Canopy Bulk Density Research Articles

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

BackgroundThe capacity of forest fuel treatments to moderate the behavior and severity of subsequent wildfires depends on weather and fuel conditions at the time of burning. However, in-depth evaluations of how treatments perform are limited because encounters between wildfires and areas with extensive pre-fire data are rare. Here, we took advantage of a 1200-ha randomized and replicated experiment that burned almost entirely in a subsequent wildfire under a wide range of weather conditions. We compared the impacts of four fuel treatments on fire severity, including two thin-only, a thin-burn, a burn-only, and an untreated control. We evaluated four fire severity metrics—tree mortality, average bole char height, percent crown volume consumed (PCVC), and percent crown volume affected (PCVA)—and leveraged data from pre-fire surface and canopy fuels to better understand the mechanisms driving differences in wildfire severity among treatments and how they changed with fire weather.ResultsWe found strong mitigating effects of treatments on fire behavior and tree mortality, despite 20 years having elapsed since mechanical thinning and 10 years since the second entry of prescribed fire. The thin-burn treatment resulted in the lowest fire severity across all four metrics and the untreated control the highest. All four fire severity metrics were positively associated with pre-fire canopy and surface fuel loads, with the exception that PCVC (a fire severity metric related to crown fire behavior) was not associated with surface fuel load. The fire weather conditions under which fuel treatment was most effective varied among fire severity metrics. Fuel treatment benefit was maximized at intermediate burning index values for tree mortality, intermediate to high burning index values for PCVA, and high burning index for bole char height and PCVC.ConclusionsWe conclude that reducing canopy bulk density via mechanical thinning treatments can help to limit crown fire behavior for 20 years or more. However, reducing surface fuels is necessary to limit scorching and the total crown impacts associated with tree mortality. Further, while fuel treatment effectiveness may decline under the most severe fire weather conditions for fire severity metrics associated with tree mortality, it is maximized under severe fire weather conditions for fire severity metrics associated with crown fire behavior (bole charring and torching). Our results provide strong evidence for the use of fuel treatments to mitigate fire behavior and resulting fire severity even under extreme fire weather conditions.

Use of national forest inventory data to develop stand density driven models for understorey shrubs and overstorey fuel variables and associated temporal dynamics in commercial plantations

Biomass tends to accumulate both in overstorey and understorey layers of forests growing in productive areas. The rapid growth of forest plantations established in productive areas can thus also lead to the creation of fire-prone landscapes. In this study, we assessed the extent to which overstorey stand density determines current values of the fine shrub fuel load (Wshr_G1) in the understorey and also two major canopy fuel characteristics related to crown fire activity -canopy base height (CBH) and canopy bulk density (CBD)- and the temporal dynamics of these. For this purpose, we developed two types of models for the three fuel complex variables: i) extreme response models, to define the upper limit of Wshr_G1 and CBD and the lower limit of CBH, the values of which may depend on stand density; and ii) dynamic models, to estimate the probability of increasing fuel loads and rate of change when a more hazardous situation is predicted. Data were obtained from 8087 plots, measured in the third, fourth and fifth Spanish National Forest Inventories, in plantations dominated by the major commercial species in northern Spain (Pinus pinaster, Pinus radiata, Pinus sylvestris and Eucalyptus globulus). The extreme response models generally explain more than 60% of the observed variability in the three fuel complex variables. According to these models, maximum shrub fuel load and CBD and minimum CBH in stands of the four tree species were limited by stand basal area, with different responses by different species. The dynamics of the fuel complex variables were determined by canopy cover, estimated as a proxy for stand density. The use of a two-step regression approach enabled realistic modelling of the dynamics of the fuel complex variables, allowing for a decrease in the shrub fuel biomass and in CBD between two time points. The dynamic models developed are age-independent and are therefore useful for practical applications because they can be applied to any stand without the need to know the stand age. Overall, the study findings provide insights into the complex relationship between both understorey and overstorey fuels and stand density in commercial plantations. As assessment of forest fuel complex variables is a prerequisite for most fire management activities, the study findings have important implications for forest and fuel management and planning in the region.

