we dated fire scars from 66 trees at two sites in the Ridge and Valley Province of the Appalachian Mountains in central Pennsylvania, and described fire frequency, severity, and seasonality from the mid-17th century to 2013. Fires were historically frequent, of low to moderate severity, occurred mostly during the dormant season, and were influenced by aspect and topography. The current extended fire-free interval is unprecedented in the previous 250-300 years at both sites. [ABSTRACT FROM AUTHOR]

Wildfire presents a challenge to natural resource managers the world over, and the intentional setting of fires can be used to alleviate some of the challenges associated with wildfire management. Prescribed burning can be used prior to wildfires to reduce fuel loads and promote ecological integrity in fire-adapted systems, while suppression burning can help firefighters control the direction, extent, and intensity of wildfire behavior under extreme conditions. In both cases, the success of intentional fire use depends on training, knowledge, experience, and institutional and social support. The influence of these factors can significantly impact whether fire use is perceived as positive or negative, increasing or decreasing, and whether managers are supportive of its incorporation into their management planning and decision-making. Perceived impediments to fire use are likely to differ based on location, level of training and experience, and even the social context of fire management specific to different job positions in natural resource management. In order to explore how managers and stakeholders across the world perceive fire use, we surveyed over 700 respondents from 12 countries and three continents. This study represents the largest survey of perceptions on managed fire use ever conducted. Perceptions differed across age categories, job positions, and regions. Countries or regions with larger amounts of wildfire area burned tended to be more supportive of fire use for suppression, while countries with less wildfire had less positive perceptions of fire use for either prescribed or suppression burning. Bureaucracy and social perceptions were identified as impediments to using prescribed fire prior to wildfire occurrence, but neither were identified as impediments to fire use during suppression procedures. Across the countries, fire use in suppression was viewed more positively than prescribed fire use prior to wildfire occurrence. [ABSTRACT FROM AUTHOR]

Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the changes in a dry sclerophyll forest induced by a prescribed burn. Point cloud data was collected for two plots; one plot undergoing a fire treatment, and the second plot remaining untreated, thereby acting as the control. Data was collected at three epochs (pre-fire, two weeks post fire and two years post fire). Coregistration of these multitemporal point clouds to within an acceptable tolerance was achieved through a two step process utilising permanent infield markers and manually extracted stem objects as reference targets. Metrics describing fuel height and fuel fragmentation were extracted from the point clouds for direct comparison with industry standard visual assessments. Measurements describing the change (or lack thereof) in the control plot indicate that the method of data capture and coregistration were achieved with the required accuracy to monitor fire induced change. Results from the fire affected plot show that immediately post fire 67% of area had been burnt with the average fuel height decreasing from 0.33 to 0.13 m. At two years post-fire the fuel remained signicantly lower (0.11 m) and more fragmented in comparison to pre-fire levels. Results in both the control and fire altered plot were comparable to synchronus onground visual assessment. The advantage of TLS over the visual assessment method is, however, demonstrated through the use of two physical and spatially quantifiable metrics to describe fuel change. These results highlight the capabilities of multitemporal TLS data for measuring and mapping changes in the three dimensional structure of vegetation. Metrics from point clouds can be derived to provide quantified estimates of surface and near-surface fuel loss and accumulation, and inform prescribed burn efficacy and burn severity reporting. [ABSTRACT FROM AUTHOR]

The southern region of the U.S. uses prescribed fire as a management tool on more of its burnable land than anywhere in the U.S., with ecosystem restoration, wildlife habitat enhancement, and reduction of hazardous fuel loads as typical goals. Although the region performs more than 50,000 prescribed fire treatments each year, evaluation of their effects on wildfire suppression resources or behavior/effects is limited. To better understand trends in the use and effectiveness of prescribed fire, we conducted a region-wide survey of 523 fire use practitioners, working on both public and private lands. A 1-2 year prescribed fire interval was consistently viewed as effective in decreasing wildfire ignitions, behavior, and severity, as well as reducing suppression resources needed where wildfire occurred. Yet fewer than 15% of practitioners viewed burn intervals of 3-4 years as effective in reducing ignitions, underscoring the importance of high-frequency burning in vegetation communities where fuel recovery is rapid. Public lands managers identified limited budget and staffing as major institutional impediments to prescribed fire, in contrast to private individuals, more of whom chose liability as a key challenge. Differences in responses across ownership type, state, and vegetation type call for a broader perspective on how fire managers in the southern U.S. view prescribed fire. [ABSTRACT FROM AUTHOR]

Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels. [ABSTRACT FROM AUTHOR]

