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Fire suppression and the loss of western white pine (WWP) have made northern Rocky Mountain moist mixed-conifer forests less disturbance resilient. Although managers are installing hundreds of plantations, most of these plantations have not experienced wildfire since establishment. In 2015, wildfires burned through 100 WWP plantations in this region, providing an opportunity to evaluate the effects of wildfires on sapling survival. A Weibull distribution approach was used to characterize the variation of fire severity pixels, as indicated by the differenced normalized burn ratio. The distribution parameters provided a method to identify the biophysical setting and plantation characteristics influencing fire severity and sapling survival. Plantations located on lower slope positions were more resistant to wildfires than plantations located midslope or close to the ridges. Snow water equivalent was positively correlated with wildfire resistance and resilience. Results will help focus reforestation efforts and identify locations where future plantations can potentially survive wildfires.

Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R2 = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.

The continuity and depth of the surface fuel layer (i.e., litter and duff) are major drivers of fire spread and fuel consumption. Nevertheless, its spatial explicit quantification over relatively large areas remains unresolved: local fuel heterogeneity introduces large uncertainties in estimates derived from field-based models and sparse data samples. Besides that, the sensitivity of remote sensors to surface litter loads is limited, particularly under canopy cover. In fire-maintained pine forests of the Southeastern US, surface fuel accumulation and its distribution over the forest floor are mainly driven by vegetation productivity, decomposition, and time since fire (TSF). Traditional ecological and stand-level models provide a means to equilibrate between opposing rates of deposition and decomposition as a function of TSF at the landscape level but don’t account for spatial heterogeneity. We developed a top-down, object-based approach for wall-to-wall estimation of surface litter loads using TSF records, the ecological-based Olson model, and tree crown objects derived from airborne laser scanning (ALS) data. The approach involves, first, the spatially explicit estimation of litter production through a tree crown production model, driven by tree crown attributes extracted from the ALS point clouds, and informed by tree inventory data and allometric equations, including vegetation leaf turnover rates. Second, litter accumulation is estimated using the fire-driven Olson equation, which models accumulation progressively with time until decomposition balances deposition and a steady state of accumulation is reached. The methodology is demonstrated at several fire-maintained longleaf pine forest locations in southeastern USA, where tree inventory data, surface litter loads, prescribed fire records, and ALS data are available for testing and validation of the methodology. Comparison between preliminary modeled estimates and observed litter loads shows a relatively good agreement (RMSE=0.21 [kg m-2]; BIAS 0.07 [kg m-2]). This suggests that the proposed approach to indirectly map patterns of litter production and litter accumulation can provide a realistic means to map the continuity of the litter layer, thus overcoming the limitation of traditional ecological landscape models to account for spatial heterogeneity. This high-resolution map of litter loads will be further valuable as input to physics-based fire behavior and spread models and to improve the spatially explicit characterization of the duff layer.

Research Highlights: Forest management is trending toward creating multi-aged forest structures and diverse vegetative compositions. The challenge is successfully designing and implementing treatments that create these diverse forests. Regeneration establishment is the most important step when applying a silvicultural system because it determines future treatments and optimizes management options. This study provided the minimum canopy openings that favor the establishment of shade-tolerant and shade-intolerant tree species to inform the implementation of uneven-aged management. Background and Objectives: A replicated study was implemented in 2007 in moist mixed-conifer forests to design, apply, and test two silvicultural concepts, canopy opening size and site preparation. Our objective in 2015 was to evaluate tree regeneration establishment and growth and understory vegetation in relation to these two silvicultural concepts. Materials and Methods: Canopy opening sizes as measured by lidar ranged from 15% to 100%, and through the application of prescribed fire, mastication, pile and burn, or no site preparation, different combinations of forest floor substrates were created. We stratified our study area into five canopy opening classes and four site preparation treatments. Using this stratified sampling scheme, we located 65 plots and measured tree species, abundance, 5-year height growth, and vegetative lifeforms. Results: The pile and burn site preparation favored the establishment of all six tree species. The canopy opening size of 55% to 92% favored the regeneration of both shade-tolerant and shade-intolerant species. Grand-fir 5-year height growth was significantly influenced by site preparation and canopy opening, and western white pine 5-year height growth was only influenced by canopy opening. Treatments did not influence vegetative richness. Conclusions: This study provided key treatment parameters in designing the regeneration step for uneven-aged management strategies with the goal of creating vegetative diversity and establishing shade-intolerant tree species in moist mixed-conifer forests.

Bark beetles cause significant tree mortality in western North America. The United States Forest Service coordinates annual insect and disease surveys (IDS) by observers in airplanes to map and quantify the tree mortality caused by beetles. The subjective nature of these surveys means that accuracy evaluation is important for characterizing uncertainty. Furthermore, the metric reported for quantifying tree mortality recently changed (2012&ndash, 2018 depending in region) from killed trees per acre to percent tree mortality within damage polygons, posing challenges for linking older and newer records. Here we evaluated IDS severity estimates in a beetle-affected forest in northern Idaho, USA using fine-resolution satellite imagery, which permitted greater areal coverage than field data. We first used well-established methods to map beetle-caused tree mortality in two WorldView-2 (WV2) images with a high accuracy relative to field observations. Trees-per-acre measurements within collocated IDS polygons were then converted to percent mortality using three methods and evaluated with the WV2 maps. The overall accuracies for the three methods ranged from 35&ndash, 38% (for methods that used five percent-mortality classes) and 49&ndash, 56% (three classes). When IDS and WV2 estimates of mortality severity that were within &plusmn, 15% of each other were considered accurate, overall accuracies were 71&ndash, 78%. Within the aerial survey damage polygons, the total mortality area tended to be overestimated relative to WV2. WV2 imagery identified ~50% more mortality across the study region compared with the IDS methods, with most of the difference occurring where damage was low severity or in wilderness areas. Severity of Douglas-fir beetle-caused tree mortality was estimated the most accurately, whereas severity of mountain pine beetle-caused tree mortality was estimated the least accurately. Future studies that control for temporal ambiguity between IDS and satellite imagery, as well as IDS spatial error, might provide better assessments of IDS severity accuracy. Our study increases the usefulness of the rich aerial survey database by providing estimates of uncertainty in the IDS database of tree mortality severity.

Background Fuel treatments are widely used to alter fuels in forested ecosystems to mitigate wildfire behavior and effects. However, few studies have examined long-term ecological effects of interacting fuel treatments (commercial harvests, pre-commercial thinnings, pile and burning, and prescribed fire) and wildfire. Using annually fitted Landsat satellite-derived Normalized Burn Ratio (NBR) curves and paired pre-fire treated and untreated field sites, we tested changes in the differenced NBR (dNBR) and years since treatment as predictors of biophysical attributes one and nine years after the 2007 Egley Fire Complex in Oregon, USA. We also assessed short- and long-term fuel treatment impacts on field-measured attributes one and nine years post fire. Results One-year post-fire burn severity (dNBR) was lower in treated than in untreated sites across the Egley Fire Complex. Annual NBR trends showed that treated sites nearly recovered to pre-fire values four years post fire, while untreated sites had a slower recovery rate. Time since treatment and dNBR significantly predicted tree canopy and understory green vegetation cover in 2008, suggesting that tree canopy and understory vegetation cover increased in areas that were treated recently pre fire. Live tree density was more affected by severity than by pre-fire treatment in either year, as was dead tree density one year post fire. In 2008, neither treatment nor severity affected percent cover of functional groups (shrub, graminoid, forb, invasive, and moss–lichen–fungi); however, by 2016, shrub, graminoid, forb, and invasive cover were higher in high-severity burn sites than in low-severity burn sites. Total fuel loads nine years post fire were higher in untreated, high-severity burn sites than any other sites. Tree canopy cover and density of trees, saplings, and seedlings were lower nine years post fire than one year post fire across treatments and severity, whereas live and dead tree basal area, understory surface cover, and fuel loads increased. Conclusions Pre-fire fuel treatments effectively lowered the occurrence of high-severity wildfire, likely due to successful pre-fire tree and sapling density and surface fuels reduction. This study also quantified the changes in vegetation and fuels from one to nine years post fire. We suggest that low-severity wildfire can meet prescribed fire management objectives of lowering surface fuel accumulations while not increasing overstory tree mortality.

