Forest fire simulator system for emergency resources management support

Europe suffers approximately 65,000 fires every year, which burn, on average, half a million hectares of forest areas [1]. The main direct effect of forest fires is the destruction of the natural landscape and the consequent loss of ecosystem service that have drastic economic impact, but mainly and much more important, fires also result in the loss of human lives every year. Although being forest fires a problem present in all EU members, the most affected areas to this hazards are the southern countries due to their climatological conditions. All affected countries invest lots of resources to minimize fire damages, but many times when dealing with large fires, regional and national disaster management units are lack of efficient and reliable tools to help wildfire analysts. In this work, we describe a process to generate on-line wildfire simulations coupled with the regional weather forecast service (Servei Meteorològic de Catalunya, SMC) and the helicopter company (Helipistas S.L) who provides isochronous perimeters of the fire behaviour in a certain moment of the emergency and how both of this data sources feed the inputs for the simulation process.Europa sufre aproximadamente 65,000 incendios cada año, de media, medio millón de hectáreas forestales[1]. El principal efecto de los fuegos forestales es la destrucción de la superfície natural y como consecuencia la pérdida del ecosistema y el gran impacto económico, pero principamente y de manera mucho más importante el fuego tambien repercute en la pérdida de vidas humanas año tras año. Los fuegos forestales además de ser un problema para los miembros de la UE, se ven repercutidos, especialmente los paises del sur debido a sus condiciones climatológicas. Todos estos paises afectados invierten gran cantidad de recursos para minimizar estos efectos. Generalmente cuando se trata de grandes incendios forestales, las unidades de mando de estos medios de exinción a nivel regional y nacional se ven necesitados de herramientas eficientes y útiles para el análisis de la predicción del comportamiento de estos grandes incendios forestales. En este trabajo, describimos un sistema de predicción de incendios forestales acoplado con el servicio meteorológicos de catalunya (SMC) y la empresa de helicópteros (Helipistas S.L) los cuales proveen de los perímetros del incendio en un instante de tiempo de la emergencia y cómo estas dos fuentes de datos se anexan al proceso de simulación.Europa pateix aproximadament 65,000 incendis cada any, de mitja, cada mig-milió d'hectàrees forestals[1]. El principal efecte dels focs forestals es la destrucció de la superfície natural i com a conseqüència la pèrdua de l'ecosistema i el gran impacte econòmic, però principalment i de manera molt més important el foc, també, repercuteix en la pèrdua de vides humanes any rere any. Els focs forestals a més a més de representar un problema pels països membres de la UE, es veuen afectats els països del Sud degut a les seves condicions climatològiques. Tots aquests països afectats inverteixen grans quantitat de recursos per a minimitzar aquests efectes. Generalment quan es tracta de grans incendis forestals, les unitats de comandament d'aquests medis d'extinció a nivell regional i nacional es veuen necessitats d'eines útils i eficients per a l'anàlisis de la predicció en el comportament dels grans incendis forestals. En aquest treball, descrivim un sistema de predicció d'incendis forestals acoblat amb el servei meteorològic de Catalunya (SMC) i l'empresa d'helicòpters (Helipistas S.L) els quals proveïxen dels perímetres de l'incendi en un instant de temps de l'emergència i com aquestes dos fonts de dades annexen al procés de simulació

Forest fire propagation prediction based on overlapping DDDAS forecasts

International Conference on Computational Science, ICCS 2015 – Computational Science at the Gates of NatureForest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints.This research has been supported by the Ministerio de Economía y Competitividad (MECSpain) under contract TIN2011-28689-C02-01 and the Catalan government under grant 2014- SGR-576

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

Front propagation in random media.

244 p.This PhD thesis deals with the problem of the propagation of fronts under random circumstances. Astatistical model to represent the motion of fronts when are evolving in a media characterized bymicroscopical randomness is discussed and expanded, in order to cope with three distinctapplications: wild-land fire simulation, turbulent premixed combustion, biofilm modeling. In thestudied formalism, the position of the average front is computed by making use of a sharp-frontevolution method, such as the level set method. The microscopical spread of particles which takesplace around the average front is given by the probability density function linked to the underlyingdiffusive process, that is supposedly known in advance. The adopted statistical front propagationframework allowed a deeper understanding of any studied field of application. The application ofthis model introduced eventually parameters whose impact on the physical observables of the frontspread have been studied with Uncertainty Quantification and Sensitivity Analysis tools. Inparticular, metamodels for the front propagation system have been constructed in a non intrusiveway, by making use of generalized Polynomial Chaos expansions and Gaussian Processes.bcam:basque center for applied mathematic

Front Propagation in Random Media

This PhD thesis deals with the problem of the propagation of fronts under random circumstances. A statistical model to represent the motion of fronts when are evolving in a media characterized by microscopical randomness is discussed and expanded, in order to cope with three distinct applications: wild-land fire simulation, turbulent premixed combustion, biofilm modeling. In the studied formalism, the position of the average front is computed by making use of a sharp-front evolution method, such as the level set method. The microscopical spread of particles which takes place around the average front is given by the probability density function linked to the underlying diffusive process, that is supposedly known in advance. The adopted statistical front propagation framework allowed a deeper understanding of any studied field of application. The application of this model introduced eventually parameters whose impact on the physical observables of the front spread have been studied with Uncertainty Quantification and Sensitivity Analysis tools. In particular, metamodels for the front propagation system have been constructed in a non intrusive way, by making use of generalized Polynomial Chaos expansions and Gaussian Processes.The Thesis received funding from Basque Government through the BERC 2014-2017 program. It was also funded by the Spanish Ministry of Economy and Competitiveness MINECO via the BCAM Severo Ochoa SEV-2013-0323 accreditation. The PhD is fundend by La Caixa Foundation through the PhD grant “La Caixa 2014”. Funding from “Programma Operativo Nazionale Ricerca e Innovazione” (PONRI 2014-2020) , “Innotavive PhDs with Industrial Characterization” is kindly acknowledged for a research visit at the department of Mathematics and Applications “Renato Caccioppoli” of University “Federico II” of Naples

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin