Introduction to Fire Danger Rating and Remote Sensing — Will Remote Sensing Enhance Wildland Fire Danger Rating?

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.

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Environmental Plant Responses and Wildland Fire Danger

Plant-Fire Interactions - Managing Forest Ecosystems ◽

10.1007/978-3-030-41192-3_5 ◽

2020 ◽

pp. 75-92

Author(s):

Víctor Resco de Dios

Keyword(s):

Wildland Fire ◽

Fire Danger ◽

Plant Responses

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Wildland Fire Danger Zoning - a Methodology

International Journal of Wildland Fire ◽

10.1071/wf9980037 ◽

1998 ◽

Vol 8 (1) ◽

pp. 37 ◽

Cited By ~ 10

Author(s):

V Gouma ◽

A Chronopoulou-Sereli

Keyword(s):

Information System ◽

Geographic Information System ◽

Wildland Fire ◽

Geographic Information ◽

Fire Danger ◽

Fire Prevention ◽

Overlay Analysis ◽

Fire Occurrence ◽

Thematic Maps ◽

Mountain Area

A mountain area in Southeastern Greece exposed to wildland fire problems was used to establish a method for fire danger zoning. Meteorological risk (MR), fuel susceptibility (FS) and fire occurrence (FO) maps are created. The method integrates these maps and produces the constant and variable danger (CFD,VFD) zones that require respective activities for wildland fire prevention. A Geographic Information System (GIS) was used to perform the overlay analysis of thematic maps and delineate the fire danger zones.

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Meteorological fire danger indices and remote sensing

Remote Sensing of Large Wildfires ◽

10.1007/978-3-642-60164-4_4 ◽

1999 ◽

pp. 39-59 ◽

Cited By ~ 9

Author(s):

Andrea Camia ◽

Giovanni Bovio ◽

Inmaculada Aguado ◽

Nicolas Stach

Keyword(s):

Remote Sensing ◽

Fire Danger ◽

Fire Danger Indices

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

Remote Sensing of Environment ◽

10.1016/j.rse.2013.05.029 ◽

2013 ◽

Vol 136 ◽

pp. 455-468 ◽

Cited By ~ 116

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

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The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma

International Journal of Wildland Fire ◽

10.1071/wf02003 ◽

2002 ◽

Vol 11 (4) ◽

pp. 183 ◽

Cited By ~ 17

Author(s):

J. D. Carlson ◽

Robert E. Burgan ◽

David M. Engle ◽

Justin R. Greenfield

Keyword(s):

Wildland Fire ◽

Weather Conditions ◽

Fire Danger ◽

Rating System ◽

Weather Data ◽

Fuel Load ◽

Usda Forest Service ◽

University Of Oklahoma ◽

Oklahoma Mesonet ◽

Fire Danger Rating System

This paper describes the Oklahoma Fire Danger Model, an operational fire danger rating system for the state of Oklahoma (USA) developed through joint efforts of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. The model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma, but more importantly, represents the first time anywhere that NFDRS has been implemented operationally using hourly weather data from a spatially dense automated weather station network (the Oklahoma Mesonet). Weekly AVHRR satellite imagery is also utilized for live fuel moisture and fuel load calculations. The result is a near-real-time mesoscale fire danger rating system to 1-km resolution whose output is readily available on the World Wide Web (http://agweather.mesonet.ou.edu/models/fire). Examples of output from 25 February 1998 are presented.The Oklahoma Fire Danger Model, in conjunction with other fire-related operational tools, has proven useful to the wildland fire management community in Oklahoma, for both wildfire anticipation and suppression and for prescribed fire activities. Instead of once-per-day NFDRS information at two to three sites, the fire manager now has statewide fire danger information available at 1-km resolution at up to hourly intervals, enabling a quicker response to changing fire weather conditions across the entire state.

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Early warning for grassland fire danger in north China using remote sensing

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) ◽

10.1109/igarss.2003.1294490 ◽

2004 ◽

Author(s):

Weiqi Zhou ◽

Yi Zhou ◽

Shixin Wang ◽

Qing Zhao

Keyword(s):

Remote Sensing ◽

Early Warning ◽

North China ◽

Fire Danger ◽

Grassland Fire

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Validation of remote sensing of burn severity in south-eastern US ecosystems

International Journal of Wildland Fire ◽

10.1071/wf10013 ◽

2011 ◽

Vol 20 (3) ◽

pp. 453 ◽

Cited By ~ 37

Author(s):

Joshua J. Picotte ◽

Kevin M. Robertson

Keyword(s):

Remote Sensing ◽

Wildland Fire ◽

Ground Truth ◽

Fire Effects ◽

Burn Severity ◽

Burned Area ◽

Vegetation Types ◽

South Eastern ◽

Eastern Usa ◽

Landsat Satellite

We assessed an existing method of remote sensing of wildland fire burn severity for its applicability in south-eastern USA vegetation types. This method uses Landsat satellite imagery to calculate the Normalised Burn Ratio (NBR) of reflectance bands sensitive to fire effects, and the change in NBR from pre- to post fire (dNBR) to estimate burn severity. To ground-truth ranges of NBR and dNBR that correspond to levels of burn severity, we measured severity using the Composite Burn Index at 731 locations stratified by plant community type, season of measurement, and time since fire. Best-fit curves relating Composite Burn Index to NBR or dNBR were used to determine reflectance value breakpoints that delimit levels of burn severity. Remotely estimated levels of burn severity within 3 months following fire had an average of 78% agreement with ground measurements using NBR and 75% agreement using dNBR. However, percentage agreement varied among habitat types and season of measurement, with either NBR or dNBR being advantageous under specific combinations of conditions. The results suggest this method will be useful for monitoring burned area and burn severity in south-eastern USA vegetation types if the provided recommendations and limitations are considered.

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