Predicting and mapping site index in operational forest inventories using bitemporal airborne laser scanner data

Abstract Forest productivity reflects the wood production capacity of a given site and provides crucial information for forest management planning. The most widely accepted measure of forest productivity is site index (SI), defined as the average height of dominant trees at a given index age. In forest management inventories, SI is commonly interpreted manually from aerial images. While the use of airborne laser scanner (ALS) data has revolutionized operational practices for estimating many forest attributes relevant to forest management planning, practices for determining SI remain unchanged. The main objective of this study was to demonstrate a practical method for predicting and mapping SI in repeated ALS-based forest inventories. We used data acquired as part of three operational large-scale forest inventories in southeastern Norway. First, we identified areas in which forest growth had remained undisturbed since the initial inventory. We then regressed field predictions of SI against bitemporal ALS canopy metrics and used the regression models to predict SI for forest areas classified as undisturbed. The result was SI maps constructed with a spatial resolution of 15.81 m. User accuracies of class predictions of undisturbed forest in the three districts were 92%, 95% and 89%. Plot-level validation revealed root mean squared errors of SI predictions ranging from 1.72 to 2.84 m for Norway spruce, and 1.35 to 1.73 m for Scots pine. The method presented here can be used to map SI over large areas of forest automatically, depicting forest productivity at a much finer spatial resolution than what is common in operational inventories.