Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Southern California possesses a Mediterranean climate having semi-arid to arid characteristics and contains shrubland areas at high risk to wildfire. To assess wildfire danger, fire agencies have been monitoring the moisture of vegetation, called live fuel moisture (LFM), using field-based sampling. Unfortunately, spatial and temporal resolution of live fuel moisture data are significantly limited because sampling is labor intensive. Remote sensing satellite data has been used to monitor vegetation moisture content and health of shrublands. Therefore, a potential approach to overcome the limitations of manual measurements of live fuel moisture is to use vegetation indices (VIs) derived from satellite data. The objective of this study is to understand the link between vegetation indices derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both Terra and Aqua satellites and in-situ live fuel moisture data. In this study, five vegetation indices were calculated using 6 bands of MODIS data within the visible and infrared spectrum collectively with the focus on the three best performing: enhanced vegetation index (EVI), normalized difference water index (NDWI), and visible atmospherically resistant index (VARI). Six sites with multi-year live fuel moisture data collection type were each represented with one pixel of MODIS data with a 500m by 500m spatial resolution covering the time period of February 2000 through December 2017 acquired aboard Terra and June 2002 through December 2017 acquired aboard Aqua. Linear regression was then applied to measure the coefficient of determination (R2) between the vegetation indices and live fuel moisture data. The results show a great variance of R2 between the sites as well as a variance of best performing VI. The two strongest coefficients of determination, R2=0.74 and R2=0.72, were calculated at one site for enhanced vegetation index vs. live fuel moisture over a 15-year time period of data collected on Aqua and a 17-year time period of data collected on Terra respectively. The relationship was also affected by annual climate conditions including precipitation. Our results indicate that the satellite data reasonably well-represents the live fuel moisture with higher temporal resolutions over a large area. Utilizing the remote sensing data in wildfire danger assessment will support fire agencies by saving resources for collecting ground data and providing better dataset in both time and space. This will also be beneficial for land management and planning, stakeholders and local governments

Estimating Live Fuel Moisture in Southern California Using Remote Sensing Vegetation Water Content Proxies

Wildfires are a major ecological disturbance in Southern California and often lead to great destruction along the Wildland-Urban Interface. Live fuel moisture has been used as an important indicator of wildfire risk in measurements of vegetation water content. However, the limited field measurements of live fuel moisture in both time and space have affected the accuracy of wildfire risk estimations. Traditional estimation of live fuel moisture using remote sensing data was based on vegetation indices, indirect proxies of vegetation water content and subject to influence from weather conditions. In this study, we investigated the feasibility of estimating live fuel moisture using vegetation indices, Soil Moisture Active Passive L-band soil moisture data and the modeled vegetation water content using a non-linear model based on VIs and the stem factor associated with remote sensing moisture data products. The stem factor describes the peak amount of water residing in stems of plants and varies by land cover. We also compared the outcomes from regression models and recurrent neural network using the same independent variables. We found the modeled vegetation water content outperformed vegetation indices and the L-band soil moisture observations, suggesting a non-linear relationship between live fuel moisture and the remotely sensed vegetation signatures. We discuss our results which will improve the predictability of live fuel moisture

Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas

Fire risk assessment is one of the most important components in the management of fire that offers the framework for monitoring fire risk conditions. Whilst monitoring fire risk conditions commonly revolved around field data, Remote Sensing (RS) plays key role in quantifying and monitoring fire risk indicators. This study presents a review of remote sensing data and techniques for fire risk monitoring and assessment with a particular emphasis on its implications for wildfire risk mapping in protected areas. Firstly, we concentrate on RS derived variables employed to monitor fire risk conditions for fire risk assessment. Thereafter, an evaluation of the prominent RS platforms such as Broadband, Hyperspectral and Active sensors that have been utilized for wildfire risk assessment. Furthermore, we demonstrate the effectiveness in obtaining information that has operational use or immediate potentials for operational application in protected areas (PAs). RS techniques that involve extraction of landscape information from imagery were summarised. The review concludes that in practice, fire risk assessment that consider all variables/indicators that influence fire risk is impossible to establish, however it is imperative to incorporate indicators or variables of very high heterogeneous and “multi-sensoral or multivariate fire risk index approach for fire risk assessment in PA.Keywords: Protected Areas, Fire Risk conditions; Remote Sensing, Wildfire risk assessmen

Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests

One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for Abies pinsapo (R2 = 0.69), Juniperus spp. (R2 = 0.70), Pinus halepensis (R2 = 0.59), Pinus spp. mixed (R2 = 0.80), and Pinus spp.–Juniperus spp. (R2 = 0.59) forests. The highest values of biomass were obtained for Pinus halepensis forests (190.43 Mg ha−1). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to Pinus spp.–evergreen (2.56), Quercus suber (2.54), and Pinus mixed (2.49) forests, with the minimum being obtained for Juniperus (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to Pinus halepensis and Pinus pinaster forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation

Doctor of Philosophy

dissertationWildfire is a multifaceted, global phenomenon with ecological, environmental, climatic and socioeconomic impacts. Live fuel moisture content (LFMC) is a critical fuel property for determining fire danger. Previous research has used meteorological data and remote sensing to estimate LFMC with the goal of extending direct ground measurement. A fundemental understanding of plant physiology and spectral response toLFMC variation is needed to advance use of LFMC for fire risk management and remote sensing applications. This study integrates field samples of three species, lab measurements, remote sensing dataand statistical analysis to construct a more complete knowledge of the physical foundations of LFMC seasonalityfrom three perspectives: 1)relationships between soil moisture and LFMC; 2) spectroscopic analysis of seasonal changes in LFMC and leaf dry mass; 3) relationships between LFMC and leaf net heat content, and between leaf net heat content and remotely sensed indices. This study is the first to demonstrate a relationship between in situ soil moisture and LFMC. It also challengesthe current asumption of changing water content and stable dry matter content over time in remote sensing esimation of LFMC, showing the dominant contribution of dry matter in LFMC variation in some conifer species. The resultsdemonstrate the combination of spectroscopic data and partial least squares regression can improve modeling accuray for LFMC temporal variation, but the spectral response to changing LFMC and dry mass is difficult to seperate from broader spectral trends due to temporal change in chlorophyll, leaf structure, water and covaried biochemical components. Lastly it introducesa new vegetation variable, leaf net heat content, and demostrates its relationship with LFMC and potential for remote sensing estimation.This study will improve present capabilities of remote sensing for monitoring vegetation water stress and physiological properties. It will also advance understanding of seasonal changes in LFMC to better estimate fire danger and potential impacts of fire on ecosystems and the carbon cycle

Ground vegetation biomass detection for fire prediction from remote sensing data in the lowveld region

Student Number : 0310612G - MSc research report - School of Geography, Archaeology and Environmental Studies - Faculty of ScienceWildfire prediction and management is an issue of safety and security for many rural communities in South Africa. Wildfire prediction and early warning systems can assist in saving lives, infrastructure and valuable resources in these communities. Timely and accurate data are required for accurate wildfire prediction on both weather conditions and the availability of fuels (vegetation) for wildfires. Wildfires take place in large remote areas in which land use practices and alterations to land cover cannot easily be modelled. Remote sensing offers the opportunity to monitor the extent and changes of land use practices and land cover in these areas. In order for effective fire prediction and management, data on the quantity and state of fuels is required. Traditional methods for detecting vegetation rely on the chlorophyll content and moisture of vegetation for vegetation mapping techniques. Fuels that burn in wildfires are however predominantly dry, and by implication are low in chlorophyll and moisture contents. As a result, these fuels cannot be detected using traditional indices. Other model based methods for determining above ground vegetation biomass using satellite data have been devised. These however require ancillary data, which are unavailable in many rural areas in South Africa. A method is therefore required for the detection and quantification of dry fuels that pose a fire risk. ASTER and MAS (MODIS Airborne Simulator) imagery were obtained for a study area within the Lowveld region of the Limpopo Province, South Africa. Two of the ASTER and two of the MAS images were dated towards the end of the dry season (winter) when the quantity of fuel (dry vegetation) is at its highest. The remaining ASTER image was obtained during the middle of the wet season (summer), against which the results could be tested. In situ measurements of above ground biomass were obtained from a large number of collection points within the image footprints. Normalised Difference Vegetation Index and Transformed Vegetation Index vegetation indices were calculated and tested against the above ground biomass for the dry and wet season images. Spectral response signatures of dry vegetation were evaluated to select wavelengths, which may be effective at detecting dry vegetation as opposed to green vegetation. Ratios were calculated using the respective bandwidths of the ASTER and MAS sensors and tested against above ground biomass to detect dry vegetation. The findings of this study are that it is not feasible, using ASTER and MAS remote sensing data, to estimate brown and green vegetation biomass for wildfire prediction purposes using the datasets and research methodology applied in this study. Correlations between traditional vegetation indices and above ground biomass were weak. Visual trends were noted, however no conclusive evidence could be established from this relationship. The dry vegetation ratios indicated a weak correlation between the values. The removal of background noise, in particular soil reflectance, may result in more effective detection of dry vegetation. Time series analysis of the green vegetation indices might prove a more effective predictor of biomass fuel loads. The issues preventing the frequent and quick transmission of the large data sets required are being solved with the improvements in internet connectivity to many remote areas and will probably be a more viable path to solving this problem in the near future

