Experimental confirmation of the MWIR and LWIR grey body assumption for vegetation fire flame emissivity

2014 ◽  
Vol 23 (4) ◽  
pp. 463 ◽  
Author(s):  
J. M. Johnston ◽  
M. J. Wooster ◽  
T. J. Lynham

The temperature and emissivity of forest fire flames play a key role in understanding fire behaviour, modelling fire spread and calculating fire parameters by means of active fire thermal remote sensing. Essential to many of these is the often-made assumption that vegetation fire flames behave as grey bodies in the infrared (IR). Although the emissivity of flames and its relationship to flame depth has been measured experimentally using thermal imagers working in the long-wave IR (LWIR, 8–12µm), no published study has yet demonstrated relationships in the important mid-wave IR (MWIR, 3–5µm) spectral region, nor conclusively demonstrated that assumptions about grey body behaviour across these two important IR atmospheric windows fit well with reality. Our study explores these issues using measurements of boreal forest fuels burned with flame depths ranging from 0.2 to 4.2 m. Observations of two stable black body sources made through the differing flame depths were used to explore flame spectral emissivities and their relationship to flame depth. We found essentially the same relationship between flame emissivity and flame depth for both spectral regions, (extinction coefficient K=0.7 m–1), confirming that the grey body assumption for forest fire flames in the MWIR and LWIR atmospheric windows appears valid for the fire conditions encountered here.

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  

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.


2000 ◽  
Vol 43 (S1) ◽  
pp. 104-112 ◽  
Author(s):  
Qijiang Zhu ◽  
Taizong Rong ◽  
Rui Sun

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.


2016 ◽  
Vol 15 (1) ◽  
pp. 85-92
Author(s):  
Ágoston Restás

It is commonly known that firefighting is very expensive solution; therefore it isn’t useless to study it by the criteria of efficiency. But the meaning of efficiency for fire managers can be different from the meaning of efficiency for economists. From an economic viewpoint, it is stricter than from a technical view. Method: this research used geometric aspects of the fire spread created rectangular and concentric circles models and used basic mathematic calculations and logical conclusions. Results and discussion: The rectangular model shows the criteria of economic efficiency of firefighting. Moreover, the results from rectangular model can be transferred also to the section of concentric circles model. Based on the concentric circle model we can define both the economic efficiency of fighting forest fire and minimal criteria of successful suppression expressed by the elementary information we have regarding the actual fire.


Author(s):  
Evdokia Sotirova ◽  
Emilia Velizarova ◽  
Stefka Fidanova ◽  
Krassimir Atanassov
Keyword(s):  

2018 ◽  
Vol 11 (6) ◽  
pp. 1-6 ◽  
Author(s):  
N.M.. Rajan1 ◽  
J. Shanmugam ◽  
◽  
Keyword(s):  

2014 ◽  
Vol 70 (2) ◽  
pp. 721-732 ◽  
Author(s):  
Carlos Brun ◽  
Tomàs Margalef ◽  
Ana Cortés ◽  
Anna Sikora
Keyword(s):  

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