Monitoring post-fire neighborhood competition effects on pine saplings under different environmental conditions by means of UAV multispectral data and structure-from-motion photogrammetry.

In burned landscapes, the recruitment success of the tree dominant species mainly depends on plant competition mechanisms operating at fine spatial scale, that may hinder resource availability during the former years after the disturbance. Data acquisition at very high spatial resolution from unmanned aerial vehicles (UAV) have promoted new opportunities for understanding context-dependent competition processes in post-fire environments. Here, we explored the potentiality of UAV-borne data for assessing inter-specific competition effects of understory woody vegetation on pine saplings, as well as intra-specific interactions of neighboring saplings, across three burned landscapes located along a climatic/productivity gradient in the Iberian Peninsula. Geographic object-based image analysis (GEOBIA), including multiresolution segmentation and support vector machine (SVM) classification, was used to map pine saplings and understory shrubs at species level. Input data were, on the one hand, multispectral (11.31 cm·pixel-1) and Structure-from-Motion (SfM) canopy height model (CHM) data fusion, hereafter MS-CHM, and, on the other, RGB (3.29 cm·pixel-1) and CHM data fusion, hereafter RGB-CHM. A Random Forest (RF) regression algorithm was used to evaluate the effects of neighborhood competition on the relative growth in height of 50 pine saplings randomly sampled across the MS-CHM classified map. Circular plots of 3 m radius were set from the centroid of each target pine sapling to measure percentage cover, mean height of all individuals in the plot and mean height of individuals contacting the target sapling. Competing shrub species were differentiated according to their fire-adaptive traits (i.e. seeders vs resprouters). Object-based image classification applied on MS-CHM yielded higher overall accuracy for the three sites (83.67% ± 3.06%) than RGB-CHM (74.33% ± 3.21%). Intra-specific competitive effects were not detected, whereas increasing cover and height of shrub neighbors had a significant non-linear impact on the growth on pine saplings across the study sites. The strongest competitive effects of seeder shrubs occurred in open areas with low vegetation cover and fuel continuity, following a gap-dependent model. The non-linear relationships evidenced in this study between the structure of neighboring shrubs and the growth of pine seedlings/saplings have profound implications for considering possible competing thresholds in post-fire decision-making processes. These results endorse the use of UAV multispectral and SfM photogrammetry as a valuable post-fire management tool for measuring accurately the effect of competition in heterogeneous burned landscapes.

Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire.

In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R2 = 0.83; RMSE = 7.92%) than from Sentinel-2 (R2 = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and under-estimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.

Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape.

The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges.

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.

Resistance of soil bacterial communities from montane heathland ecosystems in the Cantabrian mountains (NW Spain) to a gradient of experimental nitrogen deposition.

Elevated atmospheric nitrogen (N) deposition on terrestrial ecosystems has become one of the most important drivers of microbial diversity loss on a global scale, and has been reported to alter the soil function of nutrient-poor, montane Calluna vulgaris heathlands in the context of global change. In this work we analyze for the first time the shifts of bacterial communities in response to experimental addition of N in Calluna heathlands as a simulation of atmospheric deposition. Specifically, we evaluated the effects of five N addition treatments (0, 10, 20, and 50 kg N ha-1 yr-1 for 3-years; and 56 kg N ha-1 yr-1 for 10-years) on the resistance of soil bacterial communities as determined by changes in their composition and alpha and beta diversities. The study was conducted in montane Calluna heathlands at different development stages (young and mature phases) in the southern side of the Cantabrian Mountains (NW Spain). Our results evidenced a substantial increase of long-term (10-years) N inputs on soil extractable N-NH4+, particularly in young Calluna stands. The alpha diversity of soil bacterial communities in mature Calluna stands did not show a significant response to experimental N addition, whereas it was significantly higher under long-term chronic N addition (56 kg N ha-1 yr-1 for 10-years) in young Calluna stands. These bacterial community shifts are mainly attributable to a decrease in the dominance of Acidobacteria phylum, the most representative in montane Calluna ecosystems, in favor of copiotrophic taxa such as Actinobacteria or Proteobacteria phyla, favored under increased N availability. Future research should investigate what specific ecosystem functions performed by soil bacterial communities may be sensitive to increased nitrogen depositions, which may have substantial implications for the understanding of montane Calluna ecosystems' stability.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls.

