"Researching, modelling and mapping forest productivity in relation to topographically influenced soil properties, including soil moisture"

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This proposal deals with learning how forest productivity varies from ridge tops to depressions, according to topography- and substrate-influenced moisture and nutrient regimes. Until now, this continuum has been represented by thematic soil and forest surveying and mapping methods. High-resolution digital elevation models (DEM), however, are now enabling a functional approach to model and map this change in a comprehensive manner at, e.g., 1m resolution. The individual research questions addressed with this proposal are as follows: (1) How well do LiDAR-mapped metrics such as the cartographic depth-to-water index (DTW) perform in reproducing plot-based determinations of ridge-to-depression variations regarding (i) forest productivity, (ii) soil, drainage and vegetation type, (iii) productivity affecting variables such as soil texture, bulk density (or pore space), organic matter content and rooting depth, and (iv) end-of summer levels pertaining to depth-to-water and soil moisture content. (2) How well are all these measures related to the DEM-derived DTW index and other DEM-derived indices such as slope, curvature and other widely used terrain wetness indices? (3)What combination of DEM-generated soil and site variables including DTW work best in modelling and mapping tree growth across the landscape? This research, to be done in New Brunswick with industrial facilitation, features natural forest conditions interspersed by forest plantations (jack pine, black spruce, white spruce). Data layers dealing with up-to-date forest inventories, and high-resolution surface images and topography (LiDAR, point cloud data) are already available. This proposal supports a team involving graduate students, summer assistants, and technical staff to conduct the field work (plot-based soil and vegetation surveying and sampling), laboratory analyses, GIS programming, and forest productivity modelling. This research will generate GIS-based models designed to map forest productivity by dominant species type across the forest management of immediate interest, and will also deal with matters pertaining to model generalization and mapping applications elsewhere.