Fuels reduction can directly improve spotted owl foraging habitat in the Sierra Nevada

Wildfire has been an important force in shaping biological diversity in forests of western North America. Patterns of reburning helped to maintain heterogenous landscapes with low tree densities that naturally limited the extent of severe stand-replacing fires. However, anthropogenic impacts over the past two centuries have dramatically altered fire activity and its ecological function, creating conditions that promote large and intense fires with no historical analog. Fuels reduction treatments represent proven methods to mitigate extreme fire events, but there is controversy over their potential effects to sensitive wildlife species. The California spotted owl (Strix occidentalis occidentalis) is a forest species that evolved under a frequent-fire regime. Controversy over how spotted owls may be impacted by fuels reduction is one of numerous contributing factors that has limited the pace and scale of implementation. To examine the relationships between spotted owls and fuels reduction treatments, we studied breeding season nocturnal foraging habitat selection related to fuel structural metrics of 159 GPS-tagged California spotted owls across the Sierra Nevada bioregion. Spotted owls selected for higher canopy base height, lower ladder fuel density, and lower canopy bulk density, which represent synergies between fuels reduction treatments and owl space use. Spotted owls also selected for higher surface fuels and higher canopy layer counts, representing potential trade-offs between fuels reduction goals and spotted owl space use. We overlaid the probability of space use by foraging spotted owls with relative stand density index (SDI), a measure of forest resilience, to classify Sierra Nevada landscapes into four groups: low management priority (low owl use/high forest resilience), fuels reduction priority (low owl use/low forest resilience), potential conflict zones (high owl use/low forest resilience), and habitat retention priority (high owl use/high forest resilience). Fuels reduction priority (34.4%) was the largest category, suggesting that potential impacts of fuels reduction on spotted owl foraging habitat may be less widespread in the Sierra Nevada than previously believed. Our results suggest that fuels reduction treatments, especially those focused on mutually beneficial goals such as increasing canopy base height, reducing ladder fuels, and reducing canopy bulk density may directly improve spotted owl foraging habitat while also mitigating the effects of large and severe wildfires on forest species and forest ecosystems.

Modeling Juvenile Stand Development and Fire Risk of Post-Fire Planted Forests under Variations in Thinning and Fuel Treatments Using FVS–FFE

In the past, the dry mixed conifer forests of California’s Sierra Nevada mountains experienced frequent low to mixed severity fires. However, due to fire suppression and past management, forest structure has changed, and the new fire regimes are characterized by large, high severity fires which kill a majority of the overstory trees. These new disturbance patterns require novel approaches to regenerate the forest as they are not adapted to large, high severity fires. We forecasted growth and fire behavior of young plantations for 100 years into the future using the Forest Vegetation Simulator (FVS) and its Fire and Fuels Extension (FFE). In these simulations, we tested combinations of different fuel treatments (mastication only, mastication with prescribed burning, and no fuels treatments) with different overstory thinning intensities (residual densities of 370 SDI (stand density index), 495 SDI, 618 SDI (TPH), and no overstory thinning) on stand growth and potential fire behavior using analysis of variance. We compared growth and crowning index at the end of the simulation and the simulation age when the flame length, basal area mortality, and fire type reached low severity between fuel treatment, thinning intensity, and original management of stands (plantation with PCT [precommercial thinning], plantation without PCT, and natural regenerating stands). These comparisons are essential to identify which fuel treatment categories reduce fire risk. We found an overall pattern of decreasing crown fire occurrence and fire induced mortality across all simulations due to increasing canopy base height and decreasing canopy bulk density. In particular, stands with mastication and prescribed burning transitioned from crown fire types to surface fires 10 years earlier compared to mastication only or no fuel treatment. Furthermore, pre-commercially thinned stands transitioned from crown fire states to surface fires 10 years earlier in the simulations compared to un-thinned and naturally regenerating stands. Stands with mastication and burning went below 25% reference threshold of basal area mortality 11 and 17 years earlier before the mastication only and no fuel treatment, respectively. In addition, pre-commercially thinned stands went below 25% basal area mortality 9 and 5 years earlier in the simulation compared to un-thinned or naturally regenerated stands, respectively. Mastication with prescribed burning (MB) was the most effective treatment for quickly reducing fire behavior by consuming surface fuels, thus drastically lowing flame length (e.g., surface flame length of MB was 0.6 m compared to mastication only [1.3 m] and no treatment [1.4 m]). Furthermore, intensive thinning reduced risk of active crown fires spreading through the stand. Prioritizing prescribed burning, when possible, and thinning (both pre-commercially and from below) are the most effective ways to quickly improve fire resistance in mixed conifer plantations. Our results highlight the different stressors that post-fire planted forests experience and how different silvicultural treatments interact over time to reduce fire risk, which demonstrates the importance of treating stands early and the effectiveness of surface fuel treatments.

