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)

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

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

Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006

EARLINET: towards an advanced sustainable European aerosol lidar network

The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.Peer ReviewedPostprint (published version

Improving Safety Service Patrol Performance

Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: a) generate alternative feasible SSP beat configurations for a corridor; b)predict incidents and SSP characteristics (e.g., incident frequency, SSP service time) for a given SSP beat configuration; c) estimate performance measures (e.g., SSP response time, number of incidents responded to); and d) identify and present the best SSP configuration(s) through visual aids that facilitate decision making. To generate the incident data needed for the simulation-based SSP-OPT tool, a hierarchical negative binomial model and a hierarchical Weibull model are developed for incident frequencies and incident durations, respectively, based on the historical incident data. These models have been found to be effective in simulating the spatiotemporal distribution of incidents along highway corridors and for generating their attribute data (e.g., incident type, duration). The simulation program employs a discrete event-based approach and requires a few calibration parameters (e.g., SSP vehicle speed). After calibrating the model, the validation results show good agreement with field observations when applied to a sample SSP corridor from I-95. A user interface is created for the SSP-OPT tool in MS Excel to facilitate data entry and visualization of the output metrics for a given corridor. The output includes the list of alternative feasible beat configurations and aggregated performance measures from multiple runs for each individual beat, as well as for each alternative beat configuration spanning the entire corridor. The proposed SSP optimization model could be applied to corridors with or without existing SSP service. The tool will help identify the best beat configurations to minimize SSP response times and maximize SSP response rates for a given number of SSP vehicles on a corridor. Implementing these optimal solutions in the field will result in travel time savings and improve highway safety since the SSP resources will be more efficiently utilized, thus reducing the impacts of incidents on traffic flow

Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain

Fire regimes are strongly dependent on human activities. Understanding the relative influence of human factors on wildfire is an important ongoing task especially in human-dominated landscapes such as the Mediterranean, where anthropogenic ignitions greatly surpass natural ignitions and human activities are modifying historical fire regimes. Most human drivers of wildfires have a temporal dimension, far beyond the appearance of change, and it is for this reason that we require an historical/temporal analytical perspective coupled to the spatial dimension. In this paper, we investigate and analyze spatial–temporal changes in the contribution of major human factors influencing forest fire occurrence, using Spanish historical statistical fire data from 1988 to 2012. We hypothesize that the influence of socioeconomic drivers on wildfires has changed over this period. Our method is based on fitting yearly explanatory regression models—testing several scenarios of wildfire data aggregation—using logit and Poisson generalized linear models to determine the significance thresholds of the covariates. We then conduct a trend analysis using the Mann–Kendall test to calculate and analyze possible trends in the explanatory power of human driving factors of wildfires. Finally, Geographically Weighted Regression Models are explored to examine potential spatial–temporal patterns. Our results suggest that some of the explanatory factors of logistic models do vary over time and that new explanatory factors might be considered (such as arson-related variables or climate factors), since some of the traditional ones seem to be losing significance in the presence–absence models, opposite to fire frequency models. In particular, the wildland–agricultural interface and wildland–urban interface appear to be losing explanatory power regarding ignition probability, and protected areas are becoming less significant in fire frequency models. GWR models revealed that this temporal behavior is not stationary neither over space nor time