Aplicaciones de machine learning para el uso sustentable de recursos naturales

Se propone una investigación para predecir la ocurrencia de incendios forestales basada en el entrenamiento de Modelos de Machine Learning. Se utiliza para el entrenamiento de los modelos, datos de registros históricos provistos por las propias Asociaciones de Bomberos Voluntarios, datos del Servicio Meteorológico Nacional (SMN) e imágenes satelitales provistas por la NASA. Se propone extender la solución para abarcar el monitoreo de áreas en riesgo, mediante dispositivos de IoT en puntos fijos o móviles, y equipados con sensores y cámaras. El procesamiento de las imágenes se propone realizar mediante algoritmos de reconocimiento de imágenes para enviar alertas de posibles focos de incendios.Red de Universidades con Carreras en Informátic

Modeling Naturally Occurring Wildfires Across the US Using Niche Modeling

Wildfires can cause significant damage to an area by destroying forested and agricultural areas, homes, businesses, and leading to the potential loss of life. Climate change may further increase the frequency of wildfires. Thus, developing a quick, simple, and accurate method for identifying key drivers that cause wildfires and modeling and predicting their occurrence becomes very important and urgent. Various modeling methods have been developed and applied for this purpose. The objective of this study was to identify key drivers and search for an appropriate method for modeling and predicting natural wildfire occurrence for the United States. In this thesis, various vegetation, topographic and climate variables were examined and key drivers were identified based on their spatial distributions and using their correlations with natural wildfire occurrence. Five models including General Linearized Models (GLM) with Binomial and Poisson distribution, MaxEnt, Random Forests, Artificial Neural Networks, and Multiple Adaptive Regression Splines, were compared to predict natural wildfire occurring for seven different climate regions across the United States. The comparisons were conducted using three datasets including LANDFIRE consisting of thirteen variables including characteristics of vegetation, topography and disturbance, BIOCLIM containing climate variables such as temperature and precipitation, and composite data that combine the most important variables from LANDFIRE and BIOCLIM after the multicollinearity test of the variables done using variance inflation factor (VIF). This results of this study showed that niche modeling techniques such as MaxEnt, GLM with logistic regression (LR), and binomial distribution were an appropriate choice for modeling natural wildfire occurrence. MaxEnt provided highly accurate predictions of natural wildfire occurrence for most of seven different climate regions across the United States. This implied that MaxEnt offered a powerful solution for modeling natural wildfire occurrence for complex and highly specialized systems. This study also showed that although MaxEnt and GLM were quite similar, both models produced very different spatial distributions of probability for natural wildfire occurrence in some regions. Moreover, it was found that natural wildfire occurrence in the western regions was more influenced by precipitation and drought conditions while in the eastern regions the natural wildfire occurrence was more affected by extreme temperature

A multi-century perspective of the Sala mega-fire : understanding risks for future large fires in Sweden

In the summer of 2014, the Sala municipality in central Sweden registered the largest fire in modern Swedish history. The circumstances under which the fire took place highlight the complex interactions between the ignition as well as the spread of fire, and human activities along with weather. Although the role of human activities and climate are of critical importance in shaping modern fire activity, their joint effects remain largely unstudied in Northern Europe. The main aim of this thesis was to investigate the impact of landscape properties (natural and human-related) and climate patterns on forest fires at different spatial and temporal scales. Dendrochronological study (Paper II) suggested that in the past the lack of major firebreaks, homogenization of forests due to its long-term management, and a long period without fires might have contributed to the occurrence of the exceptionally large 2014 mega-fire in Sala. A study of modern fire activity (Paper I) showed that a combination of human-related ignitions, weather conditions controlling fire spread, and vegetation composition are the main drivers of fire activity in Sweden. At the scale of the European boreal zone (Paper IV), forest fire activity remains strongly connected to the annual climate variability. Predictions of the future area burned in Sweden (Paper III) indicated that changes in climate would lead to an increase in area burned, with changes in vegetation leading either to further increase or mitigation of this increase to a certain degree