Canopy and surface fuel estimations using RPAS and ground-based point clouds

Abstract Forest management activities intended to reduce wildfire risk rely on accurate characterizations of the amount and arrangement of canopy and surface fuels. Metrics that describe these fuels are typically estimated with various systems that transform plot-level field data into metrics that can be used within fire behaviour models. Remote sensing data have long been used to estimate these metrics across large spatial scales, but more advanced, high-density point clouds have the potential to estimate these metrics with higher accuracy. This study collected LiDAR and digital aerial photogrammetric (DAP) point clouds from a remotely piloted aerial system (RPAS), as well as mobile laser scanning (MLS) point clouds from a mobile ground-based system, and compared their ability to estimate fuel metrics. This involved the extraction of predictor variables from each point cloud, of which small subsets were used to estimate various fuel metrics. These included six overstory canopy metrics (stand height, canopy cover, tree density, canopy fuel load, canopy bulk density and canopy base height), three diameter at breast height (DBH)–related metrics (stand density index, basal area and quadratic mean diameter) and three surface fuel metrics (total woody debris (TWD), coarse woody debris (CWD) and fine woody debris (FWD)). Overall, canopy metrics were estimated most accurately by the RPAS LiDAR models, although none of the point clouds were able to accurately estimate DBH-related metrics. For the other six canopy metrics, RPAS LiDAR models had an average R2 value of 0.70; DAP – 0.63 and MLS – 0.63. CWD (>7 cm) and TWD loads were estimated most accurately by the MLS models (average R2 values – 0.70), followed by the RPAS LiDAR – 0.38 and DAP – 0.13. None of these models were able to accurately estimate FWD loads (≤7 cm in diameter), with the three types of point clouds having a maximum R2 value of 0.08. Overall, this research shows the relative ability of three types of high-density point clouds to estimate metrics relevant for fire behaviour modeling.

Stocking method effects on forage accumulation, canopy attributes, and nutritive value of grazed Mulato II brachiariagrass pastures

AbstractRotational stocking (RS) is generally associated with the intensification of pasture‐based animal production systems, although many studies have shown little advantage over continuous stocking (CS). The objective of this research was to describe and explain the effects of two average canopy heights (20 and 30 cm) and three stocking methods (CS; rotational stocking with lenient defoliation, RSL; and rotational stocking with moderate defoliation, RSM) on forage accumulation (FA), vertical distribution of plant‐part components, and nutritive value of ‘Mulato II’ hybrid brachiariagrass (Brachiaria spp. syn. Urochloa spp.) during two summer rainy seasons in Piracicaba, São Paulo, Brazil. Pastures were maintained at average canopy height of 20 and 30 cm under CS, and treatments under RSL and RSM were imposed by variations of ±20 and 30% of the average canopy heights, representing defoliation intensities of 33 and 47% of the pre‐graze heights, respectively. The FA was not affected by CS, RSL, RSM stocking methods, averaging 8090 kg DM ha−1 year−1. Canopy bulk density and distribution of plant‐part components in the canopy profile were better at the average canopy height of 20 cm, accompanied by the greater nutritive value. The leaf bulk density was generally greater in RSM compared to CS. Pastures under CS had greater crude protein and in vitro digestible matter, and lesser neutral detergent fibre concentrations. Mulato II brachiariagrass should be managed at an average height of 20 cm under CS.

