Accuracy of forest inventory mapping: Some implications for boreal forest management

Abstract

We tested the accuracy of forest resource inventory (FRI) mapping by measuring tree species composition and density on 129 forest stands in two boreal forest regions in Ontario, Canada. For each stand we used chi-square analysis to determine significant differences between interpreted and ground data and then tallied differences to estimate percent error by stand type. To determine whether or not errors were compensatory over large areas, we combined stands and ground results within larger areas of 1, 2, and 5 km radii. We used our corrected information in the contemporary Ontario forest modelling process to compare long-term, large-scale effect of errors for model results from the original FRI. For this model, we compared available harvest volumes for important species and habitat availability for six wildlife indicator species. We observed that 83 of 129 stands were incorrectly classified by species composition. Approximately 30% were also misclassified by broad forest categories of conifer, mixed or deciduous. Common boreal species, including jack pine, black spruce and trembling aspen were incorrectly classified in about half of the cases. Rate of misclassification of species among forest types was inconsistent. Errors were not compensatory across larger areas when stands were combined. Our model indicated that resulting errors in available harvest volumes were compensatory in the jack pine-black spruce-dominated forests, but not in the black spruce-mixedwoods types, where 10–20% less softwood fibre was available for harvest than predicted from the original FRI. Similarly, less poplar was predicted over 30–50 years in all management units. These errors could potentially have financial implications for forest companies and for forest management. Errors in tree species classification altered preferred habitat availability for some wildlife species but not sufficiently to suggest lack of forest sustainability. The implications for researchers seeking specific stand types for sampling is that they should expect a 30–60% error rate, depending on classification, and select additional stands to ground-truth accordingly. The study provides clear implications for any jurisdiction using aerial photograph interpretation in forest management.