Grassland fire spread simulation using NDVI data

2017 ◽  
Author(s):  
Nikolay V. Baranovskiy ◽  
Rita Kogan ◽  
Vladimir Glagolev ◽  
Anna Zubareva
Keyword(s):  
Fire Spread ◽  
Grassland Fire

Monte Carlo-based ensemble method for prediction of grassland fire spread

2010 ◽  
Vol 19 (4) ◽  
pp. 521 ◽  
Author(s):  
Miguel G. Cruz
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Fire Spread ◽  
Error Component ◽  
Ensemble Method ◽  
Fire Behaviour ◽  
Spread Model ◽  
Grassland Fire

The operational prediction of fire spread to support fire management operations relies on a deterministic approach where a single ‘best-guess’ forecast is produced from the best estimate of the environmental conditions driving the fire. Although fire can be considered a phenomenon of low predictability and the estimation of input conditions for fire behaviour models is fraught with uncertainty, no error component is associated with these forecasts. At best, users will derive an uncertainty bound to the model outputs based on their own personal experience. A simple ensemble method that considers the uncertainty in the estimation of model input values and Monte Carlo sampling was applied with a grassland fire-spread model to produce a probability density function of rate of spread. This probability density function was then used to describe the uncertainty in the fire behaviour prediction and to produce probability-based outputs. The method was applied to a grassland wildfire case study dataset. The ensemble method did not improve the general statistics describing model fit but provided complementary information describing the uncertainty associated with the predictions and a probabilistic output for the occurrence of threshold levels of fire behaviour.

RATES OF SURFACE FIRE SPREAD IN A YOUNG CALABRIAN PINE (Pinus brutia Ten.) PLANTATION

2012 ◽  
Vol 11 (8) ◽  
pp. 1475-1480 ◽  
Author(s):  
Omer Kucuk ◽  
Ertugrul Bilgili ◽  
Serkan Bulut ◽  
Paulo M. Fernandes
Keyword(s):  
Fire Spread ◽  
Surface Fire ◽  
Pinus Brutia ◽  
Calabrian Pine

scholarly journals Ignition Vulnerabilities of Combustibles around Houses to Firebrand Showers: Further Comparison of Experiments

2021 ◽  
Vol 13 (4) ◽  
pp. 2136
Author(s):  
Sayaka Suzuki ◽  
Samuel L. Manzello
Keyword(s):  
Experimental Data ◽  
Thermal Radiation ◽  
Driving Force ◽  
Fire Spread ◽  
Significant Problem ◽  
Next Generation ◽  
Similar Time ◽  
Comparison Of Experiments ◽  
Fire Front ◽  
Wui Fires

Wildland fires and wildland urban-interface (WUI) fires have become a significant problem in recent years. The mechanisms of home ignition in WUI fires are direct flame contact, thermal radiation, and firebrand attack. Out of these three fire spread factors, firebrands are considered to be a main driving force for rapid fire spread as firebrands can fly far from the fire front and ignite structures. The limited experimental data on firebrand showers limits the ability to design the next generation of communities to resist WUI fires to these types of exposures. The objective of this paper is to summarize, compare, and reconsider the results from previous experiments, to provide new data and insights to prevent home losses from firebrands in WUI fires. Comparison of different combustible materials around homes revealed that wood decking assemblies may be ignited within similar time to mulch under certain conditions.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Upward Fire Spread Rate Over Real-Scale EPS ETICS Façades

2021 ◽  
Author(s):  
Biao Zhou ◽  
Hideki Yoshioka ◽  
Takafumi Noguchi ◽  
Kai Wang ◽  
Xinyan Huang
Keyword(s):  
Fire Spread ◽  
Spread Rate ◽  
Real Scale
Sign in / Sign up

Export Citation Format

Share Document