E. Louise Loudermilk

Measurements relating fire radiative energy density and surface fuel consumption – RxCADRE 2011 and 2012

Small-scale experiments have demonstrated that fire radiative energy is linearly related to fuel combusted but such a relationship has not been shown at the landscape level of prescribed fires. This paper presents field and remotely sensed measures of pre-fire fuel loads, consumption, fire radiative energy density (FRED) and fire radiative power flux density (FRFD), from which FRED is integrated, across forested and non-forested RxCADRE 2011 and 2012 burn blocks. Airborne longwave infrared (LWIR) image time series were calibrated to FRFD and integrated to provide FRED. Surface fuel loads measured in clip sample plots were predicted across burn blocks from airborne lidar-derived metrics. Maps of surface fuels and FRED were corrected for occlusion of the radiometric signal by the overstorey canopy in the forested blocks, and FRED maps were further corrected for temporal and spatial undersampling of FRFD. Fuel consumption predicted from FRED derived from both airborne LWIR imagery and various ground validation sensors approached a linear relationship with observed fuel consumption, which matched our expectation. These field, airborne lidar and LWIR image datasets, both before and after calibrations and corrections have been applied, will be made publicly available from a permanent archive for further analysis and to facilitate fire modelling.

Measuring Radiant Emissions from Entire Prescribed Fires with Ground, Airborne and Satellite Sensors RxCADRE 2012

Characterising radiation from wildland fires is an important focus of fire science because radiation relates directly to the combustion process and can be measured across a wide range of spatial extents and resolutions. As part of a more comprehensive set of measurements collected during the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research (RxCADRE) field campaign, we used ground, airborne and spaceborne sensors to measure fire radiative power (FRP) from whole fires, applying different methods to small (2 ha) and large (>100 ha) burn blocks. For small blocks (n = 6), FRP estimated from an obliquely oriented long-wave infrared (LWIR) camera mounted on a boom lift were compared with FRP derived from combined data from tower-mounted radiometers and remotely piloted aircraft systems (RPAS). For large burn blocks (n = 3), satellite FRP measurements from the Moderate-resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors were compared with near-coincident FRP measurements derived from a LWIR imaging system aboard a piloted aircraft. We describe measurements and consider their strengths and weaknesses. Until quantitative sensors exist for small RPAS, their use in fire research will remain limited. For oblique, airborne and satellite sensors, further FRP measurement development is needed along with greater replication of coincident measurements, which we show to be feasible.

High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012

Wildland fire radiant energy emission is one of the only measurements of combustion that can be made at wide spatial extents and high temporal and spatial resolutions. Furthermore, spatially and temporally explicit measurements are critical for making inferences about fire effects and useful for examining patterns of fire spread. In this study we describe our methods for capturing and analysing spatially and temporally explicit long-wave infrared (LWIR) imagery from the RxCADRE (Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment) project and examine the usefulness of these data in investigating fire behaviour and effects. We compare LWIR imagery captured at fine and moderate spatial and temporal resolutions (from 1 cm2 to 1 m2; and from 0.12 to 1 Hz) using both nadir and oblique measurements. We analyse fine-scale spatial heterogeneity of fire radiant power and energy released in several experimental burns. There was concurrence between the measurements, although the oblique view estimates of fire radiative power were consistently higher than the nadir view estimates. The nadir measurements illustrate the significance of fuel characteristics, particularly type and connectivity, in driving spatial variability at fine scales. The nadir and oblique measurements illustrate the usefulness of the data for describing the location and movement of the fire front at discrete moments in time at these fine and moderate resolutions. Spatially and temporally resolved data from these techniques show promise to effectively link the combustion environment with post-fire processes, remote sensing at larger scales and wildland fire modelling efforts.

Observations of energy transport and rate of spreads from low-intensity fires in longleaf pine habitat – RxCADRE 2012

Wildland fire rate of spread (ROS) and intensity are determined by the mode and magnitude of energy transport from the flames to the unburned fuels. Measurements of radiant and convective heating and cooling from experimental fires are reported here. Sensors were located nominally 0.5 m above ground level. Flame heights varied from 0.3 to 1.8 m and flaming zone depth varied from 0.3 to 3.0 m. Fire ROS derived from observations of fire transit time between sensors was 0.10 to 0.48 m s–1. ROS derived from ocular estimates reached 0.51 m s–1 for heading fire and 0.25 m s–1 for backing fire. Measurements of peak radiant and total energy incident on the sensors during flame presence reached 18.8 and 36.7 kW m–2 respectively. Peak air temperatures reached 1159°C. Calculated fire radiative energy varied from 7 to 162 kJ m–2 and fire total energy varied from 3 to 261 kJ m–2. Measurements of flame emissive power peaked at 95 kW m–2. Average horizontal air flow in the direction of flame spread immediately before, during, and shortly after the flame arrival reached 8.8 m s–1, with reverse drafts of 1.5 m s–1; vertical velocities varied from 9.9 m s–1 upward flow to 4.5 m s–1 downward flow. The observations from these fires contribute to the overall understanding of energy transport in wildland fires.