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Premio extraordinario de Trabajo Fin de Máster curso 2021/2022. Máster en Geomática, Teledetección y Modelos Espaciales Aplicados a la Gestión ForestalRemotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC), an important driver of wildfire risk, due to broad data availability. However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, I explored the potential of Random Forests (RF), a machine learning technique based on the ensemble of multiple decision trees, to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. I built RF models using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. I used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). The modelling process consisted in a feature selection and two step spatial cross-validation in order to avoid spatial overfitting. The final LFMCRF model was calibrated and evaluated with samples collected between 2000 and 2014, and independently tested with samples from 2015 to 2019 reporting an overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively. The results from LFMCRF were comparable to current approaches based on radiative transfer models (RMSE ~74–78%), introducing a reliable alternative for large-scale applications. This study fills an important research gap by creating a homogeneous approach to estimate LFMC across the Western Mediterranean basin. I used the final model to generate a public database with weekly LFMC maps extended to the fire-prone Mediterranean basin.Los índices de vegetación derivados de la teledetección se han utilizado ampliamente para estimar el contenido de humedad del combustible vivo (LFMC por sus siglas en inglés), un factor importante del riesgo de incendios forestales, debido a la amplia disponibilidad de datos. Sin embargo, marcadas diferencias en la estructura de la vegetación afectan la relación entre LFMC medido en campo y la reflectancia captada por los sensores de los satélites, lo que limita la extrapolación espacial de estos índices. Para superar esta limitación, exploré el potencial de Random Forests (RF), una técnica de aprendizaje automático basada en la agregación de múltiples árboles de decisión, para estimar LFMC a escala subcontinental en la cuenca Mediterránea. Probé distintos modelos de RF usando una combinación de bandas espectrales de MODIS, índices de vegetación, la temperatura superficial terrestre y el día del año como predictores. Utilicé las bases de datos del Globe-LFMC y del programa catalán de seguimiento de LFMC como muestras de verdad-terreno (10.374 muestras). El proceso de modelado consistió en una selección de predictores y una validación cruzada espacial para evitar el sobreajuste espacial. El modelo final de LFMCRF se calibró y evaluó con muestras recolectadas entre 2000 y 2014, y se probó de forma independiente con muestras de 2015 a 2019, reportando valores generales de raíz del error cuadrático medio (RMSE por sus siglas en inglés) de 19,9% y 16,4%, respectivamente. Los resultados de LFMCRF fueron comparables a los enfoques actuales basados en modelos de transferencia radiativa (RMSE ~74–78%), introduciendo una alternativa confiable para aplicaciones a gran escala. Este estudio llena un importante vacío de investigación al crear un enfoque homogéneo para estimar LFMC en toda el área occidental de la cuenca Mediterránea. El modelo final fue usado para generar una base de datos pública con mapas de LFMC semanales extendidos a toda la cuenca Mediterránea propensa a incendios forestales

