Geoinformation system for prediction of forest fire danger caused by solar radiation using remote sensing data

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.

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

Remote Sensing ◽

10.3390/rs11182101 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2101 ◽

Cited By ~ 8

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.

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PROGRAM COMPONENTS FOR WEB-ORIENTED GEOINFORMATION SYSTEM OF FOREST FIRE DANGER PREDICTION

14th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING ◽

10.5593/sgem2014/b21/s8.095 ◽

2014 ◽

Author(s):

Nikolay V. Baranovskiy

Keyword(s):

Forest Fire ◽

Fire Danger ◽

Geoinformation System ◽

Forest Fire Danger ◽

Program Components

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

Epidemiology ◽

10.1097/00001648-200611001-00516 ◽

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

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Forest fire analysis with remote sensing data

International Journal of Remote Sensing ◽

10.1080/01431160118510 ◽

2001 ◽

Vol 22 (12) ◽

pp. 2265-2277 ◽

Cited By ~ 45

Author(s):

F. Sunar ◽

C. Özkan

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Remote Sensing Data ◽

Sensing Data

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Development of the preventive natural fire danger assessment algorithm using remote sensing data

Geodesy and Cartography ◽

10.22389/0016-7126-2019-943-1-102-109 ◽

2019 ◽

Vol 943 (1) ◽

pp. 102-109 ◽

Cited By ~ 1

Author(s):

A.T. Gizatullin ◽

N.A. Alekseenko ◽

V.S. Moiseeva

Keyword(s):

Remote Sensing ◽

General Structure ◽

Remote Sensing Data ◽

Fire Danger ◽

Filling Process ◽

Program Code ◽

Natural Fire ◽

Sensing Data ◽

Danger Assessment ◽

Current State

This article is devoted to the development of an algorithm for the preventive assessment of the fire danger of natural areas using remote sensing data (the preventive natural fire danger assessment algorithm). The problems of the current state of the remote sensing materials use for fires researches as a justification for the need of the algorithm are considered. A review of existing methods and algorithms of natural fire danger assessment is done. The algorithm development includes description of the general structure and the content filling process of different algorithm components. The algorithm is a stages sequence of remote sensing data processing and analysis in terms of fire danger. As a result of algorithm, the fire danger assessment of the observed territory is formed. A special feature of the algorithm is its preventiveness, universality (applicability for any territory), practical automatability (the ability to represent in the form of a program code for the processing of RSD) and flexibility (the ability to add and branch the sequence). In the end, general conclusions and recommendations on the use of the algorithm are given.

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