Canopy-derived fuels drive patterns of in-fire energy release and understory plant mortality in a longleaf pine ( Pinus palustris ) sandhill in northwest Florida, USA

Abstract Wildland fire radiant energy emission is one of the only measurements of combustion that can be made at high temporal and spatial resolutions. Furthermore, spatially and temporally explicit measurements are critical for making inferences about ecological fire effects. Although the correlation between fire frequency and plant biological diversity in frequently burned coniferous forests is well documented, the ecological mechanisms explaining this relationship remains elusive. Uncovering these mechanisms will require highly resolved, spatially explicit fire data (Loudermilk et al. 2012). Here, we describe our efforts at connecting spatial variability in fuels to fire energy release and fire effects using fine scale (1 cm2) longwave infrared (LWIR) thermal imagery. We expected that the observed variability in fire radiative energy release driven by canopy-derived fuels could be the causal mechanism driving plant mortality, an important component of community dynamics. Analysis of fire radiant energy released in several experimental burns documented a close connection among patterns of fire intensity and plant mortality. Our results also confirmed the significance of cones in driving fine-scale spatial variability of fire intensity. Spatially and temporally resolved data from these techniques show promise to effectively link the combustion environment with postfire processes, remote sensing at larger scales, and wildland fire modeling efforts.

Imputation of individual Longleaf Pine (Pinus palustris Mill.) Tree attributes from field and LiDAR data

Abstract Light Detection and Ranging (LiDAR) has demonstrated potential for forest inventory at the individual-tree level. The aim in this study was to predict individual-tree height (Ht; m), basal area (BA; m2), and stem volume (V; m3) attributes, imputing Random Forest k-nearest neighbor (RF k-NN) and individual-tree-level-based metrics extracted from a LiDAR-derived canopy height model (CHM) in a longleaf pine (Pinus palustris Mill.) forest in southwestern Georgia, United States. We developed a new framework for modeling tree-level forest attributes that comprise 3 steps: (i) individual tree detection, crown delineation, and tree-level-based metrics computation from LiDAR-derived CHM; (ii) automatic matching of LiDAR-derived trees and field-based trees for a regression modeling step using a novel algorithm; and (iii) RF k-NN imputation modeling for estimating tree-level Ht, BA, and V and subsequent summarization of these metrics at the plot and stand levels. RMSDs for tree-level Ht, BA, and V were 2.96%, 58.62%, and 8.19%, respectively. Although BA estimation accuracy was poor because of the longleaf pine growth habitat, individual-tree locations, Ht, and V were estimated with high accuracy, especially in low-canopy-cover conditions. Future efforts based on the findings could help improve the estimation accuracy of individual-tree-level attributes such as BA.

Mapping Forest Structure and Composition from Low-Density LiDAR for Informed Forest, Fuel, and Fire Management at Eglin Air Force Base, Florida, USA

Abstract Eglin Air Force Base (AFB) in Florida, in the United States, conserves a large reservoir of native longleaf pine (Pinus palustris Mill.) stands that land managers maintain by using frequent fires. We predicted tree density, basal area, and dominant tree species from 195 forest inventory plots, low-density airborne LiDAR, and Landsat data available across the entirety of Eglin AFB. We used the Random Forests (RF) machine learning algorithm to predict the 3 overstory responses via univariate regression or classification, or multivariate k-NN imputation. Ten predictor variables explained ∼ 50% of variation and were used in all models. Model accuracy and precision statistics were similar among the various RF approaches, so we chose the imputation approach for its advantage of allowing prediction of the ancillary plot attributes of surface fuels and ground cover plant species richness. Maps of the 3 overstory response variables and ancillary attributes were imputed at 30-m resolution and then aggregated to the management block level, where they were significantly correlated with each other and with fire history variables summarized from independent data. We conclude that functional relationships among overstory structure, surface fuels, species richness, and fire history emerge and become more apparent at the block level where management decisions are made.

