Global Estimation of Biophysical Variables from Google Earth Engine Platform

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 role= presentation \u3e2 = 0.87, RMSE = 0.54 m2 role= presentation \u3e2/m2 role= presentation \u3e2 and ME = 0.03 m2 role= presentation \u3e2/m2 role= presentation \u3e2 in the case of LAI, and R2 role= presentation \u3e2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 role= presentation \u3e2 = 0.42 and R2 role= presentation \u3e2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications

Google Earth Engine Applications

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [1]. Following the free availability of Landsat series in 2008, Google archived all the data sets and linked them to the cloud computing engine for open source use. The current archive of data includes those from other satellites, as well as Geographic Information Systems (GIS) based vector data sets, social, demographic, weather, digital elevation models, and climate data layers

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

[EN] 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 featuresSIThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023), and with Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Víctor Fernández-García was supported by a Margarita Salas post-doctoral fellowship from the Ministry of Universities of Spain, financed with European Union-NextGenerationEU and Ministerio de Universidades Fund

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning

The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense research. However, deriving products from optical sensors is typically hampered by cloud contamination and the trade-off between spatial and temporal resolutions. In this work we rely on the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to generate long gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectances. An artificial neural network is trained on PROSAIL inversion to predict monthly biophysical variables at 30 m spatial resolution with associated, realistic uncertainty bars. We emphasize the need for a more thorough analysis of uncertainty, and propose a general and scalable approach to combine both epistemic and aleatoric uncertainties by exploiting Monte Carlo (MC) dropout techniques from the trained artificial network and the propagation of HISTARFM uncertainties through the model, respectively. A model recalibration was performed in order to provide reliable uncertainties. We provide new high resolution products of several key variables to quantify the terrestrial biosphere: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Water Content (CWC) and Fractional Vegetation Cover (FVC) are at 30 m Landsat spatial resolution and over large continental areas. Two study areas are considered: the large heterogeneous but moderately cloud covered contiguous United States, and the homogeneous but largely cloud covered Amazonia. The produced vegetation products largely agree with the test dataset (R = 0.90, RMSE = 0.80 m2/m2 and ME = 0.12 m2/m2 for LAI, and R = 0.98, RMSE = 0.07 and ME = 0.01 for FAPAR) providing low error and high accuracy. Additionally, the validation considers a thorough comparison with operational and largely validated medium resolution products, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Copernicus Global Land Service. Our products presented a good agreement and consistency with both MODIS (R = 0.84 and R = 0.85 for LAI and FAPAR, respectively) and Copernicus (R = 0.92 and R = 0.91 for LAI and FAPAR, respectively). To foster a wider adoption and reproducibility of the methodology we provide an application in GEE and source code at:https://github.com/IPL-UV/ee_BioNet

Herramientas SIG y de teledetección aplicadas al incendio de Las Peñuelas (Huelva): previsión de riesgo y valoración de daños

En la tarde del 24 de junio de 2017 se inició en las inmediaciones de Moguer (Huelva) el conocido como incendio de Las Peñuelas. Durante los tres días en que se mantuvo incontrolado, el fuego arrasó más de diez mil hectáreas, afectando seriamente al Parque Natural de Doñana. En este trabajo se realiza un estudio, haciendo uso de herramientas SIG y teledetección, del riesgo de incendio que existía en el área afectada el día de su inicio, mediante el contraste de algunas metodologías existentes para su cálculo. Asimismo, se realiza una valoración de los daños producidos por el siniestro, en términos de afectación a la economía agraria y a la flora.In the evening of the 24th of June, the fire known as Las Peñuelas forest fire started on the outskirts of Moguer (Huelva). During the three days that it remained out of control, the fire scorched more than ten thousand hectares, seriously damaging the Natural Park of Doñana. In this paper research has been carried out, making use of GIS tools and remote sensing, into the fire hazard that existed on the start date, using some existing methods for its computation. The damage caused by the incident, in terms of its impact on the agricultural economy and on the flora, is also evaluated.Universidad de Sevilla. Grado en Ingeniería Civi

A relação entre a expansão agrícola e os índices biofísicos: uma análise por meio do Google Earth Engine na Bacia Hidrográfica do Alto Parnaíba

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Filosofia e Ciências Humanas. GeografiaO Matopiba apresentou nos últimos anos um notável crescimento devido à expansão da atividade agropecuária na região. A alteração do uso e cobertura do solo tem estreita relação com o clima, que, por sua vez, é de grande importância para o plantio e a vida humana, sendo seu estudo e monitoramento necessário para a preservação e manutenção do ambiente. Considerando a extensão da área em análise, o Sensoriamento Remoto se configura como uma ferramenta essencial neste tipo de estudo. Dessa forma, levando em consideração a relevância deste território para o país, bem como o seu potencial em modificar o balanço energético e hídrico local, este trabalho tem como objetivo verificar, por meio da utilização do Google Earth Engine, se houve variações significativas nos índices biofísicos (Índice de Vegetação por Diferença Normalizada, temperatura da superfície e precipitação) da Bacia Hidrográfica do Alto Parnaíba devido ao aumento de áreas voltadas à agricultura e pastagem, bem como avaliar a utilização desta plataforma no processamento dos dados e suporte à análise. Os resultados obtidos revelam que, simultaneamente à conversão de áreas naturais para uso agrícola, houve elevação da temperatura da superfície e diminuição do índice de vegetação e da precipitação, implicando, possivelmente, na dinâmica da área analisada. Por fim, o GEE mostrou se de grande eficiência, realizando os procedimentos de forma rápida e satisfatória.Matopiba has shown remarkable growth due to the expansion of agricultural activity in the region in recent years. Changings in land use is closely related to the climate, which is also very important for agriculture and human life, and its study and monitoring is necessary for the preservation and maintenance of the environment. Because of its huge land extension under analysis, Remote Sensing is taken as an essential tool for this kind of research. Thus, considering the relevance of this territory for Brazil, as well as, its potential to modify the local energy and water balance, this work aims to verify, by Google Earth Engine, if there were significant variations in the biophysical indices (Normalized Difference Vegetation Index, land surface temperature and precipitation) of Alto Parnaíba Watershed due to increases in areas used to agriculture and pasture. Furthermore, the GEE is evaluated for its use and performance in data processing and analysis support. The results obtained show that, simultaneously with the transformation of natural areas for agricultural use, there was an increase in land surface temperature and a decrease in vegetation index and precipitation, possibly implying in the dynamics of the analyzed area. Finally, the GEE proved to be highly efficient, performing the procedures quickly and satisfactorily

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

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications

Global Estimation of Biophysical Variables from Google Earth Engine Platform

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42 and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications