46 research outputs found
Modeling the influence of eucalypt plantation on wildfire occurrence in the Brazilian savanna biome
In the last decades, eucalypt plantations are expanding across the Brazilian savanna, one of the most frequently burned ecosystems in the world. Wildfires are one of the main threats to forest plantations, causing economic and environmental loss. Modeling wildfire occurrence provides a better understanding of the processes that drive fire activity. Furthermore, the use of spatially explicit models may promote more effective management strategies and support fire prevention policies. In this work, we assessed wildfire occurrence combining Random Forest (RF) algorithms and cluster analysis to predict and detect changes in the spatial pattern of ignition probability over time. The model was trained using several explanatory drivers related to fire ignition: accessibility, proximity to agricultural lands or human activities, among others. Specifically, we introduced the progression of eucalypt plantations on a two-year basis to capture the influence of land cover changes over fire likelihood consistently. Fire occurrences in the period 2010–2016 were retrieved from the Brazilian Institute of Space Research (INPE) database. In terms of the AUC (area under the Receiver Operating Characteristic curve), the model denoted fairly good predictive accuracy (AUC ≈ 0.72). Results suggested that fire occurrence was mainly linked to proximity agricultural and to urban interfaces. Eucalypt plantation contributed to increased wildfire likelihood and denoted fairly high importance as an explanatory variable (17% increase of Mean Square Error [MSE]). Nevertheless, agriculture and urban interfaces proved to be the main drivers, contributing to decreasing the RF’s MSE in 42% and 38%, respectively. Furthermore, eucalypt plantations expansion is progressing over clusters of high wildfire likelihood, thus increasing the exposure to wildfire events for young eucalypt plantations and nearby areas. Protective measures should be focus on in the mapped Hot Spot zones in order to mitigate the exposure to fire events and to contribute for an efficient initial suppression rather than costly firefighting.This research was funded by the Erasmus+ Programme student scholarship (grant to Luiz Felipe de Castro Galizia). This work has been financed by the Ministerio de Economía y Competitividad; is a postdoctoral ‘Juan de la Cierva Formación’ research grant awarded by Marcos Rodrigues (FJCI-2016-31090)
Review and new methodological approaches in human-caused wildfire modeling and ecological vulnerability: Risk modeling at mainland Spain
En las últimas décadas, las autoridades en materia de incendios han fomentado la investigación acerca de los factores desencadenantes del fuego, parámetro decisivo para lograr un entendimiento mayor de los patrones de la ocurrencia de incendios y mejorar las medidas preventivas. Existe por tanto una necesidad de mejorar y actualizar los enfoques metodológicos para el modelado de incendios forestales, teniendo en cuenta no sólo algoritmos innovadores, sino también la mejora y/o superación de los métodos clásicos de regresión. Por otra parte, es también imprescindible fomentar la evaluación de los posibles daños potenciales en los ecosistemas naturales, promoviendo así la conservación de los servicios de valor económico, ambiental, cultural y estético que éstos proporcionan a la sociedad. El objetivo principal de esta tesis doctoral es explorar nuevos métodos para el modelado de la causalidad humana en incendios forestales así como de los efectos adversos sobre las comunidades vegetales potencialmente afectadas. El modelado de la causalidad humana se ha realizado a partir de métodos de aprendizaje artificial y de técnicas de regresión geográficamente ponderada. Estas técnicas permiten por una parte el ajuste de modelos de probabilidad de ocurrencia espacialmente explícitos y, por otra, el estudio de la variabilidad espacial de los factores explicativos. La estimación de la vulnerabilidad de la vegetación frente al fuego, se ha llevado a cabo utilizando un enfoque cuantitativo, que permita superar los métodos existentes, que, si bien pueden ser útiles en algunas áreas de la gestión del territorio, son inadecuados para otros tipos de análisis, tales como la estimación de las pérdidas económicas inducidas por el fuego como consecuencia de la interrupción de los servicios ambientales (por ejemplo, la madera, la caza, y la recolección de setas). Para abordar el análisis de la vulnerabilidad se propone un método basado en la estimación del tiempo de recuperación de las comunidades vegetales tras el fuego, desarrollado mediante álgebra de mapas en entorno SIG. Los resultados indican que la utilización de métodos de aprendizaje artificial (concretamente el algoritmo Random Forest) supone una mejora sustancial respecto a los métodos clásicos de regresión, si bien parece que existe cierta incertidumbre en los modelos desarrollados, relacionada principalmente con la calidad de los datos de ocurrencia. Además, la aplicación de modelos GWR ha revelado la existencia de una elevada heterogeneidad espacial en la relación y capacidad explicativa de los factores relacionados con la ocurrencia de incendios con origen antrópico. Por otra parte, la aplicación del modelo propuesto para la estimación cuantitativa de la vulnerabilidad ecológica sugiere que la capacidad de respuesta de la vegetación se encuentra estrechamente relacionada con la estrategia reproductiva de las especies afectadas.Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. There is therefore a need to improve and update the methodological approaches for modeling forest fires, taking into account not only innovative algorithms, but also improving and/or overcoming classical regression methods. On the other hand it is also essential to encourage the assessment of potential damage on natural ecosystems, promoting the conservation of its economic, environmental, cultural and aesthetic assets they provide to society. The main objective of this PhD thesis is to explore new methods for modeling human causality in forest fires and adverse effects on the plant communities potentially affected. Human causality modeling was carried out from machine learning methods and geographically weighted regression techniques. These procedures allow the adjustment spatially explicit probability models of occurrence and, secondly, the study of the spatial variability of wildfire explanatory factors. The estimation of