Canadian Forest Service Publications
From plots to landscape: A k-NN-based method for estimating stand-level merchantable volume in the Province of Québec, Canada. 2010. Bernier, P.Y.; Daigle, G.; Rivest, L.-P.; Ung, C.-H.; Labbé, F.; Bergeron, C.; Patry, A. The Forestry Chronicle 86(4): 461-468.
Issued by: Laurentian Forestry Centre
Catalog ID: 31774
CFS Availability: PDF (request by e-mail)
Estimation of forest attributes at the stand or polygon level across the forest domain is a basic component of forest inventory programs. We tested a “k-Nearest Neighbours” (k-NN)-based method for imputing merchantable volume. Our target dataset consisted of a discrete set of forest polygons within two large forest management units, and our reference dataset was a large historical database of temporary sample plots measured over the past three decades. The linkage between the target and reference datasets was provided by polygon-level photo-interpreted forest attributes. Measurements in temporary sample plots located in all target polygons enabled us to estimate fit statistics between imputed and measured merchantable volumes. A parallel imputation exercise was also done using the current operational method used by the Province of Québec to map forest attributes over the publicly owned forest lands. Results show that the volumes estimated using the historical k-NN method show fit statistics similar to those of the operational method, with a slightly higher bias that is largely within the error term of the estimates. For both methods, the coefficient of determination between measured and imputed merchantable volume is between 0.16 and 0.19 for total volume, increases substantially when the volume is partitioned between hardwoods and softwoods, but then decreases when the volume is further distributed among species. The results underline the importance of photo-interpretation uncertainties in bounding the accuracy of volume imputation as well as the value of the k-NN procedure for imputation purposes in the context of natural forests.