Estimating live fuel moisture content in Oklahoma plants

Live fuel moisture content (LFM) is an important variable in fire danger rating systems. LFM collection is time and resource intensive and plant water relations vary within and between species. Consequently, the best approach for estimating LFM is unknown. Few studies have investigated LFM in the state of Oklahoma, and current estimates of LFM have not been validated. The objectives of this study were to evaluate the use of environmental and remote sensing proxies for estimating LFM in Oklahoma plants. I found that LFM can be accurately estimated using either hyperspectral leaf-level reflectance or environmental proxies. My analysis of several remote sensing vegetation indices identified the Water Index and VIgreen as the best suited indices for approximating LFM. Using functional group, photoperiod, vapor pressure deficit, and rainfall I was able to estimate LFM in Oklahoma plant communities. In addition to these findings, I identified a need to reevaluate current methods for estimating LFM. By advancing our understanding of LFM and how best to predict it, my results can be used in fire danger rating systems to protect lives and preserve natural resources

Aplicación de herramientas de teledetección multiescala para la caracterización espacial de indicadores y condicionantes del impacto ecológico de los incendios forestales

106 p.[ES] En las últimas décadas, la actividad antropogénica ha causado cambios notables en los atributos del régimen de incendios en los países de la cuenca del Mediterráneo occidental, debido principalmente a la pérdida de usos tradicionales derivados del abandono rural, el cambio climático y la falta de estrategias de gestión forestal adecuadas, lo que ha llevado a una acumulación de biomasa propensa a incendios. El nuevo régimen de incendios, caracterizado por un aumento en la frecuencia de incendios forestales extensos y severos, afecta a importantes funciones y servicios de los ecosistemas, con impactos sin precedentes a nivel socioeconómico. Este hecho es especialmente relevante en las zonas de interfaz urbano-forestal (WUI), donde los incendios forestales extremos representan una grave amenaza para la vida humana y los bienes. En este contexto, la caracterización espacial del impacto inducido por el fuego, comúnmente como como severidad del fuego, es crucial para proporcionar una base científica que permita diseñar estrategias de gestión forestal adecuadas que mejoren la respuesta adaptativa de los ecosistemas a los regímenes de incendios actuales. Los métodos de campo se consideran muy fiables para evaluar los impactos en la vegetación y el suelo en paisajes quemados, aunque a menudo carecen de la exhaustividad espacial que permita evaluar incendios forestales de gran tamaño. Por ello, los métodos de teledetección han surgido como herramientas fiables el seguimiento y la cuantificación de la severidad a gran escala debido a su rentabilidad y su naturaleza sinóptica. En este contexto, el objetivo principal de esta Tesis Doctoral es el desarrollo de nuevas técnicas de teledetección multiescala dirigidas a identificar indicadores espaciales de los impactos ecológicos inducidos por el fuego y evaluar los impulsores del comportamiento extremo de los incendios forestales bajo diferentes regímenes de fuego a lo largo de un gradiente climático ibérico, con especial atención a las WUIs debido a su alta vulnerabilidad socioeconómica. En primer lugar, se pretendió mejorar la estimación de la severidad del fuego en los suelos forestales, que son compartimentos críticos del ecosistema que impulsan las funciones y procesos del ecosistema, vinculando indicadores ecológicos de la severidad con la señal espectral de productos de teledetección de muy alta resolución espacial obtenidos con vehículos aéreos no tripulados (UAV) (Artículos I y II). La severidad del fuego en el suelo se evaluó en el campo 1 mes después de un incendio forestal a través de un Índice Compuesto de Severidad en el Suelo (CBSI), y de un conjunto de indicadores individuales (profundidad y cobertura de la capa de cenizas, cobertura de restos finos, cobertura de restos gruesos y profundidad de suelo desestructurado). Además, se analizaron propiedades de suelo potencialmente indicadoras de cambios inducidos por el fuego: diámetro medio ponderado (MWD), contenido de humedad del suelo (SMC) y carbono orgánico del suelo (SOC). Simultáneamente, se recolectaron imágenes multiespectrales posteriores al incendio del sensor satelital Sentinel-2A MSI (resolución espacial moderada) e imágenes RGB y multiespectrales procedentes de un vuelo UAV (resolución espacial muy alta). Se ha encontrado que los productos multiespectrales UAV tenían mejor rendimiento para estimar la variación del impacto del fuego en el suelo que los productos