In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.

Several hypotheses have been proposed to explain the direction and extent of sexual size dimorphism in anurans (in which males are usually smaller than females) as a result of sexual selection. Here, we present an analysis to test the hypothesis that sexual dimorphism in anurans is largely a function of differences between the sexes in life–history strategies. Morphological and demographic data for anurans were collected from the literature, and the mean size and age in each sex were calculated for 51 populations, across 30 species and eight genera. Comparisons across 14 Rana species, eight Bufo species and across the genera showed a highly significant relationship between size dimorphism, measured using the female–male size ratio, and mean female–male age difference. A comparison of a subset of 17 of these species for which phylogenetic information was available, using the method of independent contrasts, yielded a similar result. These results indicate that most of the variation in size dimorphism in the anura can be explained in terms of differences in the age structure between the sexes in breeding populations. If sexual selection has an effect on size dimorphism in anurans, it is likely to be only a secondary one.

Airborne laser scanning is nowadays widely used for the estimation of forest stand parameters. Prediction models have to deal with high-dimensional laser data sets as well as limited field calibration data. This problem is enhanced in mountainous areas where forest is highly heterogeneous and field data collection is costly. Artificial neural network models and support vector regression (SVR) have already demonstrated their ability to address such issues for species-specific plot volume prediction. In this letter, we compare the stand parameter prediction accuracies of support vector machines and ordinary least squares multiple-regression models for dominant height, basal area, mean diameter, and stem density. The sensitivity of these techniques to the input variables is investigated by testing data sets which include different numbers and types of laser metrics, and by reducing their dimension with principal component and independent component analyses. Whereas usual variables only reflect the vertical distribution, we also integrate the entropy of the horizontal distribution of the point cloud in the laser metrics. The results show that SVR prediction models are of similar accuracy with multiple-regression models but are more robust regarding the metrics included in the data sets. Preliminary dimension reduction of the data set by principal component analysis generally benefits more to SVR than to multiple regression. The optimal combination of laser metrics to be included in the data sets mainly depends on the forest parameter to be estimated.

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.