Short-term post-fire field studies have shown that native shrub cover in chaparral ecosystems negatively affects introduced cover, which is influenced by burn severity, elevation, aspect, and climate. Using the southern California 2003 Old and Simi fires and the 2008 Sesnon Fire, we investigated the role of native shrubs in post-fire ecosystem responses across gradients of elevation, aspect, climate, burn severities, fire histories, and time. We collected field estimates of species cover in 2004 and 2015 at nested sampling sites. We used structural equation models with introduced and shrub cover as dependent variables. Shrub cover in 2004 was most influenced by the number of reburns, while shrub cover in 2015 was most influenced by the time between the two most recent fires. In 2004, introduced cover was most influenced by burn severity in 2003; similarly, in 2015, introduced cover was most influenced by burn severity in 2008. In both one and twelve years post fire, average precipitation increased the length of time between fires and decreased the number of times a site burned. This direct reduction in the number of times a site had burned due to average precipitation resulted in lower shrub cover one and twelve years post fire. Additionally, mean annual precipitation increased burn severity one year post fire, which resulted in lower introduced cover. However, this indirect relationship between precipitation and introduced cover through burn severity was no longer present twelve years post fire. Shrub cover increased with a longer average time between fires twelve years after fire. Shrub cover did not mediate any indirect relationships between burn severity or fire history metrics and introduced cover in either year, suggesting competitive exclusion of introduced species by shrubs. Our research found that significant fire effects on shrub and introduced species are often mediated by precipitation. Precipitation trends are likely to change fire regimes and thus alter plant community dynamics.

Few studies have examined post-fire vegetation recovery in temperate forest ecosystems with Landsat time series analysis. We analyzed time series of Normalized Burn Ratio (NBR) derived from LandTrendr spectral-temporal segmentation fitting to examine post-fire NBR recovery for several wildfires that occurred in three different coniferous forest types in western North America during the years 2000 to 2007. We summarized NBR recovery trends, and investigated the influence of burn severity, post-fire climate, and topography on post-fire vegetation recovery via random forest (RF) analysis. NBR recovery across forest types averaged 30 to 44% five years post fire, 47 to 72% ten years post fire, and 54 to 77% 13 years post fire, and varied by time since fire, severity, and forest type. Recovery rates were generally greatest for several years following fire. Recovery in terms of percent NBR was often greater for higher-severity patches. Recovery rates varied between forest types, with conifer−oak−chaparral showing the greatest NBR recovery rates, mixed conifer showing intermediate rates, and ponderosa pine showing slowest rates. Between 1 and 28% of patches had recovered to pre-fire NBR levels 9 to 16 years after fire, with greater percentages of low-severity patches showing full NBR recovery. Precipitation decreased and temperatures generally remained the same or increased post fire. Pre-fire NBR and burn severity were important predictors of NBR recovery for all forest types, and explained 2 to 6% of the variation in post-fire NBR recovery. Post-fire climate anomalies were also important predictors of NBR recovery and explained an additional 30 to 41% of the variation in post-fire NBR recovery. Landsat time series analysis was a useful means of describing and analyzing post-fire vegetation recovery across mixed-severity wildfire extents. We demonstrated that a relationship exists between post-fire vegetation recovery and climate in temperate ecosystems of western North America. Our methods could be applied to other burned landscapes for which spatially explicit measurements of post-fire vegetation recovery are needed.

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 aggregate...

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 lo...

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 ...

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Forest insect outbreaks have caused and will continue to cause extensive tree mortality worldwide, affecting ecosystem services provided by forests. Remote sensing is an effective tool for detecting and mapping tree mortality caused by forest insect outbreaks. In this study, we map insect-caused tree mortality across three coniferous forests in the Western United States for the years 1984 to 2018. First, we mapped mortality at the tree level using field observations and high-resolution multispectral imagery collected in 2010, 2011, and 2018. Using these high-resolution maps of tree mortality as reference images, we then classified moderate-resolution Landsat imagery as disturbed or undisturbed and for disturbed pixels, predicted percent tree mortality with random forest (RF) models. The classification approach and RF models were then applied to time series of Landsat imagery generated with Google Earth Engine (GEE) to create annual maps of percent tree mortality. We separated disturbed from undisturbed forest with overall accuracies of 74% to 80%. Cross-validated RF models explained 61% to 68% of the variation in percent tree mortality within disturbed 30-m pixels. Landsat-derived maps of tree mortality were comparable to vector aerial survey data for a variety of insect agents, in terms of spatial patterns of mortality and annual estimates of total mortality area. However, low-level tree mortality was not always detected. We conclude that our methodology has the potential to generate reasonable estimates of annual tree mortality across large extents.