LandTrendr smoothed spectral profiles enhance woody encroachment monitoring

Secondary succession (SS) is one of the main consequences of the abandonment of agricultural and forestry practices in rural areas, causing -among other processes- woody encroachment on former pastures and croplands. In this study we model and monitor the spatial evolution of SS over semi-natural grassland communities in the mountain range of the Pyrenees in Spain, during the last 36 years (1984-2019). Independent variables for ‘annual-based’ and ‘period-based’ modeling were drawn from a suite of Surface Reflectance Landsat images, LandTrendr (LT)-algorithm-adjusted images and LT outputs. Support vector machine (SVM) classifiers were trained and tested using all possible variable combinations of all the aforementioned datasets. The best modeling strategy involved yearly time series of LT-adjusted Tasseled Cap Brightness (TCB) and Wetness (TCW) axes as predictors, attaining a F1-score of 0.85, a Matthew Correlation Coefficient (MCC) of 0.67 and an AUC 0.83. Woodlands encroached above 480, 000 ha of grasslands and crops during the study period. A model using LT outputs for the whole period also denoted good performance (F1-score = 0.85, MCC = 0.75) and estimated a similar area of woodland expansion (~509, 000 ha), but this ‘period’ approach was unable to provide temporal information on the year or the encroachment dynamics. Our results suggest an overall proportion of 66% for the Pyrenees being affected by SS, with higher intensity in the west-central part, decreasing towards the eastern end. © 2021 The Author

Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula

Background. Lightning is the most common origin of natural fires, being strongly linked to specific synoptic conditions associated with atmospheric instability, such as dry thunderstorms; dry fuels are required for ignition to take place and for subsequent propagation. Aims. The aim was to predict the daily probability of ignition by exploiting a large dataset of lightning and fire data to anticipate ignition over the entire Iberian Peninsula. Methods. We trained and tested a machine learning model using lightning strikes (>17 million) in the period 2009–2015. For each lightning strike, we extracted information relating to fuel condition, structural features of vegetation, topography, and the specific characteristics of the strikes (polarity, intensity and flash density). Key results. Naturally triggered ignitions are typically initiated at higher elevations (above 1000 m above sea level) under conditions of low dead fuel moisture (<10–13%) and moderate live moisture content (Drought Code > 300). Negative-polarity lightning strikes (−10 kA) appear to trigger fires more frequently. Conclusions and implications. Our approach was able to provide ignition forecasts at multiple temporal and spatial scales, thus enhancing forest fire risk assessment systems

Previsão de vulnerabilidade a incêndios florestais utilizando regressão logística e redes neurais artificiais : um estudo de caso no Distrito Federal brasileiro

