%0 Journal Article
%T Predictive Ability of Volume-Basal Area Ratios (VBARs) in Minnesota, USA Forests to Estimate Volume Per Hectare
%A Curtis L. VanderSchaaf
%J Open Journal of Forestry
%P 226-246
%@ 2163-0437
%D 2025
%I Scientific Research Publishing
%R 10.4236/ojf.2025.153013
%X Natural resource inventories often aim to acquire desired information in the least amount of time while minimizing costs. Within Minnesota, USA due to access issues and costs, in particular for management inventories, often few field-based sampling points can be established. Additionally, inventories conducted to establish timber sales are often of stands that contain low value timber and that consequently have low sale rates. Thus, in these cases, the cost of establishing enough field-based sampling points to meet some desired statistical level of precision is not justified. Therefore, alternative inventory methods, such as the use of remotely sensed data including LiDAR, are being examined.  This study examined the ability of two methods to estimate VBAR, where volume and basal area are stand-level values. The first method is to use a constant VBAR across all conditions of a cover type. A second method is to estimate VBAR by cover type using various combinations of plot age, site index, and basal area per hectare. Data was obtained from national inventory plots as part of the US Department of Agriculture, Forest Service, Forest Inventory and Analysis program. Although LiDAR is not actually used during this assessment, results can be used to infer about the bias, precision, and accuracy associated with using LiDAR determined tree heights to predict diameter and then to estimate stand densities to ultimately estimate volume per hectare. Results showed that basal area per hectare is not a consistently useful variable to estimate VBARs. Site index and stand age are better predictors. Based on inference from this study, at the current time, it appears that the use of LiDAR to ultimately estimate volume per hectare looks most promising for those conditions that require less accuracy and precision in estimates, such as for management plan inventories and for appraisals of low value timber.
%K Forest Inventory
%K Remote Sensing
%K <i>Abies</i>
%K <i>Acer</i>
%K <i>Betula</i>
%K <i>Fraxinus</i>
%K <i>Larix</i>
%K <i>Picea</i>
%K <i>Pinus</i>
%K <i>Populus</i>
%K <i>Quercus</i>
%K <i>Thuja</i>
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=143903