In interior Alaska’s 115 million acres of boreal forest, white and black spruce are the dominant tree species. Climate models suggest that the region is becoming warmer and drier, resulting in declining growth of black and white spruce, according to some researchers. These drier conditions also may lead to greater risk of stand-replacing wildfires, resulting in forests dominated by birch and aspen, which are early-successional tree species.
To compare long-term growth trends of the dominant coniferous and deciduous tree species, a team of researchers with the USDA Forest Service Pacific Northwest Research Station and the University of Alaska Anchorage analyzed tree cores collected from the Tanana Valley and measured tree-ring widths of these four tree species over the past 150 years. They also compared growth against monthly temperature and precipitation data to determine if there is a correlation between climate and growth.
The team found that white and black spruce have not experienced as rapid a growth decline as earlier studies suggested; instead, their annual growth remains near the long-term mean. Of the four species examined, aspen showed the greatest recent growth decline, likely reflecting a widespread insect outbreak. Among the climate variables that will affect the future growth of these species, summer rainfall was identified as a significant factor.
The United States national inventory program measures a subset of tree heights in each plot in the Pacific Northwest. Unmeasured tree heights are predicted by adding the difference between modeled tree heights at two measurements to the height observed at the first measurement. This study compared different approaches for directly modeling 10-year height increment of red alder (RA) and ponderosa pine (PP) in Washington and Oregon using national inventory data from 2001–2015. In addition to the current approach, five models were implemented: nonlinear exponential, log-transformed linear, gamma, quasi-Poisson, and zero-inflated Poisson models using both tree-level (e.g., height, diameter at breast height, and compacted crown ratio) and plot-level (e.g., basal area, elevation, and slope) measurements as predictor variables. To account for negative height increment observations in the modeling process, a constant was added to shift all response values to greater than zero (log-transformed linear and gamma models), the negative increment was set to zero (quasi-Poisson and zero-inflated Poisson models), or a nonlinear model, which allows negative observations, was used. Random plot effects were included to account for the hierarchical data structure of the inventory data. Predictive model performance was examined through cross-validation. Among the implemented models, the gamma model performed best for both species, showing the smallest root mean square error (RSME) of 2.61 and 1.33 m for RA and PP, respectively (current method: RA—3.33 m, PP—1.40 m). Among the models that did not add the constant to the response, the quasi-Poisson model exhibited the smallest RMSE of 2.74 and 1.38 m for RA and PP, respectively. Our study showed that the prediction of tree height increment in Oregon and Washington can be improved by accounting for the negative and zero height increment values that are present in inventory data, and by including random plot effects in the models.
There is growing interest in using Digital Aerial Photogrammetry (DAP) for forestry applications. However, the performance of pushbroom DAP relative to frame-based DAP and airborne lidar is not well documented. Interest in DAP stems largely from its low cost relative to lidar. Studies have demonstrated that frame-based DAP generally performs slightly poorer than lidar, but still provides good value due to its reduced cost. In the USA pushbroom imagery can be dramatically less expensive than frame-camera imagery in part because of a nationwide collection program. There is an immediate need then to understand how well pushbroom DAP works as an auxiliary data source in the prediction of key forest attributes including basal area, volume, height, and the number of trees per ha. This study compares point clouds generated from 40 cm pushbroom DAP with point clouds from lidar and 7.5 cm, 15 cm, and 30 cm frame-based DAP. Differences in point clouds from these data sources are readily apparent in visual inspections; e.g. DAP tends to measure canopy gaps poorly, omit individual trees in openings, is typically unable to represent the ground beneath canopy, and is susceptible to commission errors manifested as points above the canopy surface. Frame-based DAP provides greater canopy detail than pushbroom DAP, which becomes more apparent with higher image resolution. Our results indicated that DAP height metrics generally have a strong linear relationship with lidar metrics, with R2 values ranging from 83 – 90% for cover, and 47–80% for height quantiles. Similarly, lidar auxiliary variables explain the greatest variation in forest attributes, e.g., volume (84%), followed closely by 30 cm frame-based DAP (81%), with the poorest results from pushbroom DAP (75%). While DAP resolution had a visible effect on canopy definition, it did not appreciably affect point cloud metrics or model performances. Although pushbroom DAP explained the least variation in forest attributes, it still had sufficient explanatory power to provide good value when frame-based DAP and lidar are not available.
Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
Forest land managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality. A key challenge in improving the accuracy of these predictions is selecting appropriate wind and fuel moisture inputs. Our objective was to evaluate post-fire mortality predictions using the Forest Vegetation Simulator Fire and Fuels Extension (FVS-FFE) to determine if using representative fire-weather data would improve prediction accuracy over two default weather scenarios. We used pre- and post-fire measurements from 342 stands on forest inventory plots, representing a wide range of vegetation types affected by wildfire in California, Oregon, and Washington. Our representative weather scenarios were created by using data from local weather stations for the time each stand was believed to have burned. The accuracy of predicted mortality (percent basal area) with different weather scenarios was evaluated for all stands, by forest type group, and by major tree species using mean error, mean absolute error (MAE), and root mean square error (RMSE). One of the representative weather scenarios, MeanWind, had the lowest mean error (4%) in predicted mortality, but performed poorly in some forest types, which contributed to a relatively high RMSE of 48% across all stands. Driven in large part by over-prediction of modelled flame length on steeper slopes, the greatest over-prediction mortality errors arose in the scenarios with higher winds and lower fuel moisture. Our results also indicated that fuel moisture was a stronger influence on post-fire mortality than wind speed. Our results suggest that using representative weather can improve accuracy of mortality predictions when attempting to model over a wide range of forest types. Focusing simulations exclusively on extreme conditions, especially with regard to wind speed, may lead to over-prediction of tree mortality from fire.
Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m X 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha−1 (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha−1 year−1. The best model for AWC based solely on TS and other mapped variables had an R2 = 0.52 (RMSE = 54.6 Mg C ha−1); applying this model at two time periods to estimate net change in AWC resulted in an R2 = 0.25 (RMSE = 28.3 Mg ha−1) and a mean error of −5.4 Mg ha−1. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R2 = 0.95 (RSME = 17.0 Mg ha−1). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R2 = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments.
A Bayesian probabilistic modeling platform was used and evaluated for application in a relatively complex individual-tree growth and yield model for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco), which was expressed as a mixed discrete and continuous Bayesian Network for annual projections. The modeling platform used a common and open-source Bayesian analysis program (JAGS v3.3.0), and was sufficiently flexible to handle a relatively complex model structure; namely, a differential form, highly dynamic, recursive, hierarchical, non-linear system of equations with rather complex error structures. This novel probabilistic modeling platform met certain desirable criteria, including: (1) accurate and tractable projections that included full error propagation; (2) flexible and comprehensive analytic capabilities; (3) full consideration of hierarchical and multi-level model structures; (4) capacity for random effects calibration; (5) allowance of hypothesis testing and updating knowledge across different system components, simultaneously with varying sources of information (i.e., new data); (6) computational efficiency; and (7) relatively simple implementation as demonstrated in a compiled scripting language. Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g., estimated parameters, state variables, random effects, and residual errors). Cumulative error projections over a 40-year period for three sample Douglas-fir stands were determined. Projection errors for key metrics summed across all trees, such as total basal area and stem density, had coefficient of variations between 4-6% and 7-8%, respectively. Probabilistic projections were markedly different from deterministic projections made with the same model structure. Overall, this novel probabilistic platform showed strong promise as a general platform for ecological modeling, particularly when tractable and analytically correct error projections are required. In particular, the Bayesian probabilistic modeling approach used provided a natural platform for cross-disciplinary research, particularly between social and ecological research domains.
Data describing aircraft position and attitude are essential to computing return positions from ranging data collected during airborne laser scanning (ALS) campaigns. However, these data are often excluded from the products delivered to the client and their recovery after the contract is complete can require negotiations with the data provider, may involve additional costs, or even be infeasible. This paper presents a rigorous, fully automated, novel method for recovering aircraft positions using only the point cloud. The study used ALS data from five acquisitions in the US Pacific Northwest region states of Oregon and Washington and validated derived aircraft positions using the smoothed best estimate of trajectory (SBET) provided for the acquisitions. The computational requirements of the method are reduced and precision is improved by relying on subsets of multiple-return pulses, common in forested areas, with widely separated first and last returns positioned at opposite sides of the aircraft to calculate their intersection, or closest point of approach. To provide a continuous trajectory, a cubic spline is fit to the intersection points. While it varies by acquisition and parameter settings, the error in the computed aircraft position seldom exceeded a few meters. This level of error is acceptable for most applications. To facilitate use and encourage modifications to the algorithm, the authors provide a code that can be applied to data from most ALS acquisitions.
Evidence of shifting dominance among major forest disturbance agent classes regionally to globally has been emerging in the literature. For example, climate-related stress and secondary stressors on forests (e.g., insect and disease, fire) have dramatically increased since the turn of the century globally, while harvest rates in the western US and elsewhere have declined. For shifts to be quantified, accurate historical forest disturbance estimates are required as a baseline for examining current trends. We report annual disturbance rates (with uncertainties) in the aggregate and by major change causal agent class for the conterminous US and five geographic subregions between 1985 and 2012. Results are based on human interpretations of Landsat time series from a probability sample of 7200 plots (30 m) distributed throughout the study area. Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data. National rates of disturbance varied between 1.5% and 4.5% of forest area per year, with trends being strongly affected by shifting dominance among specific disturbance agent influences at the regional scale. Throughout the time series, national harvest disturbance rates varied between one and two percent, and were largely a function of harvest in the more heavily forested regions of the US (Mountain West, Northeast, and Southeast). During the first part of the time series, national disturbance rates largely reflected trends in harvest disturbance. Beginning in the mid-90s, forest decline-related disturbances associated with diminishing forest health (e.g., physiological stress leading to tree canopy cover loss, increases in tree mortality above background levels), especially in the Mountain West and Lowland West regions of the US, increased dramatically. Consequently, national disturbance rates greatly increased by 2000, and remained high for much of the decade. Decline-related disturbance rates reached as high as 8% per year in the western regions during the early-2000s. Although low compared to harvest and decline, fire disturbance rates also increased in the early- to mid-2000s. We segmented annual decline-related disturbance rates to distinguish between newly impacted areas and areas undergoing gradual but consistent decline over multiple years. We also translated Landsat reflectance change into tree canopy cover change information for greater relevance to ecosystem modelers and forest managers, who can derive better understanding of forest-climate interactions and better adapt management strategies to changing climate regimes. Similar studies could be carried out for other countries where there are sufficient Landsat data and historic temporal snapshots of high-resolution imagery.