Wildfire Burned Area Research Articles

A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States

We present a direct broadcast (DB) rapid response burned area mapping algorithm for Visible Infrared Imaging Radiometer Suite (VIIRS) data that combines products driven by the spectral signal of fire-affected areas from both emissive and reflective spectral bands. The algorithm processes VIIRS infrared M-bands (750 m) using spectral ratios of the top of atmosphere reflectance from a single satellite scene to identify pixels exhibiting surface properties consistent with burn scars. Next, this collection of candidate burn scar pixels is screened using a contextual filter based on VIIRS I-band (375 m) active fire detections (AFD) which removes erroneously classified pixels and provides burn scar detections (BSD). The AFD and BSD are then resampled to a 375 m grid and reported jointly as VIIRS burned area (VBA). The accuracy of the VBA was assessed for 390 wildfires (11–114,500 ha in size) in the western United States. The spatial accuracy was assessed by comparison with a validation dataset of Monitoring Trends in Burn Severity (MTBS) burned area and incident fire perimeter polygons. The VBA temporal accuracy was evaluated using a time series of daily fire perimeter polygons derived from high resolution airborne infrared imagery. The algorithm's burned area mapping accuracy is 59%. The algorithm detected 60% of burned area on the initial day of burning and 73% within 24 h.

Environmental Conditions, Ignition Type, and Air Quality Impacts of Wildfires in the Southeastern and Western United States.

This research contrasts the environmental conditions, meteorological drivers, and air quality impacts of human‐ and lightning‐ignited wildfires in the southeastern and western United States, the two continental U.S. regions with the most wildfire burn area. We use the Fire Program Analysis Wildfire Occurrence Data (FPA FOD) to determine wildfire abundance and ignition sources between 1992 and 2015. We investigate specific ecoregions within these two U.S. regions and find that in the majority of ecoregions, annual lightning‐ and human‐ignited wildfire burn area have similar relationships with key meteorological parameters. We investigate the fuel moisture values where wildfires occur segregated by ignition type and show that within a given ecoregion, the differences in median fuel moisture between ignition types are generally smaller than the differences between ecoregions. Our results suggest that annual wildfire burn area for human‐ and lightning‐ignited wildfires within a given ecoregion are modulated by environmental conditions, and climate change may similarly impact wildfires of both ignition types. Finally, we estimate fine particulate matter emissions for Fire Program Analysis Wildfire Occurrence Data wildfires using the Fire INventory from NCAR model framework. We show that emissions of fine particulate matter from human‐ignited wildfires is significant and of a similar total magnitude between the west and southeastern United States. Additionally, the west and southeast have a similar number of wildfires associated with National Weather Service air quality smoke forecasts.

Estimating wildfire growth from noisy and incomplete incident data using a state space model

Wildfire behaviors are complex and are of interest to fire managers and scientists for a variety of reasons. Many of these important behaviors are directly measured from the cumulative burn area time series of individual wildfires; however, estimating cumulative burn area time series is challenging due to the magnitude of measurement errors and missing entries. To resolve this, we introduce two state space models for reconstructing wildfire burn area using repeated observations from multiple data sources that include different levels of measurement error and temporal gaps. The constant growth parameter model uses a few parameters and assumes a burn area time series that follows a logistic growth curve. The non-constant growth parameter model uses a time-varying logistic growth curve to produce detailed estimates of the burn area time series that permit sudden pauses and pulses of growth. We apply both reconstruction models to burn area data from 13 large wildfire incidents to compare the quality of the burn area time series reconstructions and computational requirements. The constant growth parameter model reconstructs burn area time series with minimal computational requirements, but inadequately fits observed data in most cases. The non-constant growth parameter model better describes burn area time series, but can also be highly computationally demanding. Sensitivity analyses suggest that in a typical application, the reconstructed cumulative burn area time series is fairly robust to minor changes in the prior distributions.

