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Grasslands are one of the most abundant and biodiverse ecosystems in the world. However, in southern European countries, the abandonment of traditional management activities, such as extensive grazing, has caused many semi-natural grasslands to be invaded by shrubs. Therefore, there is a need to characterize semi-natural grasslands to determine their aboveground primary production and livestock-carrying capacity. Nevertheless, current methods lack a realistic identification of vegetation assemblages where grassland biophysical parameters can be accurately retrieved by the inversion of turbid-medium radiative transfer models (RTMs) in fine-grained landscapes. To this end, in this study we proposed a novel framework in which multiple endmember spectral mixture analysis (MESMA) was implemented to realistically identify grassland-dominated pixels from Sentinel-2 imagery in heterogeneous mountain landscapes. Then, the inversion of PROSAIL RTM (coupled PROSPECT and SAIL leaf and canopy models) was implemented separately for retrieving grassland biophysical parameters, including the leaf area index (LAI), fractional vegetation cover (FCOVER), and aboveground biomass (AGB), from grassland-dominated Sentinel-2 pixels while accounting for non-vegetated areas at the subpixel level. The study region was the southern slope of the Cantabrian Mountains (Spain), with a high spatial variability of fine-grained land covers. The MESMA grassland fraction image had a high accuracy based on validation results using centimetric resolution aerial orthophotographs (R2 = 0.74, and RMSE = 0.18). The validation with field reference data from several mountain passes of the southern slope of the Cantabrian Mountains featured a high accuracy for LAI (R2 = 0.74, and RMSE = 0.56 m2·m−2), FCOVER (R2 = 0.78 and RMSE = 0.07), and AGB (R2 = 0.67, and RMSE = 43.44 g·m−2). This study provides a reliable method to accurately identify and estimate grassland biophysical variables in highly diverse landscapes at a regional scale, with important implications for the management and conservation of threatened semi-natural grasslands. Future studies should investigate the PROSAIL inversion over the endmember signatures and subpixel fractions depicted by MESMA to adequately address the parametrization of the underlying background reflectance by using prior information and should also explore the scalability of this approach to other heterogeneous landscapes.

We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R2 > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R2 = 0.677 and RMSE = 0.349).

Abstract The structural complexity of plant communities contributes to maintaining the ecosystem functioning in fire‐prone landscapes and plays a crucial role in driving ecological resilience to fire. The objective of this study was to evaluate the resilience to fire off several plant communities with reference to the temporal evolution of their vertical structural diversity (VSD) estimated from the data fusion of C‐band synthetic aperture radar (SAR) backscatter (Sentinel‐1) and multispectral remote sensing reflectance (Sentinel‐2) in a burned landscape of the western Mediterranean Basin. We estimated VSD in the field 1 and 2 years after fire using Shannon's index as a measure of vertical heterogeneity in vegetation structure from the vegetation cover in several strata, both in burned and unburned control plots. Random forest (RF) was used to model VSD in the control (analogous to prefire scenario) and burned plots (1 year after fire) using as predictors (i) Sentinel‐1 VV and VH backscatter coefficients and (ii) surface reflectance of Sentinel‐2 bands. The transferability of the RF model from 1 to 2 years after wildfire was also evaluated. We generated VSD prediction maps across the study site for the prefire scenario and 1 to 4 years postfire. RF models accurately explained VSD in unburned control plots (R2 = 87.68; RMSE = 0.16) and burned plots 1 year after fire (R2 = 80.48; RMSE = 0.13). RF model transferability only involved a reduction in the VSD predictive capacity from 0.13 to 0.20 in terms of RMSE. The VSD of each plant community 4 years after the fire disturbance was significantly lower than in the prefire scenario. Plant communities dominated by resprouter species featured significantly higher VSD recovery values than communities dominated by facultative or obligate seeders. Our results support the applicability of SAR and multispectral data fusion for monitoring VSD as a generalizable resilience indicator in fire‐prone landscapes.

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.

Vegetation structural complexity (VSC) plays an essential role in the functioning and the stability of fire-prone Mediterranean ecosystems. However, we currently lack knowledge about the effects of increasing fire severity on the VSC spatial variability, as modulated by the plant community type in complex post-fire landscapes. Accordingly, this study explored, for the first time, the effect of fire severity on the VSC of different Mediterranean plant communities one year after fire by leveraging field inventory and Sentinel-1 C-band synthetic aperture radar (SAR) data. The field-evaluated VSC retrieved in post-fire scenarios from Sentinel-1 γ0 VV and VH backscatter data featured high fit (R2 = 0.878) and low predictive error (RMSE = 0.112). Wall-to-wall VSC estimates showed that plant community types strongly modulated the VSC response to increasing fire severity, with this response strongly linked to the regenerative strategies of the dominant species in the community. Moderate and high fire severities had a strong impact, one year after fire, on the VSC of broom shrublands and Scots pine forests, dominated by facultative and obligate seeder species, respectively. In contrast, the fire-induced impacts on VSC were not significantly different between low and moderate fire-severity scenarios in communities dominated by resprouter species, i.e., heathlands and Pyrenean oak forests.

Understanding the effects of traditional livestock grazing abandonment on the ability of mountain grasslands to sustain multiple ecosystem functions (ecosystem multifunctionality; EMF) is crucial for implementing policies that promote grasslands conservation and the delivery of multiple ecosystem services. In this study, we evaluated the effect of short- and long-term transhumant sheep abandonment on EMF through a grazing exclusion experiment in a grassland of the Cantabrian Mountains range (NW Spain), where transhumant sheep flocks graze in summer. We considered four key ecosystem functions, derived from vegetation and soil functional indicators measured in the field: (A) biodiversity function, evaluated from total plant species evenness, diversity and richness indicators; (B) forage production function, evaluated from cover and richness of perennial and annual herbaceous species indicators; (C) carbon sequestration function, evaluated from woody species cover and soil organic carbon indicators; and (D) soil fertility function, evaluated from NH4+-N, NO3–-N, P and K content in the soil. The EMF index was calculated by integrating the four standardized ecosystem functions through an averaging approach. Based on linear mixed modeling we found that grazing exclusion induced significant shifts in the considered individual ecosystem functions and also on EMF. Long-term livestock exclusion significantly hindered biodiversity and forage production functions, but enhanced the carbon sequestration function. Conversely, the soil fertility function was negatively affected by both short- and long-term grazing exclusion. Altogether, grazing exclusion significantly decreased overall EMF, especially in long-term livestock exclusion areas, while the decline in EMF in short-term exclusions with respect to grazed areas was marginally significant. The results of this study support the sustainability of traditional transhumance livestock grazing for promoting the conservation of grasslands and their ecosystem function in mountain regions.

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

We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity.

Heterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE ≤ 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel (α-diversity: 30 × 30 m), landscape (γ-diversity: 1 × 1 km) and regional (ε-diversity: 110 × 33 km) scales and the compositional turnover (β- and δ-diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 ≥ 0.73 and RMSE ≤ 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 ± 0.22 for α-diversity to 0.60 ± 0.09 for γ-diversity and 0.72 ± 0.11 for ε-diversity. Accordingly, we found β-diversity to be higher than δ-diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imagery.

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.