RGB, estando más relacionados con índices multi-integrados (es decir, CBSI) que con indicadores individuales (Artículo I). La profundidad y la cobertura de cenizas fueron los indicadores más representativos de los efectos del fuego en los suelos. La inclusión de datos de teledetección espacial y espectral mejorados mediante técnicas novedosas de teledetección, como la fusión de imágenes de Sentinel-2 y UAV, mejoró significativamente la predicción de las propiedades del suelo sensibles al fuego, relacionadas en gran medida con la severidad, principalmente el SOC (Artículo II). Este enfoque proporciona una herramienta importante para estimar los impactos del fuego en paisajes complejos y heterogéneos afectados por incendios de severidad mixta, y, en consecuencia, para identificar áreas prioritarias donde se deben implementar acciones de restauración posteriores al incendio. Una vez que se caracterizó adecuadamente el impacto ecológico potencial de los incendios forestales de alta severidad, se estudió que cambios del régimen de incendios pueden dirigir el comportamiento extremo del fuego, aspecto que se ha evaluado a lo largo de un gradiente climático Atlántico-Transición-Mediterráneo en la Península Ibérica (Artículo III), caracterizado por la ocurrencia de eventos extremos de incendios forestales en los últimos años. Con este propósito, se analizaron (i) los patrones de variación de los atributos temporales (recurrencia y tiempo desde el último incendio) y de magnitud (severidad de la quema) del régimen de incendios durante 35 años, utilizando para ello los perímetros históricos de incendios forestales derivados de la colección de imágenes de satélite Landsat, y (ii) la relación entre el régimen de incendios y las características de la vegetación previas al incendio que controlan el comportamiento extremo del fuego. Se seleccionaron ocho incendios extremos que ocurrieron durante el período 2017-2022, en los cuales se caracterizó tanto (i) el tipo y la estructura de los combustibles previos al incendio mediante técnicas de clasificación de imágenes y modelos de transferencia radiativa (RTMs), como (ii) el impacto ecológico a través del índice de Severidad de Diferencia Normalizada (dNBR) derivado de imágenes bitemporales del sátelite Sentinel-2 MSI. La recurrencia de incendios mostró la misma tendencia descendente en el tiempo a lo largo del gradiente climático, pero los patrones temporales de la severidad diferían significativamente entre las áreas Atlánticas y Mediterráneas. Los cambios observados en los atributos del régimen de incendios tuvieron una influencia notable en la formación de tipos de combustibles y en los patrones de acumulación en el paisaje propicios para el comportamiento extremo del fuego, pero siguiendo distintas vías en función del contexto ambiental. En las áreas Atlánticas, los incendios recurrentes de baja a moderada severidad pueden promover transiciones forestales hacia estados estables de matorrales propensos a retroalimentaciones de alta severidad en incendios posteriores. Un patrón similar se observó en los matorrales Mediterráneos y de Transición después de la ocurrencia repetida de incendios de alta severidad. En todas las condiciones climáticas, un largo periodo de tiempo transcurrido desde el último incendio de alta severidad puede favorecer la acumulación de combustibles en bosques de coníferas y matorrales, los cuales son altamente propensos al comportamiento extremo del fuego. Por último, se ha ampliado el conocimiento científico generado sobre los contextos biológicos que definen el comportamiento extremo del fuego en áreas de interfaz urbano-forestal con el fin de identificar los escenarios propensos a una alta severidad en las áreas de WUI debido a la creciente preocupación sobre las implicaciones socioeconómicas y ambientales (Artículo IV). Con este propósito, se eligieron catorce grandes incendios forestales ocurridos entre 2016 y 2021 en toda España que abarcaron diferentes tipologías de áreas de WUI. Utilizando criterios de densidad y distancia entre edificios se diferenciaron áreas de WUI aisladas, dispersas, densas y muy densas, así mismo, se estimaron varias características de combustibles previos al incendio dentro de las áreas de WUI, para lo cual se utilizaron imágenes de satélite multiespectrales, siguiendo la metodología utilizada en el Artículo III. El efecto combinado de los patrones de combustibles previos al incendio y la densidad de edificios se utilizó para identificar los escenarios de WUI más propensos al comportamiento extremo del fuego. Las tipologías de WUI con edificios aislados, dispersos y agrupados de manera dispersa, rodeados de un denso matorral, fueron las que presentaron el mayor riesgo de incendio. Además, las áreas WUI dominadas por árboles dispersos con un sotobosque de matorral denso y