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In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters.

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models.

During the last decade, Airborne Laser Scanning (ALS) data has been used increasingly in numerous studies to assess e.g. bird habitats and biodiversity, but less often in studies focusing on ground dwelling mammals. Here, we utilized ALS data to study the role of forest structure in moose habitat use. The data consisted of 18 GPS-collared moose in western Finland. ALS data was extracted around the moose locations. The aim was to examine the habitat selection patterns of moose within a year. Special attention was given: 1) to winter to detect when moose-related forest damages occur (when moose favor young forests) and 2) to the calving period to examine the possible role of forest structure in determining where females give birth and where they move with the newborn calves. During calving, females were occupying forests with minimal amounts of vegetation below the height of five meters. Shortly after calving, the females and their calves moved to forests with dense vegetation under the height of five meters, which is explained by the increasing demand of food both for the growing calf and the lactating mother. In summer and autumn, all moose were found more often in mature forests with higher and denser canopies, which is explained by the fact that their most preferred food during autumn (e.g. berries and twigs) grows in mature forests. This trend diminished as autumn turned to winter and moose started to favor vegetation below five meters. In general, we were able to see clear patterns and differences in habitat use between sexes, as well as with calves, and we gained new and more accurate information about the role of the forest structure to calving females. The results show that ALS data alone can yield valuable additional information about wildlife ecology.

Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).

Data assimilation (DA) has a broad category of mathematical procedures for updating and calibrating existing predictions or parameter estimates using new observations. Typical DA procedures such as the Kalman filter require that the observations of a variable of interest used in a forecast of this variable to be collected just prior to the updating procedure and that they continue to be collected over time, a requirement that is not always practical for forest inventory problems. Distinct from the Kalman filter, the best linear unbiased predictor (BLUP) circumvents this time constraint, because it can calibrate the predicted values of a variable of interest using observations from other variables correlated with this variable of interest. Remote sensing played a major role in this study by providing the latter observations. The objective of this study was threefold: (1) to propose and illustrate a DA calibration procedure that incorporates simultaneous predictions and BLUP to improve prediction accuracy; (2) to expand this procedure in the framework of model-based inference; and (3) to compare the inferential properties before and after DA calibration for multiple scenarios. Four major conclusions are relevant. First, the prediction accuracies for all variables of interest were consistently increased by 10%–74% after implementing the DA procedure. Second, the DA procedure supported simultaneous inference, and the precision of the simultaneously estimated population means for the variables of interest were increased by 55%–76% after implementing this procedure. Third, the parametric bootstrap procedures used to estimate the inferential precisions for this complex estimation problem were effective and converged rapidly. In addition, this DA calibration procedure is universally applicable for DA calibrations using other data sources such as airborne or terrestrial laser scanning and spectral data, given that cross-model correlations exist. Overall, the proposed procedure demonstrated considerable potential for better serving forest inventory with respect to both simultaneous inference and inferential precision.