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2017.Incêndios florestais são um problema global e queimam milhões de hectares de vegetação nativa todos os anos. O Cerrado brasileiro é a savana neotropical mais rica em biodiversidade do mundo, e uma das regiões mais afetadas por incêndios, sendo considerado um ecossistema tolerante ao fogo. Apesar da adaptação do bioma ao fogo, a alta frequência de incêndios trazida pela ocupação humana tem danificado o ecossistema mais rápido do que ele é capaz de se recuperar. O combate a incêndios é custoso e, portanto, medidas de prevenção de incêndios são a melhor maneira de evitar seus danos em longo prazo. Prever a distribuição espacial da ocorrência de incêndios florestais é um passo importante para a realização do manejo do fogo. Para isto, podem ser utilizados modelos que relacionem a ocorrência do fogo às variáveis que o influenciam. Neste estudo, dois modelos distintos de previsão — Regressão Logística (RL) e uma Rede Neural Artificial (RNA) — foram aplicados à região do Distrito Federal brasileiro, que se encontra inserida dentro do bioma Cerrado. Produtos de área queimada baseados em imagens LANDSAT foram utilizados para gerar a variável dependente, e nove outras variáveis espacialmente explícitas e de origem antropogênica ou ambiental foram utilizadas como variáveis independentes. Os modelos foram otimizados em função da melhor medida de Area under Receiver Operating Characteristic (AUROC, ou simplesmente AUC) a partir da seleção de atributos, e posteriormente validados utilizando dados reais de áreas queimadas. Os modelos mostraram performances similares, mas o modelo utilizando a RNA demonstrou melhor AUC (0.7755), e melhor acurácia ao classificar áreas não queimadas (73.39%), porém pior acurácia média (66.55%) e ao classificar áreas queimadas, para as quais o modelo LR apresentou o melhor resultado (65.24%). Adicionalmente, foi comparada a importância de cada variável aos modelos, contribuindo para o conhecimento das causas principais de incêndios na região. As variáveis demonstraram importâncias similares em ambos os modelos utilizados, e as variáveis de maior importância foram a elevação do terreno e o tipo de uso do solo. Os resultados demonstraram bons desempenhos de todos os modelos testados, mas recomenda-se a execução de mais estudos similares mais detalhados em outras áreas de na savana Brasileira, dado que ainda são poucos os estudos deste tipo.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Wildfires are a global problem, burning millions of hectares of natural forests every year. The Brazilian Cerrado is the richest neotropical savanna of the world in regards to biodiversity, and one of the regions most affected by fires, and also considered a firedependent ecosystem. Despite being adapted to the occurrence of fire, the high frequency of wildfires in the region due to human occupation is damaging the ecosystem faster than it can recover. Fighting fires is costly, and therefore the best way to avoid damages in the long-term is through prevention techniques. Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. For that purpose, models that relate the occurrence of fire to certain variables can be used. In this work, we applied two distinct prediction models — Logistic Regression (LR) and an Artificial Neural Network (ANN) — to the region of Brazil’s Federal District, located inside the Brazilian Cerrado, the largest savanna in South America and the world’s richest Neotropical Savanna. We used LANDSAT based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors were used as the explanatory variables. The models were optimized via feature selection for best Area Under Receiver Operating Characteristic Curve (AUROC) and then validated with real burn area data. The models had similar performance, but the ANN model showed a better AUC value (0.7755) and better accuracy when evaluating exclusively nonburned areas (73.39%), while it had worse accuracy overall (66.55%) and when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. Variables had similar contributions to the models, and the main driving aspects of the distribution of burned areas in the region were the land use type and elevation. The results showed good performance for both models tested, but further research regarding wildfire in the Brazilian savanna is recommended, as such studies are still scarce

Integrating geospatial wildfire models to delineate landscape management zones and inform decision-making in Mediterranean areas

Despite the abundant firefighting resources deployed to reinforce the fire exclusion policy, extreme events continue to cause substantial losses in Mediterranean regions. These catastrophic wildfires question the merely-reactive response, while science-based decision-making advocates for a paradigm shift towards a long-term solution to coexist with fire. Comprehensive management solutions integrate multiple efforts to minimize the number of escaped wildfires in fire ignition hotspots, restrict large fire spread across the landscape, and prevent losses to valued resources and assets. This study develops a wildfire management zone (WMZ) delineation framework to inform decision-making in fire-prone Mediterranean landscapes. First, we combined modeling outcomes of wildfire occurrence, initial attack success, and wildfire transmission to communities to segment the landscape in WMZ blocks. We assumed the worst-case scenario in terms of fire simultaneity and weather conditions to implement the models. The geospatial outcomes were assembled and classified into four primary archetypes, and we then designated the most suitable risk mitigation strategies for each management unit. The WMZs included (1) comprehensive management, (2) human ignition prevention, (3) intensive fuel management, and (4) fire reintroduction areas. Finally, we downscaled within zones to assign specific management prescriptions to the different areas. The results were presented in a set of cross-scale maps to assist in designing risk management plans and raise social awareness. The methodological framework developed in this study may be valuable to help mitigate risk in fire-prone Mediterranean areas, but also in other regions in which similar total suppression policies fail to reduce catastrophic wildfire losses.This work has been financed by the Spanish Ministry of Economy and Competitiveness, postdoctoral ‘Juan de la Cierva Formación’ research grant (FJCI-2016-31090) awarded by Marcos Rodrigues. The work was partially funded by the projects FirEUrisk “DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT”. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003890; and CLIMARK “Forest management promotion for climate mitigation through the design of a local market of climatic credits” (LIFE16 CCM/ES/000065)