continuo constituyeron otra tipología crítica propensa a impactos severos por incendios. Se ha puesto de relieve el papel de la gestión del combustible antes de los incendios para minimizar el riesgo para las vidas humanas y los bienes, en particular bajo la creciente presión humana en las zonas WUI. Los resultados obtenidos en esta Tesis Doctoral permiten predecir escenarios prioritarios para una planificación efectiva del uso del suelo, estrategias de prevención y gestión de incendios forestales, educación comunitaria y esfuerzos colaborativos en áreas WUI, lo cual es esencial para abordar los desafíos planteados por los incendios forestales de nueva generación a la población en las zonas rurales. Se destaca que la reducción de tipos de combustibles homogéneos, en particular los combustibles de matorral alrededor de áreas de WUI aisladas y dispersas, debe ser una línea de intervención prioritaria. Estas acciones deben centrarse en romper la continuidad horizontal de los combustibles y fomentar el desarrollo de mosaicos paisajísticos diversos para promover la resistencia y la capacidad de recuperación frente al fuego. Esto se puede lograr apoyando actividades sostenibles y tradicionales, como el pastoreo extensivo de ganado o acciones silvícolas, lo cual es esencial para la fijación de la población en áreas sociológicamente relevantes como las áreas WUI.[EN] In recent decades, anthropogenic activity has caused remarkable changes in the fire regime attributes in the western Mediterranean Basin, mainly due to the loss of traditional land use derived from rural abandonment, climate change and the absence of adequate forest management strategies, leading to a dense and continuous accumulation of fire-prone biomass. The new fire regime, characterized by an increase in the frequency of extensive and severe wildfires, affects important ecosystem functions and services, with unprecedented impacts at socioeconomic level. This fact is particularly relevant in wildland urban interface (WUI) areas, where extreme wildfires represent a serious threat to human life and assets. In this context, spatial characterization of fire-induced impact, commonly referred to as burn severity, is crucial to provide scientific basis to design appropriated forest management strategies that enhance adaptive responses to current fire regimes. Field methods are considered highly trustworthy for assessing the impacts on vegetation and soils in burned landscapes, though they often lack spatial exhaustiveness to evaluate large wildfires. Therefore, remote sensing methods have emerged as reliable tools for monitoring and quantifying burn severity because of their cost-effectiveness and synoptic nature. In this context, the main objective of this PhD Thesis is the development of new multiscale remote sensing techniques aimed to identify spatial indicators of fire-induced ecological impacts and evaluate the drivers of extreme wildfire behavior under different fire regimes along an Iberian climatic gradient, with particular focus in WUIs due to their high socioeconomic vulnerability. First, we aimed to improve the estimation of burn severity in forest soils, which are critical ecosystem compartments driving ecosystem functions and processes, by linking ecological indicators of burn severity with the spectral signal of very high spatial resolution remote sensing products obtained with unmanned aerial vehicles (UAV) (Articles I & II). Soil burn severity was assessed in the field 1-month after a wildfire through a Composite Burn Soil Index (CBSI) and, a set of individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover and unstructured soil depth). Furthermore, indicative soil properties of fire-induced changes were analyzed: mean weight diameter (MWD), soil moisture content (SMC), and soil organic carbon (SOC). Simultaneously, post-fire multispectral images from the Sentinel-2A MSI satellite sensor, and RGB and multispectral images from a UAV survey were collected. We found that UAV multispectral products had a better performance than RGB products for estimating fire impacts on soils, being more related to integrative indices (ie., CBSI) than to individual indicators (Article I). Depth and ash cover were the most representative indicators of fire effects on soils. The inclusion of spatially and spectrally enhanced remote sensing data through novel remote sensing techniques, such as the fusion of Sentinel-2 and UAV images, significantly improved the prediction of fire-sensitive soil properties highly related to burn severity, mainly SOC (Article II). This approach provides a powerful tool for estimating fire impacts in complex and heterogeneous landscapes affected by mixed severity wildfires, and consequently to identify priority areas where post-fire restoration actions need to be implemented. Once