the vulnerability of vegetation to fire was carried out using a quantitative approach to overcome current methods, which, while they may be useful in some areas of land management, are inadequate for other types of analysis, such as estimating economic losses induced by interrupting ecosystem services (e.g., wood, hunting, and gathering mushrooms). To address the vulnerability a method based on evaluating the recovery time of plant communities after the fire using a GIS map algebra approach is proposed. The results suggest that the use of machine learning methods (specifically the Random Forest algorithm) represents a substantial improvement over traditional methods of regression, although it appears that there is some uncertainty in the models, primarily related to the quality of ignition. Furthermore, the application of GWR models has revealed the existence of a high spatial heterogeneity in the relationship and explanatory power of the factors related to the occurrence of anthropogenic fires. Moreover, the application of the proposed model for the quantitative estimation of ecological vulnerability suggests that the responsiveness of vegetation is closely related to the reproductive strategy of the fire-affected species
Uncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations
Biomass and carbon content are essential indicators for monitoring forest ecosystems and their role in climate action, but their estimation is not straightforward. A typical approach to solve these limitations has been the estimation of tree or stand biomass based on forest inventory data, using either allometric equations or biomass expansion factors. Many allometric equations exist, but very few studies have assessed how the calculation methods used may impact outcomes and how this impact depends on genera, functional group, climate or forest structural attributes. In this study we evaluate the differences in biomass estimates yielded by the most widely used biomass equations in Spain. We first quantify the discrepancies at tree level and among the main forest tree species. We observed that the divergences in carbon estimations between different equations increased with tree size, especially in the case of hardwoods and for diameters beyond the range used to calibrate the equations. At the plot level, we found considerable differences between the biomass values predicted using different methods (above 25% in one out of three plots), which constitutes a warning against the uncritical choice of equations to determine biomass or carbon values. The spatial representation of the differences revealed geographical patterns related to the dominance of fast-growing species such as Eucalyptus or Pinus pinaster, with a minor effect of forest structure, and almost no effect of climate. Finally, we observed that differences were mostly due to the data source rather than the modelling approach or equation used. Based on our results, BEF equations seem a valid and unbiased option to provide nation-level estimations of carbon balance, although local equations should preferably be used if they are available for the target area.This work was funded by the LIFE CLIMARK project (LIFE16 CCM/ES/000065) and by the Spanish State Research Agency through the UMBRACLIM project (PID2019-111781RB-I00). The authors would like to thank Lluis Coll for helpful discussions during the conceptualization of the study and Pere Gelabert for his help in map design and creation. Special thanks to Miquel de Caceres, Jordi Vayreda and Jordi Martinez-Vilalta for the creation of the Catalan Forestry Lab and the gathering and release of the IEFC data
Análisis de indicadores urbanos como base para la definición de las zonas de bajas emisiones. Zaragoza como caso de estudio.
En los últimos años, nuestro modelo de desplazamiento ha priorizado el uso del vehículo privado. Como consecuencia, las ciudades han empeorado su calidad del aire llegando a niveles de riesgo para la salud. Desde Europa se ha tratado de establecer unas medidas que regulen las emisiones de gases de efecto invernadero contribuyendo a mitigar los efectos del cambio climático. En España se han aprobado unas estrategias que trabajan en línea con las planteadas en Eu-ropa. Una de esas estrategias es la delimitación de zonas de bajas emisiones (ZBE). Ciudades como Madrid o Barcelona ya las han llevado a la práctica generado gran controversia. La falta de un documento general que las regule es una de las razones. Este Trabajo trata de dar solución a esta carencia a través del análisis de indicadores urbanos que puedan servir como base para la delimitación de las ZBE a nivel general. El estudio se ha realizado en Zaragoza por presentar características socioeconómicas que la convierten en un modelo extrapolable al resto de ciudades. Para ello, se han estudiados qué factores afectan por su naturaleza a la calidad del aire, utilizando para ello una metodología estadística que permita establecer relaciones entre ellos. De esta forma, se han obtenido como resultado la identificación de zonas que son susceptibles de ser reguladas y que corresponde actualmente con áreas con problemas de movilidad. Las ZBE deberán contar con un seguimiento de los indicadores urbanos para revisar su adecuación. Se plantea como desarrollo futuro a este trabajo el estudio de una metodología que ayude a establecer unas directrices generales de supervisión y seguimiento.<br /
ANALISIS GEOESTADÍSTICO DE LA PREVALENCIA DE DEPRESION EN RELACION A FACTORES SOCIOECONOMICOS Y MEDIOAMBIENTALES EN LA COMUNIDAD AUTONOMA DE ARAGÓN
En los últimos años se viene considerando la depresión como la nueva gran epidemia del siglo XXI. La OMS señala la depresión como enfermedad mental frecuente en todo el mundo y calcula que la padecen unos 300 millones de personas actualmente. EL presente estudio analiza la pre-valencia de la depresión en Aragón en el año 2010 a través de las tecnologías de la información geográfica y los SIG para encontrar las posibles relaciones de esta con los determinantes de sa-lud. Para ello todo el conjunto de datos disponible se ha agregado a una única unidad espacial, la Zona Básica de Salud y se han implementado los modelos global y logístico (GWRL) para el análi-sis estadístico. El modelo GWLR ha encontrado relaciones significativas entre los determinantes de la salud de origen socioeconómico con el porcentaje de paro e índice de feminidad
Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)
Introduction: The global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management.