Desiccation Avoidance and Hummock Formation Traits of rich fen Bryophytes

The aim of this study was to understand the variation in traits relevant for desiccation avoidance among bryophyte species dominant in rich fens and to assess whether these traits explain the formation of a hummock-hollow gradient within peatlands. In samples of 10 species (Aulacomnium palustre, Calliergonella cuspidata, Climacium dendroides, Hamatocaulis vernicosus, Helodium blandowii, Marchantia polymorpha, Plagiomnium ellipticum, Sphagnum teres, S. warnstorfii, Tomentypnum nitens) collected in rich fens of NE Poland, we calculated: canopy bulk density of wet (CDW) and dry (CDD) colonies, maximum water content of bryophyte colonies (WCmax), desiccation rate (K), shoot area index (SAI), canopy dry mass per surface-projected area (CMA), and specific leaf area of a whole living bryophyte part (SLA). The hummock-forming frequency was quantified for each species in the field. Sphagna had the highest WCmax, SAI and CDW, T. nitens and C. dendroides had the lowest WCmax and SLA, P. ellipticum had the highest K, the lowest CMA and CDD. Hummock-forming frequency was positively correlated with CMA and generally negatively related to K, with exception of H. vernicosus showing a high water-retaining ability (low K) despite a hollow or lawn form of growth.

Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management

Key messageWe used clustering to construct fuel classes from fuel inventory data based on three stand attributes relevant to crown fire behaviour: surface fuel load (SFL), canopy base height (CBH) and canopy bulk density (CBD). Resulting fuel classes explained more of the stand-to-stand variability in predicted crown fire behaviour than fuel types of the Canadian Forest Fire Behaviour Prediction (FBP) System.ContextWildfire behaviour is partly determined by stand structure and composition. Fuel characterization is essential for predicting fire behaviour and managing vegetation. Currently, categorical fuel types based on associations with major forested or open vegetated landcovers are used nationally in Canada for fire research and management applications.AimTo provide an alternative description of selected forest fuels in Alberta, Canada, using direct classification in which fuel categories are constructed from data using analytical methods.MethodsFuel inventory data for 476 stands were used to construct fuel classes with clustering. Potential crown fire behaviour was modelled for resulting fuel class clusters (FCCs) and FCCs were compared with assigned FBP System fuel types. Tree-based modelling was used to identify stand characteristics most influential on FCC membership. Fuel treatment effects on FCC and modelled crown fire behaviour were explored for the FCC most susceptible to crown fire.ResultsFour FCCs were identified: Red (low SFL, low CBH, low CBD); Green (high SFL, low-moderate CBH, low CBD); Blue (low SFL, high CBH, low-moderate CBD); and Black (low SFL, moderate CBH, high CBD). Stand density of live conifers and FBP System fuel type were the most important variables influencing FCC membership; however, FCCs did not align directly with assigned FBP System fuel types. Fuel reduction treatments in the Black FCC were effective at shifting the stand to a less flammable FCC.ConclusionFCCs explained more of the stand-to-stand variability in predicted crown fire behaviour than assigned FBP System fuel types, which suggests FCCs could be used to improve fire behaviour predictions and aid fire managers in prioritizing areas for fuel treatments. Future technological and remote sensing advances could enable mapping FCCs across large regions.

Canopy characterization and nutritive value of stockpiled ‘Floralta’ limpograss

AbstractLimpograss [Hemarthria altissima (Poir.) Stapf and C. E. Hubb.] has been gaining ground in beef cattle production as a stockpiling option. This study evaluated canopy characteristics of stockpiled pastures of limpograss cultivar Floralta over two cool seasons. Treatments included canopy layers (upper third, middle, lower third) and plant fractions (leaf and stem) arranged in a randomized split block design. There were 12 paddocks (blocks) with 1.2 ha each. Response variables included forage mass, canopy height and density, proportion of leaf and stem, and leaf and stem nutritive value within each layer. Forage mass fluctuated throughout the season, varying from 4,410 kg dry matter (DM) ha–1 in December to 7,220 kg DM ha–1 in October. Canopy bulk density was greater in February compared with November and December (290 vs. 145 kg ha–1 cm–1), and similar to October and January (200 kg ha–1 cm–1, on average). The lower canopy layer had the greatest proportion of stem whereas the upper layer had greatest proportion of leaves. Leaf had greater in vitro digestible organic matter (IVDOM) (700 g kg–1) compared with stem (600 g kg–1). Leaf/stem ratio was greater in the upper layer (1.12), but similar between the middle and lower layers (0.4, on average). Leaf crude protein (CP) was greater than stem CP 70 vs. 20 g kg–1), with no differences between stem CP across evaluation dates. Stockpiled limpograss maintained leaf and stem digestibility and can be used as an alternative to reduce feeding costs in North Florida during the cool season.

