Integration of remote sensing-based bioenergy inventory data and optimal bucking for stand-level decision making

This paper tests the reliability of a biomass prediction procedure which combines aerial data collection, biometric models and optimisation for forest management planning. Tree stock information is obtained by predicting species-specific diameter and height distributions by a combination of field sampling, ALS data and aerial photographs. The subsequent steps in the chain are (1) assignment of the plots to forestry operation classes by means of remote sensing-based tree stock estimates, (2) estimation of the biomass components removed by simulating forestry operations, and (3) estimation of forest owners' income flow from optimised bucking of the species-specific diameter distributions. The error effects caused by these steps are analysed, and the applicability of remote sensing-based data collection for biomass inventories and planning is assessed. The approach used for assigning the plots to operation classes resulted in moderate accuracies (75%). The reliability estimates indicated quite poor performance when predicting the biomass components removed in forest treatments, with RMSEs of 33.0-69.4% in the case of final cutting and 76.9-228.0% in the case of thinning. The relative RMSEs of the above-ground biomass estimates of the standing stock were about 19%. The relative bias for the biomasses removed was 10.0-88.6% and that for the standing stock biomasses 0.0%. When optimising bucking, the bucked assortments were larger and the incomes enhanced with this estimation method relative to the reference. This explains why the estimation of forest owner's incomes in the energy wood thinning simulations led to suboptimal decisions and income losses.