Computational Analysis of the U.S. Forest Fires

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
António M. Lopes ◽  
J. A. Tenreiro Machado
Keyword(s):  
Dynamical Systems ◽  
Forest Fires ◽  
Computational Analysis ◽  
Space Time ◽  
Burn Severity ◽  
Computational Visualization ◽  
The U.S

This paper analyses forest fires (FF) in the U.S. during 1984–2013, based on data collected by the monitoring trends in burn severity (MTBS) project. The study adopts the tools of dynamical systems to tackle information about space, time, and size. Computational visualization methods are used for reducing the information dimensionality and to unveil the relationships embedded in the data.

Application of Fractional Techniques in the Analysis of Forest Fires

2016 ◽  
Vol 17 (7-8) ◽  
pp. 381-390 ◽  
Author(s):  
António M. Lopes ◽  
J.A. Tenreiro Machado
Keyword(s):  
United States ◽  
Fourier Transform ◽  
Fractal Dimension ◽  
Dynamical Systems ◽  
Forest Fires ◽  
Clustering Analysis ◽  
Fractional Fourier Transform ◽  
Public Domain ◽  
Burn Severity ◽  
Global Behavior

Abstract:In this paper we study the global behavior of forest fires (FFs) in the Continental United States for the period 1984–2013. The data are obtained from a public domain catalog maintained by the Monitoring Trends in Burn Severity project. First we adopt clustering analysis to reduce the information dimensionality. Then we adopt mathematical tools commonly used in the analysis of dynamical systems, namely fractal dimension, entropy and fractional Fourier transform. The fractional techniques unveil FF patterns embedded in the data.

scholarly journals Detecting Forest Fire Area using Landsat Images: A Case Study of Manang District, Nepal

2021 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Forest Fires ◽  
Near Infrared ◽  
Human Life ◽  
Vegetation Indices ◽  
Google Earth ◽  
Burn Severity ◽  
Landsat 8 ◽  
Landsat Images ◽  
Severity Rating ◽  
Forest Regions

Abstract Background: Wildfires are on the rise for various reasons, including hunting, the growth of new plants, and the encroachment of forest regions, particularly in developing countries. As a result, it will lose its environment, property, wildlife, and human life. Methods: It generates a burn severity map that can estimate the extent of wildfire damage. The nine bands and vegetation indices are derived using Google Earth Engine (GEE) and the Quantum Geographic Information System (QGIS) platform from Landsat 8 satellite imagery. The Manang district employs wavelengths near-infrared (NIR) and shortwave-infrared (SWIR) to determine burnt patches and burn severity. Results: According to the evaluation, 26 percent of forest fires have moderate, low, high, and higher severity; however, 30 percent of unburned and low-severity fires receive a severity rating of 37 percent. Thus, it shows a considerable rise in wildfires in the Manang area. Conclusion: In general, it has been a novel technique for recognizing wildfire hotspots and mapping their intensity in higher elevations that takes fewer resources and time. Such necessary data assists vital stakeholders, communities, and decision-makers in making well-informed decisions.

scholarly journals Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

2021 ◽  
Vol 13 (24) ◽  
pp. 5127
Author(s):  
Changming Yin ◽  
Minfeng Xing ◽  
Marta Yebra ◽  
Xiangzhuo Liu
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Coniferous Forest ◽  
Northern China ◽  
Tree Canopy ◽  
Burn Severity ◽  
Environmental Drivers ◽  
Obvious Effect ◽  
Tree Canopy Cover

Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.

A synthesis of post-fire Burned Area Reports from 1972 to 2009 for western US Forest Service lands: trends in wildfire characteristics and post-fire stabilisation treatments and expenditures

2014 ◽  
Vol 23 (7) ◽  
pp. 929 ◽  
Author(s):  
Peter R. Robichaud ◽  
Hakjun Rhee ◽  
Sarah A. Lewis
Keyword(s):  
Forest Fires ◽  
Timber Harvest ◽  
Burn Severity ◽  
Burned Area ◽  
Soil Productivity ◽  
Treatment Implementation ◽  
Western Us ◽  
Treatment Type ◽  
Rate Of Increase ◽  
Over Time

Over 1200 post-fire assessment and treatment implementation reports from four decades (1970s–2000s) of western US forest fires have been examined to identify decadal patterns in fire characteristics and the justifications and expenditures for the post-fire treatments. The main trends found were: (1) the area burned by wildfire increased over time and the rate of increase accelerated after 1990; (2) the proportions of burned area assessed as low, moderate and high burn severity likely have remained fairly constant over time, but the use of satellite imagery that began c. 2000 increased the resolution of burn severity assessments leading to an apparent decreased proportion of high burn severity during the 2000s; (3) treatment justifications reflected regional concerns (e.g. soil productivity in areas of timber harvest) and generally reflected increased human encroachment in the wildland–urban interface; (4) modifications to roads were the most frequently recommended post-fire treatment type; (5) seeding was the most frequently used land treatment, but declined in use over time; (6) use of post-fire agricultural straw mulch has steadily increased because of proven success; and (7) the greatest post-fire expenditures have been for land treatments applied over large areas to protect important resources (e.g. municipal water sources).

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