In this work, the potential of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map the fire-disturbed areas using the PALSAR imagery acquired before and shortly after fire events. The advantage of employing an object-based approach was not only the use of the temporal variation of the backscatter coefficient, but also the incorporation in the classification of topological features, such as neighbor objects, and class related features, such as objects classified as burned. The classification scheme resulted in mapping the burned areas with satisfactory results: 0.71 and 0.82 probabilities of detection for the two study areas. Our investigation revealed that the pre-fire vegetation conditions and fire severity should be taken in consideration when mapping burned areas using PALSAR in Mediterranean regions. Overall, findings suggest that the developed scheme could be applied for rapid burned area assessment, especially to areas where cloud cover and fire smoke inhibit accurate mapping of burned areas when optical data are used. [ABSTRACT FROM AUTHOR]

Wildfire in Siberia is extensive, affecting up to 15 Mha annually. The proportion of the vegetation affected by severe fires is yet unknown, and it is a problem that requires a solution because post-fire mortality of tree stands in Siberian taiga has a strong effect on the global budget of carbon. The impact of fire in our area of interest in eastern Siberia was analyzed using the normalized burn ratio (NBR) and its pre- versus post-fire difference (dNBR) applied to Landsat-8 (OLI) collected in 2020–2021. In this paper, we present the classification of fire impact in relation to dominant tree stands and vegetation types in boreal forests of eastern Siberia. The dNBR of post-fire plots ranged widely (0.30–0.60) in homogeneous larch (Larix sibirica, L. gmelinii) forests, pine (Pinus sylvestris) forests, dark coniferous stands (Pinus sibirica, Abies sibirica, Picea obovata), sparse larch stands, and Siberian dwarf pine (Pinus pumila) stands. We quantified the proportions of low, moderate, and high fire severity (37%, 39%, and 24% of the total area burned, respectively) in dense tree stands, which were varied to 30%, 57%, and 13%, respectively, for sparse stands and tundra vegetation dominated in the north of eastern Siberia. The proportion of severe fires varied according to the transition from dominant larch stands (33.2% of the area burned) to pine (12.6%) and dark coniferous (up to 26.4%). The current proportion of stand-replacement fires in eastern Siberia is 12–33%, depending on vegetation type and tree density, which is about 2500 thousand hectares in 2021 in the region. According to our findings, the "healthy/unburned vegetation" class was quantified as well at least 700 thousand hectares in 2021. [ABSTRACT FROM AUTHOR]

Background: Wildfire is known to reduce forest carbon stocks, but the influence of antecedent disturbance on wildfire related carbon stock losses is not as well understood. Disturbances such as logging and wildfire may increase the vulnerability of remaining carbon stocks to subsequent wildfire. Conversely, these disturbances may reduce the impact of subsequent wildfire, resulting in lower carbon stock losses. Methods: We measured above ground carbon stocks in productive resprouting Eucalypt dominated forests before and after a mixed severity fire that burned during the 2019/20 'Black Summer' fire season in south-eastern Australia. The initial surveys were stratified by time since logging and time since wildfire, allowing for an assessment of how these disturbance histories influence above ground carbon stock losses caused by subsequent wildfire. Results: Above ground carbon stock losses varied substantially; however, there was a weak decrease in losses associated with time since logging but not time since wildfire. Variance in carbon stock losses associated with logging were greater than that caused by the severity of the 2019/20 wildfire itself. Carbon losses and predicted effects of disturbance may be underestimated in some cases due to the accumulation of carbon at sites between pre- and post-fire surveys. Conclusions: This study presents the largest published dataset of direct carbon stock changes resulting from wildfire in eucalypt forests. Our findings indicate that logging reduces the stability of above ground carbon stocks in resprouting eucalypt forests. This information will be critical for land managers looking to manage forests for carbon sequestration. [ABSTRACT FROM AUTHOR]

Forest fires are a significant factor that affects the boreal forest carbon distribution which emits carbon into the atmosphere and leads to carbon redistribution among carbon pools. However, knowledge about how much carbon was transferred among pools and the immediate changes in soil nutrient contents in areas that were burned by fires of various severities are still limited. In this study, we surveyed eight wildfire sites that are located in northeast China within three months after the fires occurred. Our results indicate that the total soil nitrogen, phosphorus, and organic carbon contents significantly increased after moderate- and high-severity fires. The carbon emissions were 3.84, 5.14, and 12.86 Mg C/ha for low-, moderate-, and high-severity fires, respectively. The amount of carbon transferred among pools increased with fire severity except for the charcoal pool, storing the highest amounts of carbon in moderate-severity fires. Although the charcoal and ash pools accounted for a small proportion of the total ecosystem, they are important for biogeochemical cycles and are worthy of attention. The carbon redistribution information in our study is important for accurately estimating the forest carbon budget and providing crucial parameters for forest carbon cycling models to incorporate the carbon transfer process. [ABSTRACT FROM AUTHOR]

