Highlights • Pooled data from nine inventory projects in Finland were used to create nationwide laserbased regression models for dominant height, volume and biomass. • Volume and biomass models provided regionally different means than real means, but for dominant height the mean difference was small. • The accuracy of general volume predictions was nevertheless comparable to relascope-based field inventory by compartments.

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An area-based approach (ABA) is the most common method used to predict forest attributes with airborne laser scanning (ALS) data. Individual-tree detection (ITD) offers an alternative to ABA; however, few studies have examined the selection of these two alternatives for the prediction of diameter distributions. We predicted diameter distributions by applying ABA and ITD in coniferous-dominated boreal forests using ALS data and examined their predictive performance based on the shapes of the diameter distributions (Gaussian, bimodal, and reverse-J). We proposed an ABA–ITD fusion for diameter distribution prediction. Firstly, the fusion was optimized and its potential was evaluated using an error index. Secondly, we offer two alternatives to incorporate the fusion into ALS-based forest inventories. Our results indicate that ITD is more prone to errors than ABA and that the predictive performance of ITD is more sensitive than ABA to the shape of the diameter distribution. The results show that ITD outperforms ABA with Gaussian diameter distributions. In contrast, ABA was seen as preferable to ITD with bimodal- or reverse-J-shaped diameter distributions. The findings indicate that ABA–ITD fusion has potential for predicting diameter distributions, although the predictive capability of ITD is limited compared with that of ABA.

This study evaluated the suitability of different airborne laser scanning (ALS) datasets for the prediction of forest canopy fuel parameters in managed boreal forests in Finland. The ALS data alternatives were leaf-off and leaf-on unispectral and leaf-on multispectral data, alone and combined with aerial images. Canopy fuel weight, canopy base height, biomass of living and dead trees, and height and biomass of the understory tree layer were predicted using regression analysis. The considered categorical forest parameters were dominant tree species, site fertility and vertical forest structure layers. The canopy fuel weight was modeled based on crown biomass with an RMSE% value of 20-30%. The canopy base heights were predicted separately for pine and spruce stands with satisfactory results the RMSE% values being 9-10% and 15-17%, respectively. Following the initial classification of the existence of an understory layer (with kappa-values of 0.47-0.53), the prediction of understory height performed well (RMSE% 20-25%) but the understory biomass was predicted with larger RMSE% values (about 60-70%). Site fertility was classified with kappa-values of 0.5-0.6. The most accurate results were obtained using multispectral ALS data, although the differences between the datasets were minor.