Analyses of forest fire danger based on remote sensing in subtropical monsoon zone

2009 ◽  
Author(s):  
Shu-e Huang ◽  
Jinxiang Xiao ◽  
Ning Zhao ◽  
Biqin Zhu ◽  
Jianping Zhang
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Fire Danger ◽  
Forest Fire Danger

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.

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.

Study on forest fire danger model with remote sensing based on GIS

2000 ◽  
Vol 10 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Fang Huang ◽  
Xiang-nan Liu ◽  
Jin-guo Yuan
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Fire Danger ◽  
Forest Fire Danger

scholarly journals The Remote Sensing Method of Forest Fire Danger Rating Categorization

2004 ◽  
Vol 11 (1) ◽  
pp. 359-362
Author(s):  
Małgorzata Mycke-Dominko
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Satellite Images ◽  
Fire Danger ◽  
Landsat Tm ◽  
Forest Classification ◽  
Forest Fire Prevention ◽  
Forest Fire Danger ◽  
Remote Sensing Indices

Abstract The article presents the use of satellite images in the determination of forest fire danger rating categories. The assessment was carried out based on images from the LANDSAT TM, IKONOS and NOAA satellites, with the finding that the LANDSAT TM images are the most useful. A new solution proposed is to make forest fire danger rating categories refer to forest ranger sub-districts, what gives the forest service greater control over forest fire prevention activities. Forest fire danger assessment was done taking into account remote sensing indices such as the NDVI, TNDVI, and IHT, as well by the analysis of the spatial distribution and the number of fires in the previous six years. In accordance with the Polish State Forest Classification System, three classes were specified: 1 – high fire danger, 2 – moderate fire danger, 3 – low fire danger.

Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data

2012 ◽  
Vol 21 (8) ◽  
pp. 1025 ◽  
Author(s):  
Mar Bisquert ◽  
Eduardo Caselles ◽  
Juan Manuel Sánchez ◽  
Vicente Caselles
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Surface Temperature ◽  
Forest Fire ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Fire Danger ◽  
Forest Fire Danger

Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.

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