Meteorological fire danger indices and remote sensing

1999 ◽  
pp. 39-59 ◽  
Author(s):  
Andrea Camia ◽  
Giovanni Bovio ◽  
Inmaculada Aguado ◽  
Nicolas Stach
Keyword(s):  
Remote Sensing ◽  
Fire Danger ◽  
Fire Danger Indices

scholarly journals Forest fire danger rating in complex topography – results from a case study in the Bavarian Alps in autumn 2011

2013 ◽  
Vol 13 (9) ◽  
pp. 2157-2167 ◽  
Author(s):  
C. Schunk ◽  
C. Wastl ◽  
M. Leuchner ◽  
C. Schuster ◽  
A. Menzel
Keyword(s):  
Forest Fire ◽  
Temperature Inversion ◽  
Fire Danger ◽  
Complex Topography ◽  
Pressure System ◽  
Drought Period ◽  
Bavarian Alps ◽  
Forest Fire Danger ◽  
Fire Danger Indices

Abstract. Forest fire danger rating based on sparse meteorological stations is known to be potentially misleading when assigned to larger areas of complex topography. This case study examines several fire danger indices based on data from two meteorological stations at different elevations during a major drought period. This drought was caused by a persistent high pressure system, inducing a pronounced temperature inversion and its associated thermal belt with much warmer, dryer conditions in intermediate elevations. Thus, a massive drying of fuels, leading to higher fire danger levels, and multiple fire occurrences at mid-slope positions were contrasted by moderate fire danger especially in the valleys. The ability of fire danger indices to resolve this situation was studied based on a comparison with the actual fire danger as determined from expert observations, fire occurrences and fuel moisture measurements. The results revealed that, during temperature inversion, differences in daily cycles of meteorological parameters influence fire danger and that these are not resolved by standard meteorological stations and fire danger indices (calculated on a once-a-day basis). Additional stations in higher locations or high-resolution meteorological models combined with fire danger indices accepting at least hourly input data may allow reasonable fire danger calculations under these circumstances.

scholarly journals Comparison of fire danger indices in the Mediterranean for present day conditions

2012 ◽  
Vol 5 (1) ◽  
pp. 197-203 ◽  
Author(s):  
C Giannakopoulos ◽  
P LeSager ◽  
M Moriondo ◽  
M Bindi ◽  
A Karali ◽  
...  
Keyword(s):  
Fire Danger ◽  
The Mediterranean ◽  
Fire Danger Indices

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals Comparison of Methods for the Assessment of Fire Danger in the Czech Republic

2019 ◽  
Vol 67 (5) ◽  
pp. 1285-1295 ◽  
Author(s):  
František Jurečka ◽  
Martin Možný ◽  
Jan Balek ◽  
Zdeněk Žalud ◽  
Miroslav Trnka
Keyword(s):  
Czech Republic ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Danger ◽  
Special Focus ◽  
Fire Weather ◽  
Fire Event ◽  
Fire Weather Index ◽  
The Czech Republic ◽  
Fire Danger Indices

The performance of fire indices based on weather variables was analyzed with a special focus on the Czech Republic. Three fire weather danger indices that are the basis of fire danger rating systems used in different parts of the world were assessed: the Canadian Fire Weather Index (FWI), Australian Forest Fire Danger Index (FFDI) and Finnish Forest Fire Index (FFI). The performance of the three fire danger indices was investigated at different scales and compared with actual fire events. First, the fire danger indices were analyzed for different land use types during the period 1956–2015. In addition, in the analysis, the three fire danger indices were compared with wildfire events during the period 2001–2015. The fire danger indices were also analyzed for the specific locality of the Bzenec area where a large forest fire event occurred in May 2012. The study also focused on the relationship between fire danger indices and forest fires during 2018 across the area of the Jihomoravský region. Comparison of the index values with real fires showed that the index values corresponded well with the occurrence of forest fires. The analysis of the year 2018 showed that the highest index values were reached on days with the greater fire occurrence. On days with 5 or 7 reported fires per day, the fire danger indices reached values between 3 and 4.

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.

A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

2013 ◽  
Vol 136 ◽  
pp. 455-468 ◽  
Author(s):  
Marta Yebra ◽  
Philip E. Dennison ◽  
Emilio Chuvieco ◽  
David Riaño ◽  
Philip Zylstra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Fire Danger ◽  
Fuel Moisture ◽  
Danger Assessment ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

scholarly journals Establishing Relationships between Drought and Wildfire Danger Indices: A Test Case for the California-Nevada Drought Early Warning System

2019 ◽  
Author(s):  
Daniel J. McEvoy ◽  
Mike T. Hobbins ◽  
Tim J. Brown ◽  
Kristin VanderMolen ◽  
Tamara Wall ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Fire Risk ◽  
Fire Danger ◽  
Drought Indices ◽  
Evaporative Demand ◽  
Drought Early Warning ◽  
Drought Early Warning System ◽  
Fire Danger Indices

Relationships between drought and fire danger indices are examined to 1) incorporate fire risk information into the National Integrated Drought Information System California-Nevada Drought Early Warning System and 2) provide a baseline analysis for application of drought indices into a fire risk management framework. We analyzed four drought indices that incorporate precipitation and evaporative demand (E0) and three fire indices that reflect fuel moisture and potential fire intensity. Seasonally averaged fire danger indices were most strongly correlated to multi-scalar drought indices that use E0 (the Evaporative Demand Drought Index [EDDI] and Standardized Precipitation Evapotranspiration Index [SPEI]) at approximately annual time scales that reflect buildup of antecedent drought conditions. Results indicate that EDDI and SPEI can inform seasonal fire potential outlooks at the beginning of summer. An E0 decomposition case study of conditions prior to the Tubbs Fire in Northern California indicate high E0 (97th percentile) driven predominantly by low humidity signaled increased fire potential several days before the start of the fire. Initial use of EDDI by fire management groups during summer and fall 2018 highlights several value-added applications, including seasonal fire potential outlooks, funding fire severity level requests, and assessing set-up conditions prior to large, explosive fire cases.

scholarly journals DEVELOPMENT OF REMOTE SENSING METHODS FOR NATURAL FIRE PREVENTION

2021 ◽  
pp. 149-161
Author(s):  
Almaz T. Gizatullin ◽  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Simulation Models ◽  
Fire Spread ◽  
Simulation Modelling ◽  
Fire Danger ◽  
Fire Prevention ◽  
Natural Fire ◽  
The Subject ◽  
Remote Sensing Methods

The study deals with remote sensing methods for natural fire prevention, provides analysis and systematization on the subject. It traces the historical development and demonstrates the diversity of the methods. The main development stages and their characteristics were identified taking into account the increasing number of the sources and types of remote sensing and deepening knowledge of the subject. Fire interpretation includes fundamentally different processes of ignition and fire spread. The concepts of fire danger and its factors were introduced, the ways for their selection and application in the methods were analyzed. The source data for the methods were defined: satellite imagery of various resolutions (Landsat, Sentinel, MODIS/Terra-Aqua, AVHRR/NOAA, etc.), UAV images, lidar data, as well as technologies to process those. The study demonstrates that the most commonly used are traditional methods of geoinformation analysis, simulation modelling and neural networks. The methods were described, features of their implementation were identified. The description includes specific examples of fire danger assessment methods based on GIS, simulation models of fire spread, fire prevention methods based on neural networks and their application for territories of different spatial levels – global, regional and local.

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