the potential ecological impact of high severity wildfires has been adequately characterized using new remote sensing techniques, we studied fire regime shifts conducive to extreme fire behavior along an Atlantic-Transition-Mediterranean climatic gradient in the Iberian Peninsula, characterized by the occurrence of extreme wildfire events in the last few years. For this purpose, we analyzed (i) the variation patterns of temporal (recurrence and time since last fire) and magnitude (burn severity) fire regime attributes over 35-years using historical wildfire scars derived from Landsat satellite imagery collection, and (ii) the link between fire regime and pre-fire vegetation characteristics controlling extreme fire behavior. We selected eight extreme wildfires occurring during the period 2017-2022, in which we characterized both (i) the pre-fire fuel type and structure by means of image classification techniques and radiative transfer models (RTMs), and (ii) the ecological impact through the differenced Normalized Burn Ratio (dNBR) derived from bi-temporal Sentinel-2 MSI images. Fire recurrence showed the same downward trend along the climatic gradient, burn severity trends significantly differed among Atlantic and Mediterranean areas. The observed shifts in fire regime attributes had a remarkable influence in shaping fuel types and build-up patterns in landscapes prone to extreme fire behavior along the climate gradient but following distinct pathways as a function of the environmental context. In Atlantic areas, recurrent wildfires of low to moderate severity may foster forest transitions to shrubland stable states prone to high burn severity feedback in subsequent wildfires. A similar pattern was observed in Mediterranean and Transition shrublands after the recurrence of high burn severity wildfires. Under all climatic conditions, long times since the last high-severity wildfires may enhance fuel build-up in conifer forests and shrublands highly prone to extreme fire behavior. Finally, we broadened the generated knowledge about the biophysical contexts shaping extreme fire behavior in wildland urban interface areas to identify the scenarios prone to high burn severity in WUI areas due the growing concern about the socio-economic and environmental implications (Article IV). For this purpose, we chose fourteen large wildfires occurred between 2016 and 2021 across Spain that encompassed different WUI typologies. Density and distance between buildings criteria was used to differentiate isolated, scattered, dense and very dense WUIs, while several pre-fire fuel characteristics inside WUI areas were estimated through multispectral satellite imagery, following the methodology used in the Article III. Then, the combined effect of pre-fire fuel and building density patterns was used to recognize the WUI scenarios most prone to extreme fire behavior. Isolated, scattered and sparsely clustered buildings enclosed in a dense shrub matrix were the WUI typologies with the highest fire hazard. Additionally, WUIs dominated by sparse trees with a dense and continuous shrubby understory constituted another critical typology prone to severe fire impacts. We highlighted the role of pre-fire fuel management to minimize the risk to human lives and assets, particularly under increasing human pressure in WUI areas. The results obtained in this PhD Thesis allowed to predict priority scenarios for effective land use planning, wildfire prevention and management strategies, community education, and collaborative efforts in WUI areas, which are essential to address the challenges posed by new-generation wildfires to population in rural areas. We emphasize that the reduction of homogeneous fuel types, particularly shrub fuels around isolated and dispersed WUIs must be a priority intervention line. These actions should focus on breaking the fuel horizontal continuity and encouraging the development of diverse landscape mosaics to foster resistance and resilience to fire. This target can be achieved by supporting sustainable and traditional activities such as extensive livestock grazing or silvicultural actions by work crews, which is essential for population fixation in sociologically relevant areas such as WUIs.Consejería de Educación de la Junta de Castilla y León y por el Fondo Social Europeo (EDU/556/2019)Ministerio de Ciencia e Innovación, y los Fondos de Nueva Generación de la Unión Europea (UE) en el marco del proyecto FIREMAP (TED2021-130925B-I00)Junta de Castilla y León en el marco de los proyectos SEFIRECYL (LE001P17) y WUIFIRECYL (LE005P20)Gobierno del Principado de Asturias, la Fundación para el Fomento de la Investigación Científica Aplicada y la Tecnología en Asturias (FICYT) y el Fondo Europeo de Desarrollo Regional (FEDER) en el marco del proyecto REWLDING (AYUD/2021/51261