https://uvadoc.uva.es/handle/10324/1418 Dpto. Tecnología Electrónica - Artículos de revista Fri, 17 Apr 2026 06:19:19 GMT 2026-04-17T06:19:19Z https://uvadoc.uva.es/handle/10324/72468 The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events. Sun, 01 Jan 2023 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72468 2023-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72466 The obtention of wall-to-wall fire severity estimates through reliable remote sensing-based techniques that align with management needs is a critical factor in post-fire decision-making processes. In this paper, we novelty proposed a multi-date change detection framework based on the variation in fractional vegetation cover (FCOVER), with enough ecological sense and physical basis to be generalizable across different plant communities and burned landscapes with varying environmental conditions. This framework meets the definition of fire severity operationally used in the field as a biophysical indicator when fire effects on the understory and overstory layers are linked. The FCOVER was retrieved from Sentinel-2 surface reflectance scenes by inverting PROSAIL-D radiative transfer model (RTM) simulations using the random forest regression algorithm. FCOVER retrievals were validated in the field using burned and unburned control plots. We computed the FCOVERr metric as the ratio of post-fire to pre-fire FCOVER. We tested the relationship of the FCOVERr and the most common bi-temporal spectral indices in the literature, i.e. the differenced Normalized Burn Ratio (dNBR), the Relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), with the Composite Burn Index (CBI) measured in field plots for validation purposes in two case-study wildfires in the western Mediterranean Basin. We also calculated the transferability of FCOVERr and the spectral indices between different plant communities within each site, as well as between sites. The predictive errors of pre and post-fire FCOVER retrievals were found to be low (RMSE ≈ 10%) for the two study sites. Overall, the FCOVERr metric provided more accurate CBI estimations (R2 = 0.87 ± 0.04) than spectral indices (R2 = 0.71 ± 0.13). The CBI was linearly related with the FCOVERr metric for both sites, whereas the type of relationship with spectral indices was not consistent, which translated into better transferability performance of the FCOVERr metric (nRMSE = 14.27% ± 3.75%) than that of the spectral indices (nRMSE = 21.97% ± 8.09%), not only between different Mediterranean plant communities within sites, but also between the two sites. Spectral indices underestimated moderate to high fire severity to a greater extent than FCOVERr in the CBI field plots, and misclassified fire severity in several areas with patchiness fire effects identified in the field. The FCOVERr product proposed in this study may be a sound choice for the operational identification of priority areas for post-fire management. Sun, 01 Jan 2023 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72466 2023-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72441 Background. Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires. Aims. This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems. Methods. Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non- photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires Key results. MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation. Conclusions. Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub- pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation. Implications. MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised. Mon, 01 Jan 2024 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72441 2024-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72440 The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets. Mon, 01 Jan 2024 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72440 2024-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72439 The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP features. Mon, 01 Jan 2024 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72439 2024-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72416 The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6-22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, mono-temporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices. Tue, 01 Jan 2019 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72416 2019-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72415 The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity. Tue, 01 Jan 2019 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72415 2019-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72413 Multispectral imagery is a widely used source of information to address post-fire ecosystem management. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions, demonstrating broad transferability along the entire climatic gradient. Mon, 01 Jan 2018 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72413 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/72412 Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6–11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managers. Mon, 01 Jan 2018 00:00:00 GMT https://uvadoc.uva.es/handle/10324/72412 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/71543 Frequency converters controlling electric motors are widely used as they increase the functionality of the equipment and improve energy efficiency. However, it is inevitable that the quality of the power delivered to the motor does not have the same characteristics as when it is supplied from the mains. Due to the impact of the power quality on the operation of the motor and even on its possible diagnosis, it is desirable to characterise the power quality, preferably by means of rates or indices that reflect the quality as completely as possible. However, the rates specified in the standards and in the literature are designed to characterise the quality delivered from the grid and are not well adjusted to the features of the signal at the output of the converter with a high interharmonic and harmonic content at high and low frequencies, and with variable fundamental frequencies. For this reason, this work proposes a set of distortion rates and the appropriate procedures for calculating them to reflect as reliably as possible the quality of the energy delivered by the converter to