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.

Remotely sensed radiation, attractive for its spatial and temporal coverage, offers a means of inferring energy deposition in fires (e.g. on soils, fuels and tree stems) but coordinated remote and in situ (in-flame) measurements are lacking. We relate remotely sensed measurements of fire radiative energy density (FRED) from nadir (overhead) radiometers on towers and the Wildfire Airborne Sensor Program (WASP) infrared camera on a piloted, fixed-wing aircraft to energy incident on in situ, horizontally oriented, wide-angle total flux sensors positioned ~0.5m above ground level. Measurements were obtained in non-forested herbaceous and shrub-dominated sites and in (forested) longleaf pine (Pinus palustris Miller) savanna. Using log–log scaling to reveal downward bias, incident energy was positively related to FRED from nadir radiometers (R2=0.47) and WASP (R2=0.50). As a demonstration of how this result could be used to describe ecological effects, we predict stem injury for turkey oak (Quercus laevis Walter), a common tree species at our study site, using incident energy inferred from remotely sensed FRED. On average, larger-diameter stems were expected to be killed in the forested than in the non-forested sites. Though the approach appears promising, challenges remain for remote and in situ measurement.

[1] Disturbances affect forest-atmosphere exchanges of carbon, water, and energy, thereby influencing weather and climate. Bark beetle outbreaks are one such disturbance type that alters biogeochemical and biogeophysical processes in forests. Few studies have documented bark beetle impacts to leaf area index (LAI), gross primary productivity (GPP), evapotranspiration (ET), land surface temperature (LST), and surface albedo with satellite observations. Our objective was to use Landsat-derived estimates of bark beetle-caused tree mortality and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products to estimate beetle-caused changes in LAI, GPP, ET, LST, and surface albedo in northern Colorado. Following bark beetle-caused tree mortality, decreases occurred in LAI (0.02–0.80 m2m−2, 1–40%), annual GPP (50–248 gC m−2 yr−1, (5–26%), and daily summer ET (0.20–0.70 mm day−1, 13–44%), whereas increases occurred in August LST (1–3.9 K) and February albedo (0.03–0.09, 19–52%). We found greater responses of these variables in areas of greater mortality severity. The extent and severity of tree mortality in northern Colorado caused substantial changes in land surface variables (9–23%) when averaged across all forested areas of our study area. Our results demonstrate that land surface variables are sensitive to bark beetle-caused tree mortality and that bark beetle outbreaks can significantly impact biogeochemical and biogeophysical processes.

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.