Exploring golden eagle habitat preference using lidar-based canopy bulk density

ABSTRACT A lidar-derived canopy height profile (CHP), generated from canopy bulk density (CBD) estimates for a sequence of 1-m increments through the canopy, provides a physical measure of forest structure. Measurement of physical properties are intuitively understandable and thus facilitate the use of lidar for investigating hypotheses regarding avian resource use. We illustrate the use of lidar-derived physical measures to explore the hypothesis that golden eagles (Aquila chrysaetos) prefer an open understory, which potentially aids visual identification of prey. Two golden eagles fitted with GPS tracking devices overwintered in the New Jersey Pinelands National Preserve. We generated CHPs from discrete-return lidar data for an area occupied by the birds. We compared the CHPs of sites the birds occupied to the surrounding available habitat and found that the occupied sites were significantly lower in CBD from the ground up to 5 m for perched/stationary birds, and from the ground up to 8 m for birds in flight. These results could be used by forest resource managers for promoting golden eagle habitat through prescribed fire. In addition, these results demonstrate the power of lidar to generate physically and intuitively meaningful measures of forest structure.

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

BackgroundNational estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level.ResultsSAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates.ConclusionsThe combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Tree Canopy Management Affects Dynamics of Herbaceous Vegetation and Soil Moisture in Silvopasture Systems Using Arboreal Legumes

Understanding ecological interactions between the arboreal and the herbaceous components is key to get the full benefits from silvopastoral systems. The objective of this 2-yr research was to evaluate productivity and nutritive value of signalgrass (Urochloa decumbens (Stapf.) R. Webster) subjected to shading from the tree legumes Gliricidia (Gliricidia sepium (Jacq.) Steud) or Mimosa (Mimosa caesalpiniifolia Benth.) under different tree canopy management. Trees were planted in double rows and were either unharvested or harvested only one row, leaving the other row unharvested. Response variables for the herbaceous vegetation included canopy height, herbage mass (green leaf blade, green stem, senescent leaves, and senescent stem), herbage accumulation rate, canopy bulk density, and soil moisture. Total herbage mass, green herbage mass, and green leaf mass were affected by treatment × month and harvest management × month interactions. Herbage accumulation rate in Gliricidia was greater (55 kg DM ha−1d−1) than Mimosa (32 kg DM ha−1d−1). Soil moisture was lesser at the Mimosa sites (16.2%) compared with the Gliricidia ones (17.2%), and it was greater between tree rows (21.9%) compared with full sun (11.5%), varying across the season. Harvesting management had a short-term transient effect on herbage responses. Tree canopy management can affect forage quantity and quality; however, these effects are transient and vary with tree spacing. Signalgrass grew faster and had better nutritive value when growing with Gliricidia.

Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Predicting black spruce fuel characteristics with Airborne Laser Scanning (ALS)

Wildfire decision support systems combine fuel maps with other fire environment variables to predict fire behaviour and guide management actions. Until recently, financial and technological constraints have limited provincial fuel maps to relatively coarse spatial resolutions. Airborne Laser Scanning (ALS), a remote sensing technology that uses LiDAR (Light Detection and Ranging), is becoming an increasingly affordable and pragmatic tool for mapping fuels across localised and broad areas. Few studies have used ALS in boreal forest regions to describe structural attributes such as fuel load at a fine resolution (i.e. <100 m2 cell resolution). We used ALS to predict five forest attributes relevant to fire behaviour in black spruce (Picea mariana) stands in Alberta, Canada: canopy bulk density, canopy fuel load, stem density, canopy height and canopy base height. Least absolute shrinkage and selection operator (lasso) regression models indicated statistically significant relationships between ALS data and the forest metrics of interest (R2 ≥0.81 for all metrics except canopy base height which had a R2 value of 0.63). Performance of the regression models was acceptable and consistent with prior studies when applied to test datasets; however, regression models presented in this study mapped stand attributes at a much finer resolution (40 m2).

Airborne lidar provides reliable estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests

Accurate estimates of canopy base height (CBH) and canopy bulk density (CBD) are critical inputs for many fire modeling and simulation programs. Commonly used LANDFIRE estimates of CBH and CBD are only available at 30 m resolution and have relatively low accuracy, and traditional field-based estimates can lead to inaccurate spatial estimates by assuming averages across spatial extents. Discrete-return airborne light detection and ranging (lidar) has become increasingly common in forestry applications, with demonstrated success in measuring canopy metrics and forest structure across broad landscapes and varied forest types. However, few studies have investigated the potential of airborne lidar for estimating these metrics in southwestern forests. This study developed an approach for estimating CBH and CBD using airborne lidar data and evaluated the accuracy of estimates at 20 m resolution. We also evaluated the predicted accuracy between managed and unmanaged forest stands. We employed a quantile-based method to derive CBH and utilized the Fire and Fuels Extension of the Forest Vegetation Simulator to estimate CBD from a lidar-derived tree list. Our results indicate that airborne lidar produced more accurate estimates of both CBH (R2 = 0.4; RMSE = 1.25 m) and CBD (R2 = 0.76; RMSE = 0.021 kg−3) as compared to LANDFIRE and other regional remote-sensing based estimates. We also report that airborne lidar is more accurate at estimating CBH in unmanaged stands versus managed stands, but CBD estimates maintain similar accuracy regardless of management history. Compared to methods developed in other regions, our approach resulted in relatively low R2 values, but our RMSE values were found to be similar or slightly improved. Our approach for deriving both metrics are conceptually and computationally simple making them especially valuable for use by land managers and ecologists alike. This study provides clear evidence that airborne lidar can be used to derive broad-scale, fine-resolution fuel data layers, which are commonly required in fire modeling and forest management activities and decision-making.

Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy

Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ−1). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ−1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.

Fuel Treatment Longevity in Ponderosa Pine-Dominated Forest 24 Years After Cutting and Prescribed Burning

Fuels reduction treatments to mitigate fire behavior are common in ponderosa pine ecosystems of the western United States. While initial impacts of fuel treatments have been reported, less is known about treatment longevity as live and dead fuels change with time. We analyzed fuel dynamics in ponderosa pine–Douglas-fir forests 21-23 years following experimental fuel reduction designed as two independent studies of cutting combined with burning: one tested a commercial thinning strategy, while a second tested a retention shelterwood strategy to reduce fuels while also restoring ponderosa pine forests. Treated units were harvested in 1992 and half of the units were prescribed burned one-to-two years later. After 22 to 23 years post-treatment, few differences in fuel load persist and all treatments have increased ladder fuels in the form of live saplings and seedlings. Canopy fuel loads were lower in treated units compared to untreated control units; however, no other canopy fuel metric differed between treatments. The only persistent difference in surface fuels was in the retention shelterwood, where 1-hour fuels were lower in the treated units compared to control units. Crown fire hazard varied greatly, but means were similar between treatments. The increased hazard was driven by increases in live surface fuels from seedlings and saplings in the retention shelterwood, which increased canopy bulk density and reduced canopy base height. The overstory was still dominated by ponderosa pine 21-23 years later for all treatments, but the smaller size classes were primarily Douglas-fir, suggesting that without future disturbance, dominance will shift from pine to Douglas-fir dominated forests. The exception to this was the cut+fall burn treatment in the commercial thinning, where ponderosa pine outnumbered Douglas-fir trees across all size classes. The treatments that included a broadcast prescribed burn killed many existing seedlings and saplings. Our findings support other studies showing fuel reduction and restoration treatments are most successful with a combination of cutting and burning strategies, but also show that fuel treatments in low-elevation dry forests will likely not remain effective for much longer than historical mean fire return intervals.

Evaluating the 3-m tree crown spacing guideline for the prevention of crowning wildfires in lodgepole pine forests, Alberta

A 3-m between crown spacing is a commonly cited criterion found in the wildland-urban interface fire literature for minimizing the likelihood of a fully-developed crown fire from occurring in a conifer forest on level terrain. The validity of this general recommendation is examined here in light of our current state-of-knowledge regarding crown fire propagation in relation to canopy bulk density. Given the characteristics of the overstory structure for 20 lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stands located in Alberta, as sourced from the literature, the canopy fuel properties following a virtual thinning to a 3-m crown spacing and then to a targeted canopy bulk density of 0.05 kg/m3 were computed. On the basis of these computations, crown fire potential was then analyzed and interpreted. The conclusion reached is that, in the majority of cases, a less widely spaced stand would be adequate for preventing crown fire development in lodgepole pine forests.