Abstract

Forest site productivity, usually represented by site index (SI), is a fundamental resource variable in forest management planning as it is a quantitative measure of the production capacity of forest land. Site index is usually derived from estimates of dominant height (Hdom) at a given reference age using empirical age-height curves. However, it is commonly quantified with large uncertainty in forest management inventories, resulting in economic losses due to incorrect management decisions. In this study, we used bitemporal airborne laser scanner (ALS) data acquired for a study area in southeastern Norway with a time interval of 15 years to estimate SI by means of an area-based approach. We present two practical methods for SI determination, i.e., the (1) direct and (2) indirect method. With the direct method, we regressed field observations of age-height SI against canopy height metrics derived from ALS data from the first point in time and changes in ALS metrics reflecting canopy height growth during the observation period. With the indirect method, we first modelled Hdom for the two points in time using the respective ALS metrics as predictors. We then derived SI from the initial Hdom, the estimated Hdom increment, and the length of the observation period using empirical SI curves. We used bitemporal field data collected from 80 georeferenced sample plots of size 232.9 m2 to fit the species-specific regression models for SI and Hdom. We then applied the models to an independent dataset comprising 42 georeferenced validation plots of size ∼3700 m2, for which ground reference values were collected at both points in time, to assess the precision of both methods. Both the proposed methods produced SI estimates with satisfactory precision. For the direct method, the independent validation revealed root mean squared errors (RMSE) of 1.78 and 1.08 m for Norway spruce and Scots pine, respectively, compared to 1.82 m obtained for both tree species using the indirect method. The indirect method can provide a good alternative to the direct method as field observations of SI are not required to calibrate the regression models.

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