the motor. To verify the validity of the proposal, several practical examples are presented with induction motors fed from different frequency converters. Mon, 01 Jan 2024 00:00:00 GMT https://uvadoc.uva.es/handle/10324/71543 2024-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/71540 Harmonic distortion is one of the disturbances that most affects the quality of the electrical system. The widespread use of power electronic systems, especially power converters, has increased harmonic and interharmonic emission in a wide range of frequencies. Therefore, there are new needs in the measurement of harmonic distortion in modern electrical systems, such as measurement in the supra-harmonic range (>2 kHz) and the measurement of interharmonics. The International Electrotechnical Commission (IEC) standards define new total harmonic distortion (THD) rates based on the concept of frequency groupings. However, the rates defined in the IEC standards have shortcomings when measuring signals such as those present in the outputs of power systems with abundant interharmonic content and presence of components in the supra-harmonic range. Therefore, in this work, a comparison is made between the different THD factors currently defined, both in the literature and in the standards, to show which of them are the most suitable for assessing harmonic and interharmonic contamination in power system signals such as those present at the output of inverters. Fri, 01 Jan 2021 00:00:00 GMT https://uvadoc.uva.es/handle/10324/71540 2021-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/71497 Variable speed drives, commonly employed for motor speed control, incorporate electronic converters that emit harmonics to both the power grid and the motor-load system. These harmonics can have adverse effects on the motors, necessitating the experimental measurement of their diverse and unpredictable harmonic content. To perform these measurements on the input side of the drives or power grid, commercial quality analyzers are available, but they are not prepared to perform these measurements on the motor-load or output side of the drive. The outputs of the drives generate a significant harmonic and interharmonic content across a wide range of low and high frequencies, coupled with variable fundamental frequencies. Consequently, the development of a specialized measurement system tailored to these signal characteristics becomes imperative. This paper presents a harmonic measurement and analysis system specifically designed for frequency inverter’s output signals, which adheres to international standards that standardize measurements and includes new features to evaluate converter signals not available in commercial meters. By utilizing this system, frequency groupings and distortion rates are obtained to comprehensively analyze the power quality of commercial drives under realistic operating conditions, thus enabling the early detection of potential failures and the implementation of preventive measures. Mon, 01 Jan 2024 00:00:00 GMT https://uvadoc.uva.es/handle/10324/71497 2024-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/67791 Background: The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results: Total and overstory AGB were more accurately estimated (R2 equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R2 ¼ 0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ¼ 0.60) and overstory (R2 ¼ 0.53) AGB, whereas the relationship with understory AGB was weaker (R2 ¼ 0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ¼ 122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ¼ 158.41). Conclusions: This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard. Sat, 01 Jan 2022 00:00:00 GMT https://uvadoc.uva.es/handle/10324/67791 2022-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/67789 Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recur Wed, 01 Jan 2020 00:00:00 GMT https://uvadoc.uva.es/handle/10324/67789 2020-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/67787 Successful post-fire management depends on accurate burn severity maps that are increasingly derived from satellite data, replacing field-based estimates. Post-fire vegetation and soil changes, besides modifying the reflected and emitted radiation recorded by sensors onboard satellites, strongly alters water balance in the fire affected area. While fire-induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis (MESMA), changes in water balance are mainly registered by evapotranspiration (ET). As both types of variables have a clear physical meaning, they can be easily understood in terms of burn severity, providing a clear advantage compared to widely-used spectral indices. In this research work, we evaluate the potential of Landsat-derived ET to estimate burn severity, together with MESMA derived Sentinel-2 fraction images and important environment variables (pre-fire vegetation, climate, topography). In this study, we use the random forest (RF) classifier, which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate. Our study area is located in Central Portugal, where a mega-fire burned >450 km2 from 17 to 24 June 2017. We used the official burn severity map as ground reference. The RF algorithm identified ET as the most important variable in the burn severity model, followed by MESMA char fractions. When both ET and MESMA char fraction image were used as RF inputs, burn severity estimates reached higher accuracy than if only one of them was used, which suggests their potential synergetic interaction. In particular, when environmental variables were used in addition to ET and char fraction, the highest accuracy for burn severity was reached (κ = 0.79). Our main conclusion is that post-fire fine resolution ET is a useful and easily understandable indicator of burn severity in Mediterranean ecosystems, in particular when used in combination with a MESMA char fraction image. This novel approach to estimate burn severity may help to develop successful post-fire management strategies not only in Mediterranean ecosystems but also in other ecosystems, due to ease of generalization. Wed, 01 Jan 2020 00:00:00 GMT https://uvadoc.uva.es/handle/10324/67787 2020-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/67786 All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR Tue, 01 Jan 2019 00:00:00 GMT https://uvadoc.uva.es/handle/10324/67786 2019-01-01T00:00:00Z