Mountain pine beetle outbreaks have caused widespread tree mortality in North American forests in recent decades, yet few studies have documented impacts on carbon cycling. In particular, landscape scales intermediate between stands and regions have not been well studied. Remote sensing is an effective tool for quantifying impacts of insect outbreaks on forest ecosystems at landscape scales. In this study, we developed and evaluated methodologies for quantifying aboveground carbon (AGC) stocks affected by mountain pine beetle using field observations, lidar data, and multispectral imagery. We evaluated methods at two scales, the plot level and the tree level, to ascertain the capability of each for mapping AGC impacts of bark beetle infestation across a forested landscape. In 27 plots across our 5054-ha study area in central Idaho, we measured tree locations, health, diameter, height, and other relevant attributes. We used allometric equations to estimate AGC content of individual trees and, in turn, summed tree AGC estimates to the plot level. Tree-level and plot-level AGC were then predicted from lidar metrics using separate statistical models. At the tree level, cross-validated additive models explained 50–54% of the variation in tree AGC (root mean square error (RMSE) values of 26–42 kg AGC, or 32–48%). At the plot level, a cross-validated linear model explained 84% of the variation in plot AGC (RMSE of 9.2 Mg AGC/ha, or 12%). To map beetle-caused tree mortality, we classified high-resolution digital aerial photography into green, red, and gray tree classes with an overall accuracy of 87% (kappa = 0.79) compared with our field observations. We then combined the multispectral classification with lidar-derived AGC estimates to quantify the amount of AGC within beetle-killed trees at the field plots. Errors in classification, apparent tree lean caused by off-nadir aerial imagery, and a bias between percent cover and percent AGC reduced accuracy when combining multispectral and lidar products. Plot-level models estimated total plot AGC more accurately than tree-level models summed for plots as determined by RMSE (9.2 versus 21 Mg AGC/ha, respectively) and mean bias error (0.52 versus − 6.7 Mg AGC/ha, respectively). When considering individual tree classes (green, red, gray) summed for plots, comparisons of plot-level and tree-level methods exhibited mixed results, with some accuracy measures higher for plot-level models. Despite a lack of clear improvement in tree-level models, we suggest that tree-level models should be considered for assessing situations with high spatial variability such as beetle outbreaks, especially if apparent tree lean effects can be minimized such as through the use of satellite imagery. Our methods illustrate the utility of combining lidar and multispectral imagery and can guide decisions about spatial resolution of analysis for understanding and documenting impacts of these forest disturbances.

The Cooney Ridge Fire Experiment conducted by fire scientists in 2003 was a burnout operation supported by a fire suppression crew on the active Cooney Ridge wildfire incident. The fire experiment included measurements of pre-fire fuels, active fire behavior, and immediate post-fire effects. Heat flux measurements collected at multiple scales with multiple ground and remote sensors illustrate the spatial and temporal complexity of the fire progression in relation to fuels and fire effects. We demonstrate how calculating cumulative heat release can provide a physically based estimate of fuel consumption that is indicative of fire effects. A map of cumulative heat release complements estimates of ground cover constituents derived from post-fire hyperspectral imagery for mapping immediate post-fire ground cover measures of litter and mineral soil. We also present one-year and 10-year post-fire measurements of overstory, understory, and surface conditions in a longer-term assessment of site recovery. At the time, the Cooney Ridge Fire Experiment exposed several limitations of current state-of-science fire measurement methods, many of which persist in wildfire and prescribed fire studies to this day. This Case Report documents an important milestone in relating multiple spatiotemporal measurements of pre-fire, active fire, and post-fire phenomena both on the ground and remotely.

Fire radiative energy density (FRED, J m−2) integrated from fire radiative power density (FRPD, W m−2) observations of landscape-level fires can present an undersampling problem when collected from fixed-wing aircraft. In the present study, the aircraft made multiple passes over the fire at ~3 min intervals, thus failing to observe most of the FRPD emitted as the flame front spread. We integrated the sparse FRPD time series to obtain pixel-level FRED estimates, and subsequently applied ordinary kriging (OK) and Gaussian conditional simulation (GCS) to interpolate across data voids caused by the undersampling. We compared FRED interpolated via OK and GCS with FRED estimated independently from ground measurements of biomass consumed from five prescribed burns at Eglin Air Force Base, Florida, USA. In four of five burns considered where undersampling prevailed, OK and GCS effectively interpolated FRED estimates across the data voids, improving the spatial distribution of FRED across the burning event and its overall mean. In a fifth burn, the burning characteristics were such that undersampling did not present a problem needing to be fixed. We also determined where burning and FRPD sampling characteristics merited applying OK and CGS only to the highest FRED estimates to interpolate more accurate FRED maps.

