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Fire in the forest
\"How destructive or beneficial are forest fires to wildlife? Should we be trying to reduce or increase the amount of fire in forests? How are forest fires controlled, and why does this sometimes fail? What effect will climate change have? These and many other questions are answered in this richly illustrated book, written in non-technical language. The journey starts in the long geological history of fire leading up to our present love-hate relationship with it. Exploring the physics of how a single flame burns, the journey continues through how whole forests burn and the anatomy of firestorms. The positive and negative ecological effects of fires are explored, from plants and wildlife to whole landscapes. The journey ends with how fires are controlled, and a look to the future. This book will be of interest to ecologists, biogeographers and anyone with an interest in forest fires and the role they play\"-- Provided by publisher.
Book
Between Two Fires
From a fire policy of prevention at all costs to today's restored
burning, Between Two Fires is America's history channeled
through the story of wildland fire management. Stephen J. Pyne
tells of a fire revolution that began in the 1960s as a reaction to
simple suppression and single-agency hegemony, and then matured
into more enlightened programs of fire management. It describes the
counterrevolution of the 1980s that stalled the movement, the
revival of reform after 1994, and the fire scene that has evolved
since then. Pyne is uniquely qualified to tell America's fire
story. The author of more than a score of books, he has told fire's
history in the United States, Australia, Canada, Europe, and the
Earth overall. In his earlier life, he spent fifteen seasons with
the North Rim Longshots at Grand Canyon National Park. In
Between Two Fires , Pyne recounts how, after the Great
Fires of 1910, a policy of fire suppression spread from America's
founding corps of foresters into a national policy that manifested
itself as a costly all-out war on fire. After fifty years of
attempted fire suppression, a revolution in thinking led to a more
pluralistic strategy for fire's restoration. The revolution
succeeded in displacing suppression as a sole strategy, but it has
failed to fully integrate fire and land management and has fallen
short of its goals. Today, the nation's backcountry and
increasingly its exurban fringe are threatened by larger and more
damaging burns, fire agencies are scrambling for funds,
firefighters continue to die, and the country seems unable to come
to grips with the fundamentals behind a rising tide of megafires.
Pyne has once again constructed a history of record that will shape
our next century of fire management. Between Two Fires is
a story of ideas, institutions, and fires. It's America's story
told through the nation's flames.
eBook
Fearsome forest fires
\"Discusses the science behind forest fires and what to do to stay safe from them\"-- Provided by publisher.
Book
Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
Journal Article
Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images
With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.
Journal Article
Fire : nature and culture
\"Fire has been an integral feature of our planet for over 400 million years. It has defined human culture from the beginning; it is something without which we cannot survive. For while fire is among the most destructive forces on earth, it has equally tremendous powers of cleansing renewal and controlled energy. In this book the author delivers a masterclass history of fire and its use by humanity, explaining how fire has always been at the core of how people have made their world habitable, whether hunting, foraging, farming, herding or urbanizing, and of course in managing nature reserves. Fire was deployed in the bast by aboriginal communities, and early agricultural societies began to control and contain fire and fuel. But our mastery of the science and art of fire has not given us absolute power: fire disasters have altered the course of history, and unexpected fires that begin as the result of other disasters can have shocking efffects. In addition, wildfires are a crucial component of natural regeneration. The past 200 years has also seen the growth of a massive new role of combustibles in the form of fossil biomass: 'people burn fuels from the geological past and release their effluents into the geological future. The present they overload with noxious emissions and greenhouse gases.' New combustion practices have radically changed the world's ecological balance.
Book
A Forest Fire Detection System Based on Ensemble Learning
Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.
Journal Article
Burning planet : the story of fire through time
Raging wildfires have devastated vast areas of California and Australia in recent years, and predictions are that we will see more of the same in coming years, as a result of climate change. But this is nothing new. Since the dawn of life on land, large-scale fires have played their part in shaping life on Earth. Andrew Scott tells the whole story of fire's impact on our planet's atmosphere, climate, vegetation, ecology, and the evolution of plant and animal life.-- Source other than Library of Congress.
Book
Forest Fire Occurrence Prediction in China Based on Machine Learning Methods
Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.
Journal Article