Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - May 2019)
Acquiring forest resources information for tropical developing countries is challenging due to financial and logistical constraints, yet this information is critical for enhancing management capability and engaging in initiatives such as Reducing Emissions from Deforestation and forest Degradation (REDD+). In this dissertation, I investigated innovative approaches to monitoring forest resources and deepened understanding of multi-source information (i.e., remote-sensing, environmental, and disturbance data) needs by examining methods using a model-based framework for assessment of a jurisdictional landscape in the miombo ecoregion of Zambia. I focused on percent canopy cover (CC) and above-ground biomass (AGB) within a National Forest Inventory (NFI) context because both are important for land management and REDD+. First, I compared multi-source information and four modeling methods to estimate CC and total forest area. Landsat outperformed RapidEye and a generalized additive model was most precise. Available soil water content (AWC), slope, distance to district capital, and texture of remotely sensed data were crucial predictor variables in improving estimates. Second, I used multi-source information and compared methods with and without predicted CC in three modeling methods to estimate total AGB. A nonlinear sigmoidal model was most precise when using predicted CC, AWC, pH, occurrence of late season fire, and the Normalized Difference Moisture Index as predictor variables. Third, I developed an innovative monitoring framework using time series classification and a stock-difference approach to estimate change in land cover and AGB over a 16-year annual time series of Landsat data. Forest/nonforest change trajectories were used to develop change classes relevant to underlying biotic and abiotic factors and provided ecologically meaningful context for land cover change and AGB change. Overall, predictor variables related to soil moisture, topography, shortwave infrared bands and texture of vegetation indices from remotely sensed data are vital to accurate models of CC and AGB. Genetic algorithms provide an opportune method for predictor variable selection across a diversity of modeling methods. Further, robust change estimates are feasible when using annual monitoring methods based on freely available, multi-source information. In conclusion, the model-based framework provides a precise, statistically sound, approach to estimating and mapping forest attributes within an NFI context.
Local and regional timber shortages may be ameliorated via planting improved stocks with higher yields. In this dissertation, I addressed an important knowledge gap on the impacts of tree improvement programs on yields of white spruce (Picea glauca (Moench) Voss) and hybrid spruce (Picea engelmannii Parry ex Engelmann x Picea glauca (Moench) Voss) plantations across the boreal and hemiboreal forests of Canada using meta-modelling approaches. In particular, I used meta-data for white and hybrid spruce provenance trials extracted from the literature to: (i) forecast provenance yields over time for broad spatial and temporal extents; (ii) model yields of provenances relative to standard stocks (termed “gain”) over time; and (iii) test alternatives for forecasting each provenance at a location using available repeated-measures data. In the first study, provenance height over time trajectories were modelled by incorporating the effects of climatic variables, provenances and site characteristics into mixed-effects nonlinear models via a random coefficients modelling approach. Height trajectories were strongly affected by planting site and provenance climates, along with planting site characteristics. The height trajectory meta-model was incorporated into an existing growth and yield model, which can be used to predict provenance yields for long temporal and large spatial extents. In the second study, the impacts of the particular gain definition (i.e., selection age, proportion of top performers) were examined using the model from the first study, and one definition was selected. A meta-model of gain as a function of plantation age, planting density, and planting site climate was developed. Planting site climate strongly affected these gain trajectories. The gain definition and trajectory model can be used to evaluate potential gains of using improved white and hybrid spruce stocks. Forecasts are needed to evaluate provenance (or progeny) performance at harvest, often 80 or more years from planting. In the third study, three alternative procedures (population-averaged, subject-specific, and autocorrelation) to forecast repeated measures for a particular progeny at a location were compared and evaluated by virtually removing some repeated measures. The subject-specific forecasts were best with accuracies similar to the measurement precision using standard height measurement devices given five or more prior measurements.
In this dissertation, a consistent, reasonably precise, verifiable system of stand structure classification was developed and demonstrated. The goal was to provide a foundation for better communication amongst forest management professionals. A novel distance metric and classification algorithm were introduced. The distance metric was based on similarity in reversed cumulative stems and basal area per ha by diameter (DBH; 1.3 m above ground). This distance metric: (1) uses commonly available information; (2) avoids the separation of data into arbitrary DBH classes; and (3) represents a broad range of simple to complex stand structures. Using 421 plots established across a range of Interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) and lodgepole pine (Pinus contorta var. latifolia (Engelm.) Critchfield) stands in the Cariboo region of British Columbia, Canada, a 17-class system of classification was constructed. Whole stand statistics, cumulative distributions, and stand structure/distribution indices were used to evaluate the results. The classes were reasonably precise, with meaningful partitions separating single layered versus complex stands. The utility of the classification system was investigated for diagnosing potential patterns of succession. Over 100 simulated stand structure progressions were simulated using plot data input into an individual-tree growth model. Similar progressions in stand structure classes were assigned common pathways. Four general patterns of succession were observed: (1) a high density single layered pathway; (2) a moderate density single layered pathway; (3) a moderate density complex pathway; and (4) a moderate density, mixed complex-single layered pathway. Lastly, the feasibility of using aerial Light Detection and Ranging (LiDAR) for stand structure classification in forest inventory was assessed. LiDAR was reasonably effective in distinguishing structural classes on the basis of cumulative distributions in basal area or gross volume with respect to DBH, but it was less successful when the distributions in numbers of stems per ha were included. Further study using additional LiDAR metrics beyond those used in this study are needed to improve the use of LiDAR for stand structure classification. This stand structure classification system has potential for a wide variety of forest management applications, including improvement of linkages between strategic and tactical planning and implementation.
The successional processes of the mixed-species Pacific coastal temperate rain forests of British Columbia (BC), Canada, are defined by gap dynamics, where small-scale disturbances, mainly due to windthrow, create openings in the canopy necessary for regeneration. Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) is the dominant, pioneer species in this area and western hemlock (Tsuga heterophylla (Raf.) Sarg.) and western redcedar (Thuja plicata Donn) are the late-successional, shade-tolerant species. Silvicultural systems such as variable retention systems have been applied to many of the secondary growth mixed-conifer forests. Variable retention in this area is designed to differ dramatically from stand to stand. This approach differs from the traditional even-aged management applied to forests of the Pacific Northwest coast. In this study, a model-based approach was used to investigate how multiple treatment interventions as a part of active management across a landscape affect mortality and growth within actively managed stands. There is a need for this information as current growth and yield models used in this area are limited by either the number of species which can co-exist in a stand (e.g., the model TASS of BC) or are limited by the need for data not commonly obtained in inventory databases (e.g., the models FVS and ORGANON of USA). Additionally, no growth and yield models have been developed to include variable retention systems, where a variety of thinning intensities and spatial patterns, timing of thinning and fertilization treatments, and number of treatments are used. Mortality, diameter increment, and height increment models were developed and the effects of thinning, fertilization and the combination of thinning and fertilization were examined for Douglas-fir, western redcedar, and western hemlock. For each species, shade-tolerance was found to impact the possible predictor variables included in model development. The use of a generalized logistic survival model resulted in accurate estimates for larger trees, but poor results for smaller trees. To model the effects of fertilization, additional fertilization effect variables were included in the models; conversely, thinning effects were modeled using the immediate change in state variables such as basal area of larger trees which occurred immediately following thinning.