Examination of imputation methods to estimate status and change of forest attributes from paneled inventory data

The Forest Inventory and Analysis (FIA) program conducts an annual inventory throughout the United States. In the western United States, 10% of all plots (one panel) are measured annually, and a moving average is used for estimating current condition and change of forest attributes while alternative methods are sought in all regions of the United States. This dissertation explored alternatives to the moving average in the Pacific Northwest using Current Vegetation Survey data collected in Oregon and Washington. Several nearest neighbor imputation methods were examined for their suitability to update plot-level forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha) to the current point in time. The results were compared to estimates obtained using a moving average and a weighted moving average. In terms of bias and accuracy, the weighted moving average performed better than the moving average. When the most recent measurements of the variables of interest were used as ancillary data, randomForest imputation outperformed both the moving average and the weighted moving average. For estimating current basal area/ha, stems/ha, volume/ha, and biomass/ha, tree-level imputation outperformed plot-level imputation. The difference in bias and accuracy between tree- and plot-level imputation was more pronounced when the variables of interest were summarized by species groups. Nearest neighbor imputation methods were also investigated for estimating mean annual change in selected forest attributes. The imputed mean annual change was used to update unmeasured panels to the current point in time. In terms of bias and accuracy, the resulting estimates of current basal area/ha, stems/ha, volume/ha, and biomass/ha outperformed the results obtained using plot-level imputation. Information on hard to estimate forest attributes such as cavity tree and snag abundance are important for wildlife management plans. Using FIA data collected in Washington, Oregon, and California, nearest neighbor imputation approaches and negative binomial regression models were examined for their suitability in estimating cavity tree and snag abundance. The negative binomial models were preferred to the nearest neighbor imputation approaches

Modeling Relative Humidity in Headwater Forests Using Correlation with Air Temperature

Microclimate variables such as air temperature and relative humidity influence habitat conditions and ecological processes in riparian forests. The increased relative humidity levels within riparian areas are essential for many plant and wildlife species. Information about relative humidity patterns within riparian areas and adjacent uplands are necessary for the prescription of effective buffer widths. Relative humidity monitoring is more expensive than temperature monitoring due to greater sensor costs, and it is primarily conducted for research purposes. To make relative humidity monitoring in riparian areas more cost effective, we explored modeling relative humidity as a function of air temperature and other covariates using linear fixed and linear mixed effects models applied to two case studies. Localizing predictions for stream reaches using a linear mixed effects model or a linear fixed effects model with correction factor improved model predictions, especially when large variability among stream reaches was present. A minimum of three to five relative humidity measurements per stream reach seem sufficient to estimate the random stream reach effect or correction factor for the linear mixed and linear fixed effects models, respectively. Including covariates that describe distance to stream and canopy cover in addition to air temperature improved model performance. Although further model refinement is probably needed to allow detection of small changes in relative humidity associated with changes in stand structure from partial overstory removal, the models developed provide a means towards decreasing the costs of monitoring microclimates of importance to riparian area function.Keywords: Localized prediction, Linear mixed effects model, Pacific Northwest, Subsampling, Riparian microclimateKeywords: Localized prediction, Linear mixed effects model, Pacific Northwest, Subsampling, Riparian microclimat

Teaching in Contemporary Forest Resources Curricula: Applications to Courses in Forest Measurements and Biometrics

Foresters face new and evolving challenges as society reconsiders the balance of its interests between wood production and the provision of ecosystem services in the management of forests. Whatever paths this process may take, sound and broad-based decisions will continue to require accurate and relevant measurements of current forest conditions and projections of future conditions under alternative management programs. Forest measurements and biometrics (FMB) will remain a key component of future forest management and a critical element in the education of future forest managers. As professors who both teach and do research in FMB, we offer teaching goals that we believe will improve FMB education in forestry schools to meet future needs. In the following sections, we outline teaching goals for university-level instruction in forest resources curricula and the roles of FMB in modern forestry. We then identify what we feel are the most critical challenges in teaching and learning FMB and discuss selected strategies to meet teaching objectives for FMB. A fourth section presents an overview of how selected strategies can be integrated into FMB classes, including examples and comments on the role that new technology might play in meeting the above-described challenges. The final section summarizes our main points and provides concluding remarks.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the Society of American Foresters and can be found at: http://www.safnet.org/publications/jof/index.cfm.Keywords: forest inventory and monitoring, teaching applied statistics, Pacific Northwest, forest analysis, forest pedagog