Quantifying relations between surface runoff and aridity after wildfire

AbstractPost‐wildfire runoff and erosion are major concerns in fire‐prone landscapes around the world, but these hydro‐geomorphic responses have been found to be highly variable and difficult to predict. Some variations have been observed to be associated with landscape aridity, which in turn can influence soil hydraulic properties. However, to date there has been no attempt to systematically evaluate the apparent relations between aridity and post‐wildfire runoff. In this study, five sites in a wildfire burnt area were instrumented with rainfall‐runoff plots across an aridity index (AI) gradient. Surface runoff and effective rainfall were measured over 10 months to allow investigation of short‐ (peak runoff) and longer‐term (runoff ratio) runoff characteristics over the recovery period. The results show a systematic and strong relation between aridity and post‐wildfire runoff. The average runoff ratio at the driest AI site (33.6%) was two orders of magnitude higher than at the wettest AI site (0.3%). Peak runoff also increased with AI, with up to a thousand‐fold difference observed during one event between the driest and wettest sites. The relation between AI, peak 15‐min runoff (Q15) and peak 15‐min rainfall intensity (I15) (both in mm h‐1) could be quantified by the equation: Q15 = 0.1086I15 × AI 2.691 (0.65<AI<1.80, 0<I15<45) (adjusted r2 = 0.84). The runoff ratios remained higher at drier AI sites (AI 1.24 and 1.80) throughout the monitoring period, suggesting higher AI also lengthens the window of disturbance after wildfire. The strong quantifiable link which this study has determined between AI and post‐wildfire surface runoff could greatly improve our capacity to predict the magnitude and location of hydro‐geomorphic processes such as flash floods and debris flows following wildfire, and may help explain aridity‐related patterns of soil properties in complex upland landscapes. Copyright © 2018 John Wiley & Sons, Ltd.

Dryness in a Mediterranean-type climate – implications for wildfire burnt area: a case study from Mount Carmel, Israel

Wildfires are an integral component of Mediterranean ecosystems, in which the occurrence of fire has markedly increased since the 1970s, due to anthropogenic stresses and climate change. Although fires are often associated with both factors, the relationship is not straightforward; however, dryness plays a key role in fire behaviour. Commonly, dryness is calculated by analysing the intervals between rainstorms – termed dry spells. The number of dry days since last rain (DDSLR) represents an innovative approach in which various aspects such as severity, consistency and temporal uncertainty of dryness are monitored. DDSLR at the University of Haifa meteorological station was calculated at two different daily rainfall thresholds (DRT = 1.0 and 5.0 mm) in 1976–2013 and correlated with large wildfires (>100 ha). The severity, consistency and temporal uncertainty of dryness are presented for both thresholds and for the entire year, rainy season and winter. The results show that dry periods longer than 60 days occur every year, and a dry period of at least 150 days occurs twice in 3 years. Large fires occur during long dry periods. DDSLR trend analysis indicates an elongation of the dry period at an average rate of 1 day per year. Therefore, larger areas affected by wildfires might be anticipated.

Climate change and growth scenarios for California wildfire

Large wildfire occurrence and burned area are modeled using hydroclimate and landsurface characteristics under a range of future climate and development scenarios. The range of uncertainty for future wildfire regimes is analyzed over two emissions pathways (the Special Report on Emissions Scenarios [SRES] A2 and B1 scenarios); three global climate models (Centre National de Recherches Meteorologiques CM3, Geophysical Fluid Dynamics Laboratory CM2.1 and National Center for Atmospheric Research PCM1); three scenarios for future population growth and development footprint; and two thresholds for defining the wildland-urban interface relative to housing density. Results were assessed for three 30-year time periods centered on 2020, 2050, and 2085, relative to a 30-year reference period centered on 1975. Increases in wildfire burned area are anticipated for most scenarios, although the range of outcomes is large and increases with time. The increase in wildfire burned area associated with the higher emissions pathway (SRES A2) is substantial, with increases statewide ranging from 36% to 74% by 2085, and increases exceeding 100% in much of the forested areas of Northern California in every SRES A2 scenario by 2085.

Global and regional analysis of climate and human drivers of wildfire

Identifying and quantifying the statistical relationships between climate and anthropogenic drivers of fire is important for global biophysical modelling of wildfire and other Earth system processes. This study used regression tree and random forest analysis on global data for various climatic and human variables to establish their relative importance. The main interactions found at the global scale also apply regionally: greatest wildfire burned area is associated with high temperature (>28°C), intermediate annual rainfall (350–1100mm), and prolonged dry periods (which varies by region). However, the regions of highest fire incidence do not show clear and systematic behaviour. Thresholds seen in the regression tree split conditions vary, as do the interplay between climatic and anthropogenic variables, so challenges remain in developing robust predictive insight for the most wildfire-threatened regions.Anthropogenic activities alter the spatial extent of wildfires. Gross domestic product (GDP) density is the most important human predictor variable at the regional scale, and burned area is always greater when GDP density is minimised. South America is identified as a region of concern, as anthropogenic factors (notably land conversions) outweigh climatic drivers of wildfire burned area.

The effects of wildfire and environmental amenities on property values in northwest Montana, USA

This study employed the hedonic price framework to examine the effects of 256 wildfires and environmental amenities on home values in northwest Montana between June 1996 and January 2007. The study revealed environmental amenities, including proximity to lakes, national forests, Glacier National Park and golf courses, have large positive effects on property values in northwest Montana. However, proximity to and view of wildfire burned areas has had large and persistent negative effects on home values. The analysis supports an argument that homebuyers may correlate proximity to and view of a wildfire burned area with increased wildfire risk. Indeed, when a burned area is not visible from a home, wildfire risk appears to be out of sight and out of mind for homebuyers. Findings from this research can be used to inform debate about efficient allocation of resources to wildfire preparedness, including public education programs, and suppression activities around the wildland–urban interface.

Economic and energy efficiency of salvaging biomass from wildfire burnt areas for bioenergy production in northwestern Ontario: A case study

Wildfire burnt forest biomass can be salvaged as feedstock for bioenergy power generating stations. Despite availability of such forest biomass in northwestern Ontario, its procurement has generally been considered uneconomic and no studies have looked into the cost of harvesting, processing, and transporting the burnt material for bioenergy production. In order to meet the demand of biomass for proposed and existing power generating stations using renewable fuels, a standard costing model is used to determine the feasibility of procuring biomass from burnt areas using a full-tree to roadside, roadside grinder to mill system. The case-study was conducted at the Hogarth Plantations near Thunder Bay, Ontario, Canada. The total cost incurred for processing and delivery of biomass from wildfire burnt area with a hauling distance of 7 km and total trip cycle time of 2.55 h was found to be $29.65 gt−1, with net energy content of 11.4 GJ gt−1. The total procurement cost depends on the hauling distance and a linear relationship between the two was established. The energy analysis found a net energy output to input ratio of 35:1 for the operation.

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) – direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km 2, and 93% of all reference fires > 10 km 2. The burned area was highly correlated ( R 2 = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km 2 in size, improving to ± 31% at a fire size of 100 km 2. Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km 2, and 66% results from fires > 100 km 2). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km 2 spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.

Impact of Weather Covariates on Wildfire in Tanjung Puting National Park

This paper explores wildfire modeling based on meteorological variables for Tanjung Puting National Park, located on the island of Borneo. A separable model is developed for predicting daily wildfire burn area using variables such as temperature, sea level pressure, humidity, precipitation, visibility, and wind speed. Each component in the model is estimated using kernel smoothing and maximum likelihood methods. The data are shown to be largely compatible with the separable model, suggesting that the relationship between wildfire burn area and any of these weather variables in particular does not appear to change significantly depending on the values of the other weather variables. The analysis appears to confirm the findings of previous studies on wildfire in Southern California which indicate that wildfire hazard may be suitably estimated using a simple multiplicative model where the impact of each weather covariate is estimated separately.

Regional relationships between climate and wildfire-burned area in the Interior West, USA

Recent studies have linked the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) with drought occurrence in the interior United States. This study evaluates the influence of AMO and PDO phases on interannual relationships between climate and wildfire-burned area during the 20th century. Palmer's Drought Severity Index (PDSI) is strongly related to burned area at both regional and subregional scales. In the southern Interior West, PDSI is most strongly related to yearly burned area during warm-phase AMO, while for the same period no significant relationships exist between PDSI and burned area in the central Interior West. During cool-phase PDO, interannual climate has little influence on burned area in either the northern or the central Interior West. The opposite is true for the southern Interior West and the eastern slope of the Colorado Rockies using the Southern Oscillation Index and PDSI, respectively. The western slope of the Colorado Rockies is the only climate division or region in which burned area is not related to preceding PDSI. During warm-phase PDO, current PDSI explains 67% of the interannual variance in burned area on the western slope. These regional and temporal differences are most likely governed by variations in fuel dynamics associated with dominant regional and subregional vegetation types.

Detection of non-linearities in the dependence of burn area on fuel age and climatic variables

Evidence from Los Angeles County in California, USA suggests that the relationships between wildfire burn area and fuel age, temperature, precipitation, and fuel moisture are not linear. Instead, the relationships appear to have thresholds.The data seem to support the notion that fire risk is nearly constant provided various conditions are met: that fuel age and temperature exceed a given threshold, and that fuel moisture and precipitation are sufficiently low. There appears to be little distinction in terms of wildfire risk between conditions that are sufficient for wildfires and those that are extreme.