Predicting forest productivity using Wet Areas Mapping and other remote sensed environmental data

  • Author / Creator
    Bjelanovic, Ivan
  • Understanding variability in forest productivity is important to sustainable forest management. The main objective of this thesis was to evaluate efficient and cost-effective ways to predict potential forest productivity (Site Index) using ecological site data obtained from either ground-based sampling or remote sensing. A geocentric approach to estimating SI was selected in order to utilize digital biophysical data for mapping fine scale variability in SI. LiDAR generated Digital Elevation Models (DEM) and Wet Areas Mapping (WAM) provide remotely sensed environmental data at a 1 m resolution for most forested land in Alberta. Relationships between environmental factors and SI of three major commercial tree species (trembling aspen, lodgepole pine, white spruce) were examined in this study by establishing a network of temporary sample plots in the Lower Foothills Natural Subregion in central Alberta. Data collection involved determination of SI and common ecological field assessment. Strong correlations were found between field determined soil properties and topography of the research area. Six different Flow Initiation Areas (FIA), from 0.5 ha to 10 ha, were tested to reveal optimal FIA for calculation of the Depth-To-Water (DTW) index for SI prediction. Results show that DTW based on smaller FIA was better in estimating aspen SI, the largest size of FIA was best for spruce SI, while the size of FIA did not influence pine SI estimation. A total of 36 species-specific Site Index models were developed for each of three data sources (DEM+WAM, WAM, field assessment) and four modeling methods (MLR, GAM, RT, RF). In terms of best predictors, among remotely sensed variables DTW was selected by each statistical method for each species and in most cases DTW is the strongest predictor in the model, while among ground-based measured variables different variables appeared as the most important for different species according to silvics specifics. In addition to revealing different major drivers, different strength of relationship was found between species. Prediction accuracy of models obtained is consistent with other similar SI-environment studies. Poorer results for spruce, than for aspen and pine, result from the wide range in ages of sampled spruce stands, the lack of spruce stands across a full range of sites, and the small number of spruce stands with top height trees free of suppression available for sampling. No significant differences in variation explained were observed between DEM+WAM and ground-based models, while WAM data by itself explained most of the total amount of SI variation explained. All four statistical methods could be used in examining SI-environment relationships but with some advantages and disadvantages for each related to data and application specifics. The study revealed that forest productivity is subject to topographic controls in this study area and variation in productivity could be explained using remotely sensed environmental data. In addition, different tree species respond differently due to contrasting autoecology. SI maps for all species were produced and plausible relationships between terrain attributes and patterns of low and high predicted productivity are generally apparent. This approach appears to adequately portray variation in productivity over short distances and is potentially applicable to forest growth and yield modeling and silviculture planning.

  • Subjects / Keywords
  • Graduation date
    Fall 2016
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Specialization
    • Forest Biology and Management
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • White, Barry (Renewable Resources)
    • Bokalo, Mike (Renewable Resources)
    • Chang, Scott (Renewable Resources)