Fire radiative energy density (FRED, Jm-2) integrated from fire radiative power density (FRPD, Wm-2) observations of landscape-level fires can present an undersampling problem when collected from fixed-wing aircraft. In the present study, the aircraft made multiple passes over the fire at ~3min intervals, thus failing to observe most of the FRPD emitted as the flame front spread. We integrated the sparse FRPD time series to obtain pixel-level FRED estimates, and subsequently applied ordinary kriging (OK) and Gaussian conditional simulation (GCS) to interpolate across data voids caused by the undersampling. We compared FRED interpolated via OK and GCS with FRED estimated independently from ground measurements of biomass consumed from five prescribed burns at Eglin Air Force Base, Florida, USA. In four of five burns considered where undersampling prevailed, OK and GCS effectively interpolated FRED estimates across the data voids, improving the spatial distribution of FRED across the burning event and its overall mean. In a fifth burn, the burning characteristics were such that undersampling did not present a problem needing to be fixed. We also determined where burning and FRPD sampling characteristics merited applying OK and CGS only to the highest FRED estimates to interpolate more accurate FRED maps.

Frequently burned low-latitude coniferous forests maintain a high-diversity understory. Longleaf pine (Pinus palustris Mill.) forests and woodlands have exceptionally high diversity at fine scales and very frequent fire return intervals (1–3 yr). Furthermore, the positive association between high-frequency, low-intensity surface fires and high species richness in longleaf pine ecosystems is well documented but poorly understood. Recent studies have demonstrated additional linkages between specific fuel assemblages and fire intensity at small spatial scales. In this study, we build upon both patterns by using long-term datasets to examine the relationship between fire and specific fuel types, and how the combination of these two elements contributes to ground cover species diversity. We used 11 yr of monitoring data from longleaf pine forests at Eglin Air Force Base, Florida (USA), to parameterize a structural equation model that examines causal relationships between fuels and fire history on ground cover plant diversity. Overstory-derived fuels, including pine needle litter, pine cones, and other 10 and 100-h woody fuels, had the greatest positive impact on diversity in relatively open-canopied, frequently burned reference stands. A second model examined surface fuel components originating from the forest overstory as characterized by airborne light detection and ranging and found that pine needle litter was positively associated with canopy density. Our parameter estimates for causal relationships between easily measured variables and plant diversity will allow for the development of management models at the stand scale while being informed by fuels measured at the plot scale.

Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.

Multidecadal trends in areas burned with high severity shape ecological effects of fires, but most assessments are limited to ~30 years of satellite data. We analysed the proportion of area burned with high severity, the annual area burned with high severity, the probability areas burned with high severity and also the area reburned (all severities and high burn severity only) over 133 years across 346265ha within the Selway-Bitterroot Wilderness (SBW) Area in Idaho, United States. We used burn severity class inferred from digitised aerial photography (1880–2000) and satellite imagery (1973–2012). Over this long record, the proportion burned with high severity did not increase, despite extensive area burned in recent decades. Much greater area burned with high severity during the Early (1880–1934) and Late (1975–2012) periods than during the Middle period (1935–1974), paralleling trends in area burned. Little area reburned with high severity, and fires in the Early period limited the extent of fires burning decades later in the Late period. Our results suggest that long-term data across large areas provides useful context on recent trends, and that projections for the extent and severity of future fires must consider prior fires and fire management.

Recent bark beetle outbreaks in western North America have been widespread and severe. Forest managers need accurate information on beetle-induced tree mortality to make better decisions on how best to remediate beetle-killed forests and restore healthy ecosystem services. We applied the LandTrendr analysis tool, which can detect beetle-induced tree mortality, to Landsat image time series (1984-2010) at study areas affected by bark beetles in Oregon, Alaska, Idaho, Colorado, and Arizona. LandTrendr outputs indicating the timing, magnitude, and duration of disturbance events were generated consistently in all study areas considered and were used to predict Dead Basal Area (BA), Live BA, Total BA, and Percent Dead BA, as summarized from field plot observations geolocated within each study area, using The Random Forest (RF) classification and regression tree algorithm. RF models explained 11-53, 15-55, 11-47, and 17-56% of variance in total, live, dead, and %dead BA, respectively. RF models based on plots from all study areas explained 42, 30, 47, and 51% of variance in total, live, dead, and %dead BA, respectively. This was generally higher than the variance explained within a single study area. These results demonstrate potential for applying LandTrendr for mapping the timing, duration, spatial extent, and magnitude of bark beetle infestations from the historical Landsat image record, which would help forest scientists and managers to better map and monitor beetle effects on coniferous forests and manage their recovery.

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.

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.

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.

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).

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.