Patrick H. Freeborn

Climate-induced variations in global wildfire danger from 1979 to 2013

Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earths vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

Thermal remote sensing of active vegetation fires and biomass burning events [Chapter 18]

his book provides a comprehensive overview of the state of the art in the field of thermal infrared remote sensing. Temperature is one of the most important physical environmental variables monitored by earth observing remote sensing systems. Temperature ranges define the boundaries of habitats on our planet. Thermal hazards endanger our resources and well-being. In this book renowned international experts have contributed chapters on currently available thermal sensors as well as innovative plans for future missions. Further chapters discuss the underlying physics and image processing techniques for analyzing thermal data. Ground-breaking chapters on applications present a wide variety of case studies leading to a deepened understanding of land and sea surface temperature dynamics, urban heat island effects, forest fires, volcanic eruption precursors, underground coal fires, geothermal systems, soil moisture variability, and temperature-based mineral discrimination. ‘Thermal Infrared Remote Sensing: Sensors, Methods, Applications’ is unique because of the large field it spans, the potentials it reveals, and the detail it providesThermal remote sensing is widely used in the detection, study, and management of biomass burning occurring in open vegetation fires. Such fires may be planned for land management purposes, may occur as a result of a malicious or accidental ignition by humans, or may result from lightning or other natural phenomena. Under suitable conditions, fires may spread rapidly and extensively, affecting the land cover properties of large areas, and releasing a wide variety of gases and particulates directly into Earth’s troposphere. On average, around 3.4 % of the Earth’s terrestrially vegetated area burns annually in this way. Vegetation fires inevitably involve high temperatures, so thermal remote sensing is well suited to its identification and study. Here we review the theoretical basis of the key approaches used to (1) detect actively burning fires; (2) characterize sub-pixel fires; and (3) estimate fuel consumption and smoke emissions. We describe the types of airborne and spaceborne systems that deliver data for use with these active fire thermal remote sensing methods, and provide some examples of how operational fire management and fire research have both benefited from the resulting information. We commence with a brief review of the significance and magnitude of biomass burning, both within the ‘whole Earth’ system and in more regional situations, aiming to highlight why thermal remote sensing has become so important to the study and management of open vegetation burning.

Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation

Satellite measurements of fire radiative power (FRP) are increasingly used to estimate the contribution of biomass burning to local and global carbon budgets. Without an associated uncertainty, however, FRP-based biomass burning estimates cannot be confidently compared across space and time, or against estimates derived from alternative methodologies. This work addresses this issue and quantifies the precision of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of FRP by collecting duplicate, off-nadir, overlapping observations of the same fires. Differences in the per-pixel FRP measured near-simultaneously in consecutive MODIS scans are approximately normally distributed with a standard deviation (ση) of 26.6%. Simulations demonstrate that this uncertainty decreases to less than ~5% (at ±1 ση) for aggregations larger than ~50 MODIS active fire pixels. Although FRP uncertainties limit the confidence in flux estimates on a per-pixel basis, the sensitivity of biomass burning estimates to FRP uncertainties can be mitigated by conducting inventories at coarser spatiotemporal resolutions.

Laboratory investigation of fire radiative energy and smoke aerosol emissions

[1] Fuel biomass samples from southern Africa and the United States were burned in a laboratory combustion chamber while measuring the biomass consumption rate, the fire radiative energy (FRE) release rate (Rfre), and the smoke concentrations of carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). The PM mass emission rate (RPM) was quantified from aerosol optical thickness (AOT) derived from smoke extinction measurements using a custom-made laser transmissometer. The RPM and Rfre time series for each fire were integrated to total PM mass and FRE, respectively, the ratio of which represents its FRE-based PM emission coefficient (Ce ). A strong correlation (r 2 = 0.82) was found between the total FRE and total PM mass, from which an average Ce value of 0.03 kg MJ �1 was calculated. This value agrees with those derived similarly from satellite-borne measurements of Rfre and AOT acquired over large-scale wildfires.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

Quantifying the influence of previously burned areas on suppression effectiveness and avoided exposure: a case study of the Las Conchas Fire

We present a case study of the Las Conchas Fire (2011) to explore the role of previously burned areas (wildfires and prescribed fires) on suppression effectiveness and avoided exposure. Methodological innovations include characterisation of the joint dynamics of fire growth and suppression activities, development of a fire line effectiveness framework, and quantification of relative fire line efficiencies inside and outside of previously burned areas. We provide descriptive statistics of several fire line effectiveness metrics. Additionally, we leverage burn probability modelling to examine how burned areas could have affected fire spread potential and subsequent exposure of highly valued resources and assets to fire. Results indicate that previous large fires exhibited significant and variable impacts on suppression effectiveness and fire spread potential. Most notably the Cerro Grande Fire (2000) likely exerted a significant and positive influence on containment, and in the absence of that fire the community of Los Alamos and the Los Alamos National Laboratory could have been exposed to higher potential for loss. Although our scope of inference is limited results are consistent with other research, suggesting that fires can exert negative feedbacks that can reduce resistance to control and enhance the effectiveness of suppression activities on future fires.