Objective: To identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015–2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective.
Materials and methods: First, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables.
Results: The GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men’s prevalence, while women’s models attained more consistent and higher explanatory performance.
Conclusion: Modeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken
Simulación del comportamiento del fuego en la comarca Alto Gállego (Huesca).
En el desarrollo de este Trabajo Fin de Grado se ha caracterizado el territorio de la comarca Alto Gállego desde el punto de vista de los condicionantes del comportamiento del fuego: topografía, combustible y meteorología. A partir de diversas informaciones relativas a estos tres dominios se han realizado diversas simulaciones bajo las condiciones más frecuentemente asociadas a la ocurrencia de incendios forestales (vientos de componente sur con velocidades medias entorno a 15m/s). Las simulaciones resultantes permiten evaluar el grado de exposición (probabilidad de que una determinada localización se vea afectada por el fuego) y la peligrosidad del mismo (velocidad con la que el frente de llamas recorrería una ubicación potencialmente afectada). Los resultados revelan un patrón espacial contrastado con la mayor parte de la comarca en situaciones de baja exposición o peligrosidad, si bien se han identificado zonas potencialmente en riesgo donde no solo la probabilidad de quema es mayor, sino que además se encuentran expuestas infraestructuras diversas: vías de comunicación, tendidos eléctricos o incluso asentamientos de población, como lo son el de Sallent de Gállego o Yebra de Basa. Este, se apoyará en técnicas de análisis espacial mediante Sistemas de Información Geográfica (SIG), y algoritmos específicos para la simulación de la propagación a la escala del paisaje característico del territorio. Palabras clave: incendio forestal, simulación, comportamiento del fuego, probabilidad de quema, exposición.<br /
Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)
IntroductionThe global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management.ObjectiveTo identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015–2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective.Materials and methodsFirst, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables.ResultsThe GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men’s prevalence, while women’s models attained more consistent and higher explanatory performance.ConclusionModeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken
Sistema automático de detección temprana de afecciones en viñedo: Mildiu y Oidio
En la actualidad, la viticultura se está viendo transformada debido a las exigencias crecientes del mercado, dando lugar a un sistema de producción no convencional. Esto ha impulsado a los agri-cultores en la búsqueda de un producto de calidad, siendo necesario el control de las grandes enfermedades que afectan los viñedos: Mildiu y Oidio. De esta necesidad, nace la modelización de las enfermedades para mejorar su prevención evitando así pérdidas cuantiosas en las cose-chas. En este trabajo se analizan ambas enfermedades y se proponen dos modelos de prevención para el Mildiu y el Oidio en la Vid, basados en la espacialización de su desarrollo. Los resultados se presentan en un visualizador diseñado para ser un futuro sistema de alerta agrícola
Understanding fire regimes in Europe
Wildland fire effects are strongly associated with fire regime characteristics. Here, we developed the first
European pyrogeography based on different fire regime components to better understand fire regimes across the continent. We identified four large-scale pyroregions: a non-fire-prone (NFP) pyroregion featuring nominal fire activity across central and northern Europe; a cool-season fire (CSF) pyroregion scattered throughout Europe; a fire-prone (FP) pyroregion extending mostly across southern Europe; and a highly fire-prone (HFP) pyroregion spanning across northern Portugal, Sicily, and western Balkans. Land cover analysis indicates that pyroregions were first shaped by vegetation and then by anthropogenic factors. On interannual timescales the spatial extent of pyroregions was found to vary, with NFP
showing more stability. Interannual correlations between climate and burned area, fire frequency, and the length of fire period exhibited distinct patterns, strengthening in fire-prone pyroregions (FP and HFP) and weakening in NFP and CSF. Proportion of cool-season fires and large fires were related to fuel accumulation in fire-prone pyroregions. Overall, our findings indicate that such a pyrogeography should allow a more accurate estimate of the effects of climate on fire regimes while providing an appropriate framework to better understand fire in Europ