The changing culture of silviculture

Changing climates are altering the structural and functional components of forest ecosystems at an unprecedented rate. Simultaneously, we are seeing a diversification of public expectations on the broader sustainable use of forest resources beyond timber production. As a result, the science and art of silviculture needs to adapt to these changing realities. In this piece, we argue that silviculturists are gradually shifting from the application of empirically derived silvicultural scenarios to new sets of approaches, methods and practices, a process that calls for broadening our conception of silviculture as a scientific discipline. We propose a holistic view of silviculture revolving around three key themes: observe, anticipate and adapt. In observe, we present how recent advances in remote sensing now enable silviculturists to observe forest structural, compositional and functional attributes in near-real-time, which in turn facilitates the deployment of efficient, targeted silvicultural measures in practice that are adapted to rapidly changing constraints. In anticipate, we highlight the importance of developing state-of-the-art models designed to take into account the effects of changing environmental conditions on forest growth and dynamics. In adapt, we discuss the need to provide spatially explicit guidance for the implementation of adaptive silvicultural actions that are efficient, cost-effective and socially acceptable. We conclude by presenting key steps towards the development of new tools and practical knowledge that will ensure meeting societal demands in rapidly changing environmental conditions. We classify these actions into three main categories: reexamining existing silvicultural trials to identify key stand attributes associated with the resistance and resilience of forests to multiple stressors, developing technological workflows and infrastructures to allow for continuous forest inventory updating frameworks, and implementing bold, innovative silvicultural trials in consultation with the relevant communities where a range of adaptive silvicultural strategies are tested. In this holistic perspective, silviculture can be defined as the science of observing forest condition and anticipating its development to apply tending and regeneration treatments adapted to a multiplicity of desired outcomes in rapidly changing realities

The changing culture of silviculture

Changing climates are altering the structural and functional components of forest ecosystems at an unprecedented rate. Simultaneously, we are seeing a diversification of public expectations on the broader sustainable use of forest resources beyond timber production. As a result, the science and art of silviculture needs to adapt to these changing realities. In this piece, we argue that silviculturists are gradually shifting from the application of empirically derived silvicultural scenarios to new sets of approaches, methods and practices, a process that calls for broadening our conception of silviculture as a scientific discipline. We propose a holistic view of silviculture revolving around three key themes: observe, anticipate and adapt. In observe, we present how recent advances in remote sensing now enable silviculturists to observe forest structural, compositional and functional attributes in near-real-time, which in turn facilitates the deployment of efficient, targeted silvicultural measures in practice that are adapted to rapidly changing constraints. In anticipate, we highlight the importance of developing state-of-the-art models designed to take into account the effects of changing environmental conditions on forest growth and dynamics. In adapt, we discuss the need to provide spatially explicit guidance for the implementation of adaptive silvicultural actions that are efficient, cost-effective and socially acceptable. We conclude by presenting key steps towards the development of new tools and practical knowledge that will ensure meeting societal demands in rapidly changing environmental conditions. We classify these actions into three main categories: re-examining existing silvicultural trials to identify key stand attributes associated with the resistance and resilience of forests to multiple stressors, developing technological workflows and infrastructures to allow for continuous forest inventory updating frameworks, and implementing bold, innovative silvicultural trials in consultation with the relevant communities where a range of adaptive silvicultural strategies are tested. In this holistic perspective, silviculture can be defined as the science of observing forest condition and anticipating its development to apply tending and regeneration treatments adapted to a multiplicity of desired outcomes in rapidly changing realities

Trade-offs across densities and mixture proportions in lodgepole pine-hybrid spruce plantations

Monocultures tend to yield higher total stand volumes and are simple to manage. Yet, mixed species stands may result in similar stand volumes while providing benefits such as mitigating damage from insects and disease. To understand the effects of stand density and species mixture and their interactions on stand yield, tree size and morphology, and damage in monocultures and mixtures, we analyzed a 25-year-old experiment in interior British Columbia, Canada. The lodgepole pine (Pl)-interior hybrid spruce (Sx) experiment included three densities?1000, 1500, and 2000 stems per hectare (SPH)?and five species mixtures?1:0, 3:1, 1:1, 1:3, and 0:1 Pl:Sx. Results 25 years after stand establishment showed that stand volume was significantly larger with an increasing proportion of Pl across all stand densities. Pl had 10% larger diameters in the 1000 SPH than in the 2000 SPH and when mixed with Sx (1:1). Pl had larger crowns in mixtures regardless of density. Mixture proportion did not affect gall rust incidence or stem form in Pl, but reduced attack in Sx by spruce weevil. Our findings suggest that mixing Pl-Sx and high planting density decrease weevil attacks in Sx, which reduce loss in timber quality. Yet, Pl quality may decrease when mixed with Sx, due to larger Pl crowns. These results may be used to improve the implementation of management strategies that decrease trade-offs between yields, desired market tree sizes, and timber loss from pest and pathogens, while making the stands more resilient to further climate change impact

Tree Height Increment Models for National Forest Inventory Data in the Pacific Northwest, USA

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.Forestry, Faculty ofNon UBCForest Resources Management, Department ofReviewedFacult

Comparison of stratified and non-stratified most similar neighbour imputation for estimating stand tables

Many growth and yield simulators require a stand table or tree-list to set the initial condition for projections in time. Most similar neighbour (MSN) approaches can be used for estimating stand tables from information commonly available on forest cover maps (e.g. height, volume, per cent canopy cover and species composition). Simulations were used to compare MSN (using an entire database) with two stratified MSN approaches. The first stratified MSN approach used species composition to partition the population into two inventory type strata, while the second stratified MSN approach used average stand age to partition the data into two stand development stages (strata). The MSN approach was used within the whole population and within each stratum to select a reference stand and to impute the ground variables of the reference stand to each target stand. Observed vs estimated stand tables were then compared for the stratified and non-stratified simulations. The imputation within a stratum did not result in better estimates than using the MSN approach within the whole population. Possible reasons for poor performance of stratified MSN are provided

The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators