A Satellite Index of Risk of Forest Fire Occurrence in Summer in the Mediterranean Area

1998 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
V Prosper-Laget ◽  
A Douguedroit ◽  
JP Guinot
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Fire Risk ◽  
Mediterranean Area ◽  
Soil Water Deficit ◽  
Fire Occurrence ◽  
Risk Class ◽  
Noaa Avhrr ◽  
Vegetation Stress ◽  
Forest Fire Risk

An index of forest fire risk has been determined by using the vegetation index NDVI and the surface temperature Ts, computed from NOAA-AVHRR over 21 Mediterranean French forests. Those 2 satellite parameters can be interpreted in terms of soil water deficit and vegetation stress in summer. An inverse linear correlation between their values for each forest pixel of 10 dates in 1990 was used to establish the index which has been divided into 5 equal classes. Those classes correspond with 5 risk classes of forest fire occurrence which were mapped for several forests. Periods and areas in the highest risk class correspond with those where the most important number of fires appeared in that year for the studied forests. A statistical model of the period of highest fire risk has also been constructed for each forest.

scholarly journals Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

2020 ◽  
Vol 12 (22) ◽  
pp. 3705
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquín Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
José María Fernández-Alonso ◽  
...  
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Risk ◽  
Fire Regimes ◽  
Slope Aspect ◽  
Fire Weather Index ◽  
Novel Approach ◽  
Fuel Model ◽  
Forest Fire Risk

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation

2017 ◽  
Vol 26 (9) ◽  
pp. 789 ◽  
Author(s):  
Hyeyoung Woo ◽  
Woodam Chung ◽  
Jonathan M. Graham ◽  
Byungdoo Lee
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Forest Fire ◽  
Fire Risk ◽  
Fire Spread ◽  
Fire Occurrence ◽  
Occurrence Probability ◽  
Burn Probability ◽  
Forest Fire Risk ◽  
Fire Ignition

Risk assessment of forest fires requires an integrated estimation of fire occurrence probability and burn probability because fire spread is largely influenced by ignition locations as well as fuels, weather, topography and other environmental factors. This study aims to assess forest fire risk over a large forested landscape using both fire occurrence and burn probabilities. First, we use a spatial point processing method to generate a fire occurrence probability surface. We then perform a Monte Carlo fire spread simulation using multiple fire ignition points generated from the fire occurrence surface to compute burn probability across the landscape. Potential loss per land parcel due to forest fire is assessed as the combination of burn probability and government-appraised property values. We applied our methodology to the municipal boundary of Gyeongju in the Republic of Korea. The results show that the density of fire occurrence is positively associated with low elevation, moderate slope, coniferous land cover, distance to roads, high density of tombs and interaction among fire ignition locations. A correlation analysis among fire occurrence probability, burn probability, land property value and potential value loss indicates that fire risk in the study landscape is largely associated with the spatial pattern of burn probability.

An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain

1991 ◽  
Vol 12 (9) ◽  
pp. 1841-1851 ◽  
Author(s):  
S. LÓPEZ ◽  
F. GONZÁ;LEZ ◽  
R. LLOP ◽  
J. M. CUEVAS
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Noaa Avhrr ◽  
Forest Fire Risk

Analysis of NOAA‐AVHRR NDVI inter‐annual variability for forest fire risk estimation

2006 ◽  
Vol 27 (8) ◽  
pp. 1725-1732 ◽  
Author(s):  
A. Gabban ◽  
J. San‐Miguel‐Ayanz ◽  
P. Barbosa ◽  
G. Libertà
Keyword(s):  
Forest Fire ◽  
Risk Estimation ◽  
Fire Risk ◽  
Annual Variability ◽  
Noaa Avhrr ◽  
Forest Fire Risk

A forest fire risk assessment using NOAA AVHRR images in the Valencia area, eastern Spain

1997 ◽  
Vol 18 (10) ◽  
pp. 2201-2207 ◽  
Author(s):  
F. Gonzalez-Alonso ◽  
J. M. Cuevas ◽  
J. L. Casanova ◽  
A. Calle ◽  
P. Illera
Keyword(s):  
Risk Assessment ◽  
Forest Fire ◽  
Fire Risk ◽  
Noaa Avhrr ◽  
Fire Risk Assessment ◽  
Forest Fire Risk

Assessment of seasonal forest fire risk using NOAA-AVHRR: a case study in central Mexico

2009 ◽  
Vol 30 (19) ◽  
pp. 4991-5013 ◽  
Author(s):  
L. Manzo-Delgado ◽  
S. Sánchez-Colón ◽  
R. Álvarez
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Central Mexico ◽  
Noaa Avhrr ◽  
Forest Fire Risk

scholarly journals Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables

Fire ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 50 ◽  
Author(s):  
Omid Ghorbanzadeh ◽  
Thomas Blaschke ◽  
Khalil Gholamnia ◽  
Jagannath Aryal
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Environmental Variables ◽  
Fire Risk ◽  
Vulnerability Index ◽  
Susceptibility Map ◽  
Northern Iran ◽  
Risk Class ◽  
The Social ◽  
Forest Fire Risk

Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning.

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