Many scientists and managers have an interest in describing the environment following a fire to understand the effects on soil productivity, vegetation growth, and wildlife habitat, but little research has focused on the scientific rationale for classifying the post-fire environment. We developed an empirically-grounded soil post-fire index (PFI) based on available science and ecological thresholds. Using over 50 literature sources, we identified a minimum of five broad categories of post-fire outcomes: (a) unburned, (b) abundant surface organic matter (>85% surface organic matter), (c) moderate amount of surface organic matter (≥40 through 85%), (d) small amounts of surface organic matter (<40%), and (e) absence of surface organic matter (no organic matter left). We then subdivided each broad category on the basis of post-fire mineral soil colors providing a more fine-tuned post-fire soil index. We related each PFI category to characteristics such as soil temperature and duration of heating during fire, and physical, chemical, and biological responses. Classifying or describing post-fire soil conditions consistently will improve interpretations of fire effects research and facilitate communication of potential responses or outcomes (e.g., erosion potential) from fires of varying severities. [ABSTRACT FROM AUTHOR]

Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity developed in one location did not show the same improvement when used in a new location. We evaluated three methods of assessing burn severity in the field: the Composite Burn Index (CBI)—a standardized method of assessing burn severity that combines ecologically significant variables related to burn severity into one numeric site index—and two modifications of the CBI that weight the plot CBI score by the percentage cover of each stratum. Unexpectedly, models using the CBI had higher R² and better classification accuracy than models using the weighted versions of the CBI. We suggest that the weighted versions of the CBI have lower accuracies because weighting by percentage cover decreases the influence of the dominant tree stratum, which should have the strongest relationship to optically sensed reflectance, and increases the influence of the substrates strata, which should have the weakest relationship with optically sensed reflectance in forested ecosystems. Using a large data set of CBI plots (n = 251) from four fires and CBI scores derived from additional field-based assessments of burn severity (n = 388), we predicted two metrics of image-based burn severity, the Relative differenced Normalized Burn Ratio (RdNBR) and the differenced Normalized Burn Ratio (dNBR). Predictive models for RdNBR showed slightly better classification accuracy than for dNBR (overall accuracy = 62%, Kappa = 0.40, and overall accuracy = 59%, Kappa= 0.36, respectively), whereas dNBR had slightly better explanatory power, but strong differences were not apparent. RdNBR may provide little or no improvement over dNBR in systems where pre-fire reflectance is not highly variable, but may be more appropriate for comparing burn severity among regions. [ABSTRACT FROM AUTHOR]

Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity. [ABSTRACT FROM AUTHOR]

Fires are a disturbance that can lead to short term dune destabilisation and have been suggested to be an initiation mechanism of a transgressive dune phase when paired with changing climatic conditions. Fire severity is one potential factor that could explain subsequent coastal dune destabilisations, but contemporary evidence of destabilisation following fire is lacking. In addition, the suitability of conventional satellite Earth Observation methods to detect the impacts of fire and the relative fire severity in coastal dune environments is in question. Widely applied satellite-derived burn indices (Normalised Burn Index and Normalised Difference Vegetation Index) have been suggested to underestimate the effects of fire in heterogenous landscapes or areas with sparse vegetation cover. This work assesses burn severity from high resolution aerial and Sentinel 2 satellite imagery following the 2019/2020 Black Summer fires on Kangaroo Island in South Australia, to assess the efficacy of commonly used satellite indices, and validate a new method for assessing fire severity in coastal dune systems. The results presented here show that the widely applied burn indices derived from NBR differentially assess vegetation loss and fire severity when compared in discrete soil groups across a landscape that experienced a very high severity fire. A new application of the Tasselled Cap Transformation (TCT) and Disturbance Index (DI) is presented. The differenced Disturbance Index (dDI) improves the estimation of burn severity, relative vegetation loss, and minimises the effects of differing soil conditions in the highly heterogenous landscape of Kangaroo Island. Results suggest that this new application of TCT is better suited to diverse environments like Mediterranean and semi-arid coastal regions than existing indices and can be used to better assess the effects of fire and potential remobilisation of coastal dune systems. [ABSTRACT FROM AUTHOR]

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity. [ABSTRACT FROM AUTHOR]