scholarly journals Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

2015 ◽  
Vol 7 (3) ◽  
pp. 2431-2448 ◽  
Author(s):  
Ehsan Chowdhury ◽  
Quazi Hassan
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Sensing Data ◽  
Daily Scale ◽  
Forecasting System ◽  
Forest Fire Danger

Synergy of GIS and Remote Sensing Data in Forest Fire Danger Modeling

2008 ◽  
Vol 1 (4) ◽  
pp. 240-247 ◽  
Author(s):  
Pedro A. Hernandez-Leal ◽  
Alejandro Gonzalez-Calvo ◽  
Manuel Arbelo ◽  
Africa Barreto ◽  
Alfonso Alonso-Benito
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Gis And Remote Sensing ◽  
Sensing Data ◽  
Forest Fire Danger

Classification of Territory on Forest Fire Danger Level Using GIS and Remote Sensing

2020 ◽  
pp. 258-267 ◽  
Author(s):  
Elena Petrovna Yankovich ◽  
Ksenia S. Yankovich
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Gis And Remote Sensing ◽  
Forest Fuels ◽  
Forest Fire Danger

The vegetation cover is the most important factor in forest fires, because it reflects the presence of forest fuels. The study of the variability of the vegetation cover, as well as observation of its condition, allows estimating the level of fire danger of the forest quarter. The work presents a geo-information system containing a set of tools to determine the level of fire danger of the forest quarter. The system is able to predict (determine the probability) and classify forest quarters according to the level of fire danger. The assessment of forest fire danger of Tomsk forestry of Tomsk region has been carried out. Fire probability maps of forest quarters were created based on remote sensing data and ArcGIS software.

scholarly journals Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

2019 ◽  
Vol 11 (18) ◽  
pp. 2101 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Land Surface ◽  
Vegetation Index ◽  
Significant Risk ◽  
Fire Danger ◽  
Area Of Interest ◽  
Forest Fire Danger ◽  
Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

Forest Fire Smoke Exposure Assessment: Use of Remote Sensing Data in a Pollution Dispersion Model

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S203
Author(s):  
S B. Henderson ◽  
B Burkholder ◽  
M Brauer ◽  
P L. Jackson ◽  
B Klinkenberg ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Exposure Assessment ◽  
Forest Fire ◽  
Dispersion Model ◽  
Remote Sensing Data ◽  
Smoke Exposure ◽  
Pollution Dispersion ◽  
Sensing Data ◽  
Fire Smoke

Forest fire analysis with remote sensing data

2001 ◽  
Vol 22 (12) ◽  
pp. 2265-2277 ◽  
Author(s):  
F. Sunar ◽  
C. Özkan
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing Data ◽  
Sensing Data
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