Utilization of remote sensing techniques for the quantification of fire behavior in two pine stands
Introduction
Significant gaps remain in the current understanding of the contribution of different driving mechanisms to the spread of large-scale outdoor fires, such as wildland fires [1]. A particular difficulty lies in the fact that laboratory tests, while offering many insights, cannot fully account for and scale the relevant conditions and phenomena [2]. Thus, field-scale measurements of fire behavior are paramount for increasing the current scientific understanding and developing models of fire behavior, particularly those employing detailed physics-based formulations [3].
Experimental measurement of fire behavior has been conducted in the field for grasslands (e.g. [4], [5], [6], [7]), shrublands (e.g. [8], [9], [10]), and forested environments (e.g. [11], [12], [13]). However, the collection of work is limited by the fact that large scale experimental fires can be dangerous and resource intensive, with a significant potential for shortcomings. High intensity fires can also prove to be difficult to instrument successfully. Further, many studies report only statistics for a series of fires, without examining any particular fire in detail (e.g. [12]). This is valuable for creating empirical models, but does not provide sufficient information required for detailed analysis of singular fires (which often have dynamic behavior in the field), such as comprehensive time histories [4], [5], [6], [9]. With appropriate measurement, a single spreading fire can offer insight into detailed aspects of fire behavior at different locations in space and time [9]. This kind of analysis is required for exploring the physics and testing detailed models against specific experiments.
In this study, two experimental fires were carried out in the context of several larger objectives, including quantifying the effect of fire (particularly prescribed fire) on fuel loading and structure, and providing datasets necessary to test detailed physics-based fire behavior models. However, the current study aims to develop a broad assessment of fire behavior, while examining the capabilities of non-intrusive measurements to fully explain the observations. Detailed measurements of the flame region are ultimately important, particularly for model testing, and were a part of the overall study. Nevertheless, it is worth critiquing how well more general measurements (i.e. characteristics of wind and fuel) can explain the fire behavior. These types of measurements, along with spread rate and some flame geometry, are the most often made in field-scale experiments, as wind and fuel are known to drive fire behavior. However, it must be examined whether these efforts are sufficient to increase the current understanding of the underlying mechanisms. This work also provides the baseline inputs necessary for the subsequent modeling of fire behavior in this type of environment [14].
Here, we take advantage of the relatively recent development and improvement of advanced remote sensing techniques, which allow for detailed measurement of both fuel structure and local fire behavior for individual fires. Aerial infrared (IR) and Light Detection and Ranging (LiDAR) sensors were utilized to monitor the fire spread and canopy fuel structure, respectively. Fuel measurements were supplemented by destructive sampling, and ambient wind conditions were recorded outside the burn areas. An assessment of the respective influences of fuel and wind conditions on changes in fire behavior was carried out to understand their relative importance.
Section snippets
Study site
The two experiments (EX1 and EX2) were carried out in the Pinelands National Reserve (PNR) of New Jersey, United States. This reserve covers an area of approximately 445,000 ha, and is the site of an active prescribed fires program by the New Jersey Forest Fire Service (NJFFS) and federal wildland fire managers. This is intended to reduce fuel loads and thus mitigate fire risk [15]. The climate is classified as cool temperate, with mean monthly temperatures of 0.3 °C in January and 24.3 °C in
Experimental conditions and fire spread
A summary of the experimental conditions is given in Table 1 and FMC is given in Table 2. Data for the forest floor FMC was not available for EX2, however, both fires were carried out under light winds and cool temperatures with moderate FMC, representative of the spring prescribed burn season [21]. Given the seasonal similarity of ambient weather conditions that drive dead fuel moisture dynamics and the consistency of the other values between years, the forest floor FMC is expected to be
Fire characteristics
Overall, the fire behavior of the two experiments was comparable, spreading as surface fires with localized regions of passive crown fire. The peak spread rate estimates were similar, though EX2 had a significant period of slow, low intensity spread through the middle of the block, with spread rates varying by an order of magnitude between the minimum and maximum. Consistency between the two experiments can be attributed to the similar fuel loadings, relatively low wind speeds, and cool
Conclusions
This general overview of the fire behavior observed in two experiments offers insight into the behavior and impact that fires in the PNR can have during the late winter and early spring, placing a particular focus on remote sensing techniques. With similar pre-fire fuel loadings, the fires spread primarily through the surface fuels with moderate, but variable spread rates (0.01–0.2 m s-1) and surface fireline intensities (200–4400 kW m-1), though occasional torching and isolated regions of more
Acknowledgements
The authors wish to thank the Joint Fire Science Program (JFSP) for funding this research effort (project #12-1-03–11), and the NJFFS, particularly Division Firewarden (ret.) James Dusha and Section Firewarden Ashley House, for their strong support in the planning and execution of the experimental fires. Dr. Filkov was supported by The Tomsk State University Academic D.I. Mendeleev Fund Program.
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