Benjamin C. Bright

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.

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and percent dead basal area (BA) from lidar metrics in five different beetle-affected coniferous forests across western North America. Study areas included the Kenai Peninsula of Alaska, southeastern Arizona, north-central Colorado, central Idaho, and central Oregon, U.S.A. We created RF models with and without intensity metrics as predictor variables and investigated how intensity normalization affected RF models in Idaho. RF models predicting total BA explained the most variation, whereas RF models predicting dead BA explained the least variation, with live and percent dead BA models explaining intermediate levels of variation. Important metrics varied between models depending on the type of BA being predicted. Generally, height and density metrics were important in predicting total BA, intensity and density metrics were important in predicting live BA, and intensity metrics were important in predicting dead and percent dead BA. Several lidar metrics were important across all study areas. Whether needles were on or off beetle-killed trees at the time of lidar acquisition could not be ascertained. Future work, where needle conditions at the time of lidar acquisition are known, could improve upon our analysis and results. Although RF models predicting live, dead, and percent dead BA did not perform as well as models predicting total BA, we concluded that discrete-return lidar can be used to provide reasonable estimations of live and dead BA. Our results also showed which lidar metrics have general utility across different coniferous forest types.

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.

Fire weather conditions and fire–atmosphere interactions observed during low-intensity prescribed fires – RxCADRE 2012

The role of fire-atmosphere coupling on fire behaviour is not well established, and to date few field observations have been made to investigate the interactions between fire spread and fire-induced winds. Therefore, comprehensive field observations are needed to better understand micrometeorological aspects of fire spread. To address this need, meteorological observations were made during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE) field campaign using a suite of meteorological instrumentation to measure both the ambient fire weather conditions and the fire-atmosphere interactions associated with the fires and plumes. Fire-atmosphere interactions are defined as the interactions between presently burning fuels and the atmosphere, in addition to interactions between fuels that will eventually burn in a given fire and the atmosphere (Potter 2012).

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.

Landsat time series and lidar as predictors of live and dead basal area across five bark beetle-affected forests

Bark beetle-caused tree mortality affects important forest ecosystem processes. Remote sensing methodologies that quantify live and dead basal area (BA) in bark beetle-affected forests can provide valuable information to forest managers and researchers. We compared the utility of light detection and ranging (lidar) and the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to predict total, live, dead, and percent dead BA in five bark beetle-affected forests in Alaska, Arizona, Colorado, Idaho, and Oregon, USA. The BA response variables were predicted from lidar and LandTrendr predictor variables using the random forest (RF) algorithm. RF models explained 28%-61% of the variation in BA responses. Lidar variables were better predictors of total and live BA, whereas LandTrendr variables were better predictors of dead and percent dead BA. RF models predicting percent dead BA were applied to lidar and LandTrendr grids to produce maps, which were then compared to a gridded dataset of tree mortality area derived from aerial detection survey (ADS) data. Spearman correlations of beetle-caused tree mortality metrics between lidar, LandTrendr, and ADS were low to moderate; low correlations may be due to plot sampling characteristics, RF model error, ADS data subjectivity, and confusion caused by the detection of other types of forest disturbance by LandTrendr. Provided these sources of error are not too large, our results show that lidar and LandTrendr can be used to predict and map live and dead BA in bark beetle-affected forests with moderate levels of accuracy.

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.

Landscape-scale analysis of aboveground tree carbon stocks affected by mountain pine beetles in Idaho

Bark beetle outbreaks kill billions of trees in western North America, and the resulting tree mortality can significantly impact local and regional carbon cycling. However, substantial variability in mortality occurs within outbreak areas. Our objective was to quantify landscape-scale effects of beetle infestations on aboveground carbon (AGC) stocks using field observations and remotely sensed data across a 5054 ha study area that had experienced a mountain pine beetle outbreak. Tree mortality was classified using multispectral imagery that separated green, red, and gray trees, and models relating field observations of AGC to LiDAR data were used to map AGC. We combined mortality and AGC maps to quantify AGC in beetle-killed trees. Thirty-nine per cent of the forested area was killed by beetles, with large spatial variability in mortality severity. For the entire study area, 40–50% of AGC was contained in beetle-killed trees. When considered on a per-hectare basis, 75–89% of the study area had >25% AGC in killed trees and 3–6% of the study area had >75% of the AGC in killed trees. Our results show that despite high variability in tree mortality within an outbreak area, bark beetle epidemics can have a large impact on AGC stocks at the landscape scale.

Introducing close-range photogrammetry for characterizing forest understory plant diversity and surface fuel structure at fine scales

Abstract Methods characterizing fine-scale fuels and plant diversity can advance understanding of plant-fire interactions across scales and help in efforts to monitor important ecosystems such as longleaf pine (Pinus palustris Mill.) forests of the southeastern United States. Here, we evaluate the utility of close-range photogrammetry for measuring fuels and plant diversity at fine scales (submeter) in a longleaf pine forest. We gathered point-intercept data of understory plants and fuels on nine 3-m2 plots at a 10-cm resolution. For these same plots, we used close-range photogrammetry to derive 3-dimensional (3D) point clouds representing understory plant height and color. Point clouds were summarized into distributional height and density metrics. We grouped 100 cm2 cells into fuel types, using cluster analysis. Comparison of photogrammetry heights with point-intercept measurements showed that photogrammetry points were weakly to moderately correlated to plant and fuel heights (r = 0.19–0.53). Mann–Whitney pairwise tests evaluating separability of fuel types, species, and plant types in terms of photogrammetry metrics were significant 44%, 41%, and 54% of the time, respectively. Overall accuracies using photogrammetry metrics to classify fuel types, species, and plant types were 44%, 39%, and 44%, respectively. This research introduces a new methodology for characterizing fine-scale 3D surface vegetation and fuels.