Horvitz-Thompson-like estimation with distance-based detection probabilities for circular plot sampling of forests.

In circular plot sampling, trees within a given distance from the sample plot location constitute a sample, which is used to infer characteristics of interest for the forest area. If the sample is collected using a technical device located at the sampling point, eg, a terrestrial laser scanner, all trees of the sample plot cannot be observed because they hide behind each other. We propose a Horvitz-Thompson-like estimator with distance-based detection probabilities derived from stochastic geometry for estimation of population totals such as stem density and basal area in such situation. We show that our estimator is unbiased for Poisson forests and give estimates of variance and approximate confidence intervals for the estimator, unlike any previous methods. We compare the estimator to two previously published benchmark methods. The comparison is done through a simulation study where several plots are simulated either from field measured data or different marked point processes. The simulations show that the estimator produces lower or comparable error values than the other methods. In the sample plots based on the field measured data, the bias is relatively small-relative mean of errors for stem density, for example, varying from 0.3% to 2.2%, depending on the detection condition. The empirical coverage probabilities of the approximate confidence intervals are either similar to the nominal levels or conservative in these sample plots.

Moose (Alces alces) reacts to high summer temperatures by utilizing thermal shelters in boreal forests - an analysis based on airborne laser scanning of the canopy structure at moose locations.

The adaptation of different species to warming temperatures has been increasingly studied. Moose (Alces alces) is the largest of the ungulate species occupying the northern latitudes across the globe, and in Finland it is the most important game species. It is very well adapted to severe cold temperatures, but has a relatively low tolerance to warm temperatures. Previous studies have documented changes in habitat use by moose due to high temperatures. In many of these studies, the used areas have been classified according to how much thermal cover they were assumed to offer based on satellite/aerial imagery data. Here, we identified the vegetation structure in the areas used by moose under different thermal conditions. For this purpose, we used airborne laser scanning (ALS) data extracted from the locations of GPS-collared moose. This provided us with detailed information about the relationships between moose and the structure of forests it uses in different thermal conditions and we were therefore able to determine and differentiate between the canopy structures at locations occupied by moose during different thermal conditions. We also discovered a threshold beyond which moose behaviour began to change significantly: as day temperatures began to reach 20 °C and higher, the search for areas with higher and denser canopies during daytime became evident. The difference was clear when compared to habitat use at lower temperatures, and was so strong that it provides supporting evidence to previous studies, suggesting that moose are able to modify their behaviour to cope with high temperatures, but also that the species is likely to be affected by warming climate.

Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images.

The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species.