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015-2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.

Wildland-urban interface typologies prone to high severity fires in Spain.

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016-2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Short-term responses of ecosystem multifunctionality to fire severity are modulated by fire-induced impacts on plant and soil microbial communities.

This study represents a first attempt to shed light into the mechanisms that modulate the response of ecosystem multifunctionality (EMF) to fire severity in post-fire landscapes. We specifically investigated the role played by fire-induced changes on above and belowground communities in the modulation of EMF responses at short-term after fire. For this purpose, we estimated EMF using an averaging approach from three ecosystem functions (carbon regulation, decomposition and soil fertility) and their standardized functional indicators in field plots burned at low and high fire severity 1-year after a wildfire occurred in a Mediterranean ecosystem in the central region of Spain. Plant taxonomic and functional richness, and the bacterial and fungal taxonomic richness, were measured in the plots as community properties with a potential intermediate control over fire severity effects on EMF. The ecological effects of fire severity on above and belowground communities were important in shaping EMF as evidenced by Structural Equation Modeling (SEM). Indeed, the evidenced shrinkage exerted by high fire severity on EMF at short-term after fire was not direct, but modulated by fire-induced effects on the plant functional richness and the microbial taxonomic richness. However, EMF variation was more strongly modulated by indirect effects of fire severity on the biodiversity of soil microbial communities, than by the effects on the plant communities. Particularly, the fungal community exerted the strongest intermediate control (standardized SEM ß coefficient = 0.62), which can be linked to the differential response of bacterial (ß = -0.36) and fungal (ß = -0.84) communities to fire severity evidenced here. Our findings demonstrate that the effects of fire severity on above and belowground communities are important drivers of short-term ecosystem functioning. Efforts tailored to secure the provision of multiple functions should be focused on promoting the recovery on soil microbial communities under high-severity scenarios.

Estimates of fine fuel litter biomass in the northern Great Basin reveal increases during short fire-free intervals associated with invasive annual grasses.

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.

ALOS-2 L-band SAR backscatter data improves the estimation and temporal transferability of wildfire effects on soil properties under different post-fire vegetation responses.

Remote sensing techniques are of particular interest for monitoring wildfire effects on soil properties, which may be highly context-dependent in large and heterogeneous burned landscapes. Despite the physical sense of synthetic aperture radar (SAR) backscatter data for characterizing soil spatial variability in burned areas, this approach remains completely unexplored. This study aimed to evaluate the performance of SAR backscatter data in C-band (Sentinel-1) and L-band (ALOS-2) for monitoring fire effects on soil organic carbon and nutrients (total nitrogen and available phosphorous) at short term in a heterogeneous Mediterranean landscape mosaic made of shrublands and forests that was affected by a large wildfire. The ability of SAR backscatter coefficients and several band transformations of both sensors for retrieving soil properties measured in the field in immediate post-fire situation (one month after fire) was tested through a model averaging approach. The temporal transferability of SAR-based models from one month to one year after wildfire was also evaluated, which allowed to assess short-term changes in soil properties at large scale as a function of pre-fire plant community type. The retrieval of soil properties in immediate post-fire conditions featured a higher overall fit and predictive capacity from ALOS-2 L-band SAR backscatter data than from Sentinel-1 C-band SAR data, with the absence of noticeable under and overestimation effects. The transferability of the ALOS-2 based model to one year after wildfire exhibited similar performance to that of the model calibration scenario (immediate post-fire conditions). Soil organic carbon and available phosphorous content was significantly higher one year after wildfire than immediately after the fire disturbance. Conversely, the short-term change in soil total nitrogen was ecosystem-dependent. Our results support the applicability of L-band SAR backscatter data for monitoring short-term variability of fire effects on soil properties, reducing data gathering costs within large and heterogeneous burned landscapes.

Predicting potential wildfire severity across Southern Europe with global data sources.

The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.

The effect of sheep grazing abandonment on soil bacterial communities in productive mountain grasslands.

