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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
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
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
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.
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
Fearsome forest fires
\"Discusses the science behind forest fires and what to do to stay safe from them\"-- Provided by publisher.
Book
High‐severity and short‐interval wildfires limit forest recovery in the Central Cascade Range
Increasing forest fuel aridity with climate change may be expanding mid‐to‐high‐elevation forests' vulnerability to large, severe, and frequent wildfire. Long‐lasting changes in forests' structure and composition may occur if dominant tree species are poorly adapted to shifting wildfire patterns. We hypothesized that altered fire activity may lower existing forest resilience and disrupt the recovery of upper‐montane and subalpine conifer forest types. We empirically tested this hypothesis by quantifying post‐fire forest structure and conifer tree regeneration after spatially large, severe, and rapidly repeated wildfires (<12‐yr interval) in the Central Cascade Range in the U.S. Pacific Northwest. Post‐fire conifer regeneration was generally very poor among plots that experienced either a single high‐severity fire or rapid reburn, driven primarily by lack of proximate seed source. Pre‐fire dominant, shade‐tolerant species' abundance was highly negatively correlated with increasing seed source distances and dry, exposed post‐fire environmental conditions. In rapidly reburned plots, the order of burn severity was critical and promoted establishment of all conifer species, if low‐then‐high severity, or primarily fire‐adapted pines, if high‐then‐low severity. Our findings suggest that these forests, affected by expansive high‐severity and/or short‐interval wildfire, may transition into a patchy, low‐density, pine‐dominated forest state under future warming trends. These emerging, early seral ecosystems will incorporate more fire‐adapted tree species, lower tree densities, and more non‐forest patches than prior forests, likely expanding their resilience to anticipated increases in fire frequency. If future larger, more severe, and more frequent wildfire patterns manifest as expected in the Cascade Range, previously denser, moist mid‐to‐high‐elevation forests may begin resembling their drier, lower‐elevation mixed‐conifer counterparts in structure and composition.
Journal Article
Extreme wildfire : smoke jumpers, high-tech gear, survival tactics, and the extraordinary science of fire
In this book, young readers will learn about the ecological impacts of wildfires, the ins and outs of fire science including tactics for prevention and containment, cutting-edge technology used to track wildfires and predict fire behavior, and about the impressive skill, survival tactics, and bravery required to control a wildfire.
Book
A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN
Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.
Journal Article