A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic

Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through space and time to determine their cumulative impact on the characterization of individual fire regime parameters. Here we perform the first ever decade long, multi-scale map comparison of fire chronologies and fire seasonality derived from three publicly available satellite-based fire products: the MODIS active fire product (MCD14ML), the ATSR nighttime World Fire Atlas (WFA), and the MODIS burned area product (MCD45A1). Results indicate that: (i) the agreement between fire chronologies derived from two dissimilar satellite products improves as fire pixels are aggregated into coarser grid cells, but diminishes as the number of years included in the time series increases; and (ii) all three datasets provide distinctly different portraits of the onset, peak, and duration of the fire season regardless of the map resolution. Differences in regional, long-term fire regime parameters derived from the three datasets are attributed to the unique capability of each sensor and detection algorithm to recognize geographical gradients, seasonal oscillations, decadal trends, and interannual variability in active fire characteristics and burned area patterns. Since different satellite sensors and detection algorithm strategies are sensitive to different types of fires, we demonstrate that disagreements in fire regime maps derived from dissimilar satellite-based fire products can be used as an advantage to highlight spatial and temporal transitions in landscape fire activity. Given access to multiple, publically available datasets, we caution against describing fire regimes using a single satellite-based active fire or burned area product.

Thermal Remote Sensing of Active Vegetation Fires and Biomass Burning Events

Thermal remote sensing is widely used in the detection, study, and management of biomass burning occurring in open vegetation fires. Such fires may be planned for land management purposes, may occur as a result of a malicious or accidental ignition by humans, or may result from lightning or other natural phenomena. Under suitable conditions, fires may spread rapidly and extensively, affecting the land cover properties of large areas, and releasing a wide variety of gases and particulates directly into Earth’s troposphere. On average, around 3.4 % of the Earth’s terrestrially vegetated area burns annually in this way. Vegetation fires inevitably involve high temperatures, so thermal remote sensing is well suited to its identification and study. Here we review the theoretical basis of the key approaches used to (1) detect actively burning fires; (2) characterize sub-pixel fires; and (3) estimate fuel consumption and smoke emissions. We describe the types of airborne and spaceborne systems that deliver data for use with these active fire thermal remote sensing methods, and provide some examples of how operational fire management and fire research have both benefited from the resulting information. We commence with a brief review of the significance and magnitude of biomass burning, both within the ‘whole Earth’ system and in more regional situations, aiming to highlight why thermal remote sensing has become so important to the study and management of open vegetation burning.

Relationships between fire danger and the daily number and daily growth of active incidents burning in the northern Rocky Mountains, USA

Daily National Fire Danger Rating System (NFDRS) indices are typically associated with the number and final size of newly discovered fires, or averaged over time and associated with the likelihood and total burned area of large fires. Herein we used a decade (2003–12) of NFDRS indices and US Forest Service (USFS) fire reports to examine daily relationships between fire danger and the number and growth rate of wildfires burning within a single predictive service area (PSA) in the Northern Rockies, USA. Results demonstrate that daily associations can be used to: (1) extend the utility of the NFDRS beyond the discovery date of new fires; (2) examine and justify the temporal window within which daily fire danger indices are averaged and related to total burned area; (3) quantify the probability of managing an active incident as a function of fire danger; and (4) quantify the magnitude and variability of daily fire growth as a function of fire danger. The methods herein can be extended to other areas with a daily history of weather and fire records, and can be used to better inform fire management decisions or to compare regional responses of daily fire activity to changes in fire danger.

Towards improving wildland firefighter situational awareness through daily fire behaviour risk assessments in the US Northern Rockies and Northern Great Basin

Wildland firefighters must assess potential fire behaviour in order to develop appropriate strategies and tactics that will safely meet objectives. Fire danger indices integrate surface weather conditions to quantify potential variations in fire spread rates and intensities and therefore should closely relate to observed fire behaviour. These indices could better inform fire management decisions if they were linked directly to observed fire behaviour. Here, we present a simple framework for relating fire danger indices to observed categorical wildland fire behaviour. Ordinal logistic regressions are used to model the probabilities of five distinct fire behaviour categories that are then combined with a safety-based weight function to calculate a Fire Behaviour Risk rating that can plotted over time and spatially mapped. We demonstrate its development and use across three adjacent US National Forests. Finally, we compare predicted fire behaviour risk ratings with observed variations in satellite-measured fire radiative power and we link these models with spatial fire danger maps to demonstrate the utility of this approach for landscape-scale fire behaviour risk assessment. This approach transforms fire weather conditions into simple and actionable fire behaviour risk metrics that wildland firefighters can use to support decisions that meet required objectives and keep people safe.