Livestock grazing abandonment entails important shifts on the overall ecosystem function, but the effects of this land-use change on specific bacterial taxa remain poorly understood in mountain grasslands. Moreover, we currently lack knowledge about the feedbacks between changes in ecosystem functions affected by livestock abandonment in mountain grasslands and the soil bacterial communities. Here, we evaluated the behavior of bacterial communities' structure and composition at taxa level as a function of short (1-year) and long-term (15-years) grazing abandonment in a mountain grassland. We also linked the observed responses in the bacterial communities to changes in several ecosystem functions (i.e. primary production, plant species biodiversity, carbon stocks and soil fertility). The alpha diversity of the bacterial communities did not show a significant response as a consequence of grazing abandonment. However, we identified significant changes on the overall composition of soil bacterial communities between the long-term abandoned grassland areas and grazed or abandoned areas in the short term. We also evidenced a balance between the number of operational taxonomic units (OTUs) whose relative abundance is favored by livestock grazing (19.51 %) and those with higher relative abundances in long-term grazing exclusion areas (20.23 %) that could behave as indicators of grazing abandonment. Structural Equation Modeling analyses proved that several bacterial taxa associated with relevant ecosystem functions, such as Rhodospirillales order within Alphaproteobacteria phylum, featured significant changes in their relative abundance between grazing treatments. The direct and indirect effects of grazing exclusion on woody species encroachment and soil organic carbon were strongly linked to the changes in the abundance of bacterial taxa indicators. The assessment of the bacterial community response to livestock abandonment in mountain grasslands may thus provide early warning signs before subtle changes in ecosystem functions occur.

Nitrogen oxides and ozone in Portugal: trends and ozone estimation in an urban and a rural site.

This study provides an analysis of the spatial distribution and trends of NO, NO2 and O3 concentrations in Portugal between 1995 and 2010. Furthermore, an estimation model for daily ozone concentrations was developed for an urban and a rural site. NO concentration showed a significant decreasing trend in most urban stations. A decreasing trend in NO2 is only observed in the stations with less influence from emissions of primary NO2. Several stations showed a significant upward trend in O3 as a result of the decrease in the NO/NO2 ratio. In the northern rural region, ozone showed a strong correlation with wind direction, highlighting the importance of long-range transport. In the urban site, most of the variance is explained by the NO2/NOX ratio. The results obtained by the ozone estimation model in the urban site fit 2013 observed data. In the rural site, the estimated ozone during extreme events agrees with observed concentration.

Indoor aerosol size distributions in a gymnasium.

In this study, an indoor/outdoor monitoring program was carried out in a gymnasium at the University of Leon, Spain. The main goal was a characterization of aerosol size distributions in a university gymnasium under different conditions and sports activities (with and without magnesia alba) and the study of the mass fraction deposited in each of the parts of the respiratory tract. The aerosol particles were measured in 31 discrete channels (size ranges) using a laser spectrometer probe. Aerosol size distributions were studied under different conditions: i) before sports activities, ii) activities without using magnesia alba, iii) activities using magnesia alba, iv) cleaning procedures, and v) outdoors. The aerosol refractive index and density indoors were estimated from the aerosol composition: 1.577-0.003i and 2.055 g cm(-3), respectively. Using the estimated density, the mass concentration was calculated, and the evolution of PM1, PM2.5 and PM10 for different activities was assessed. The quality of the air in the gymnasium was strongly influenced by the use of magnesia alba (MgCO3) and the number of gymnasts who were training. Due to the climbing chalk and the constant process of resuspension, average PM10 concentrations of over 440 µg m(-3) were reached. The maximum daily concentrations ranged from 500 to 900 µg m(-3). Particle size determines the place in the respiratory tract where the deposition occurs. For this reason, the inhalable, thoracic, tracheobronchial and respirable fractions were assessed for healthy adults and high risk people, according to international standards. The estimations show that, for healthy adults, up to 300 µg m(-3) can be retained by the trachea and bronchi, and 130 µg m(-3) may reach the alveolar region. The different physical activities and the attendance rates in the sports facility have a significant influence on the concentration and size distributions observed.