High-resolution observations of combustion in heterogeneous surface fuels

In ecosystems with frequent surface fires, fire and fuel heterogeneity at relevant scales have been largely ignored. This could be because complete burns give an impression of homogeneity, or due to the difficulty in capturing fine-scale variation in fuel characteristics and fire behaviour. Fire movement between patches of fuel can have implications for modelling fire spread and understanding ecological effects. We collected high resolution (0.8×0.8-cm pixels) visual and thermal imaging data during fire passage over 4×4-m plots of mixed fuel beds consisting of pine litter and grass during two prescribed burns within the longleaf pine forests of Eglin Air Force Base, FL in February 2011. Fuel types were identified by passing multi-spectral digital images through a colour recognition algorithm in ‘Rabbit Rules,’ an experimental coupled fire-atmosphere fire spread model. Image fuel types were validated against field fuel types. Relationships between fuel characteristics and fire behaviour measurements at multiple resolutions (0.8×0.8cm to 33×33cm) were analysed using a regression tree approach. There were strong relationships between fire behaviour and fuels, especially at the 33×33-cm scale (R2=0.40–0.69), where image-to-image overlap error was reduced and fuels were well characterised. Distinct signatures were found for individual and coupled fuel types for determining fire behaviour, illustrating the importance of understanding fire-fuel heterogeneity at fine-scales. Simulating fire spread at this fine-scale may be critical for understanding fire effects, such as understorey plant community assembly.

Interactions Among Fuel Management, Species Composition, Bark Beetles, and Climate Change and the Potential Effects on Forests of the Lake Tahoe Basin

AbstractClimate-driven increases in wildfires, drought conditions, and insect outbreaks are critical threats to forest carbon stores. In particular, bark beetles are important disturbance agents although their long-term interactions with future climate change are poorly understood. Droughts and the associated moisture deficit contribute to the onset of bark beetle outbreaks although outbreak extent and severity is dependent upon the density of host trees, wildfire, and forest management. Our objective was to estimate the effects of climate change and bark beetle outbreaks on ecosystem carbon dynamics over the next century in a western US forest. Specifically, we hypothesized that (a) bark beetle outbreaks under climate change would reduce net ecosystem carbon balance (NECB) and increase uncertainty and (b) these effects could be ameliorated by fuels management. We also examined the specific tree species dynamics—competition and release—that determined NECB response to bark beetle outbreaks. Our study area was the Lake Tahoe Basin (LTB), CA and NV, USA, an area of diverse forest types encompassing steep elevation and climatic gradients and representative of mixed-conifer forests throughout the western United States. We simulated climate change, bark beetles, wildfire, and fuels management using a landscape-scale stochastic model of disturbance and succession. We simulated the period 2010–2100 using downscaled climate projections. Recurring droughts generated conditions conducive to large-scale outbreaks; the resulting large and sustained outbreaks significantly increased the probability of LTB forests becoming C sources over decadal time scales, with slower-than-anticipated landscape-scale recovery. Tree species composition was substantially altered with a reduction in functional redundancy and productivity. Results indicate heightened uncertainty due to the synergistic influences of climate change and interacting disturbances. Our results further indicate that current fuel management practices will not be effective at reducing landscape-scale outbreak mortality. Our results provide critical insights into the interaction of drivers (bark beetles, wildfire, fuel management) that increase the risk of C loss and shifting community composition if bark beetle outbreaks become more frequent.

Using Simulated 3D Surface Fuelbeds and Terrestrial Laser Scan Data to Develop Inputs to Fire Behavior Models

Abstract Understanding fine-scale variability in understory fuels is increasingly important as physics-based fire behavior models drive needs for higher-resolution data. Describing fuelbeds 3Dly is critical in determining vertical and horizontal distributions of fuel elements and the mass, especially in frequently burned pine ecosystems where fine-scale fuels arrangement drives fire intensity and resulting fire effects. Here, we describe research involving the use of highly resolved 3D models. We create fuelbeds using individual grass, litter, and pinecone models designed from field measurements. These fuel models are distributed throughout the fuelbed to replicate fuel distribution in rectified nadir photography taken for each plot. The simulated fuelbeds are converted into voxel arrays and biomass is estimated from calculated surface area between mesh vertices for each voxel. We compare field-based fuel depth and biomass with simulated estimates to demonstrate similarities and differences. Biomass distributions between simulated fuel beds and terrestrial laser scan data correlated well using Weibull shape parameters (r = 0.86). Our findings indicate that integration of field, simulated, and terrestrial laser scanner data will improve characterization of fuel mass, type, and spatial allocations that are important inputs to physics-based fire behavior models.