Snow Processes in Mountain Forests: Interception Modeling for Coarse-Scale Applications

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

Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at https://doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).</p

A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space

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.The European Commissio

Caractérisation des forêts de montagne par scanner laser aéroporté : estimation de paramètres de peuplement par régression SVM et apprentissage non supervisé pour la détection de sommets

Numerous studies have shown the potential of airborne laser scanningfor the mapping of forest resources. However, the application of thisremote sensing technique to complex forests encountered in mountainousareas requires further investigation. In this thesis, the two mainmethods used to derive forest information are tested with airbornelaser scanning data acquired in the French Alps, and adapted to theconstraints of mountainous environments. In particular,a framework for unsupervised training of treetop detection isproposed, and the performance of support vector regression combinedwith dimension reduction for forest stand parameters estimation isevaluated.De nombreux travaux ont montré le potentiel de la télédétection parscanner laser aéroporté pour caractériser les massifs forestiers.Cependant, l'application aux forêts complexes de montagne reste encorepeu documentée. On se propose donc de tester les deux principalesméthodes permettant d'extraire des paramètres forestiers sur desdonnées acquises en zone montagneuse et de les adapter aux contraintesspéci fiques à cet environnement. En particulier on évaluera d'unepart l'apport conjoint de la régression à vecteurs de support et de laréduction de dimension pour l'estimation de paramètres de peuplement,et d'autre part l'intérêt d'un apprentissage non supervisé pour ladétection d'arbres

Using airborne laser scanning for mountain forests mapping : support vector regression for stand parameters estimation and unsupervised training for treetop detection.

De nombreux travaux ont montré le potentiel de la télédétection parscanner laser aéroporté pour caractériser les massifs forestiers.Cependant, l'application aux forêts complexes de montagne reste encorepeu documentée. On se propose donc de tester les deux principalesméthodes permettant d'extraire des paramètres forestiers sur desdonnées acquises en zone montagneuse et de les adapter aux contraintesspéci fiques à cet environnement. En particulier on évaluera d'unepart l'apport conjoint de la régression à vecteurs de support et de laréduction de dimension pour l'estimation de paramètres de peuplement,et d'autre part l'intérêt d'un apprentissage non supervisé pour ladétection d'arbres.Numerous studies have shown the potential of airborne laser scanningfor the mapping of forest resources. However, the application of thisremote sensing technique to complex forests encountered in mountainousareas requires further investigation. In this thesis, the two mainmethods used to derive forest information are tested with airbornelaser scanning data acquired in the French Alps, and adapted to theconstraints of mountainous environments. In particular,a framework for unsupervised training of treetop detection isproposed, and the performance of support vector regression combinedwith dimension reduction for forest stand parameters estimation isevaluated

Cross-Correlation of Diameter Measures for the Co-Registration of Forest Inventory Plots with Airborne Laser Scanning Data

Continuous maps of forest parameters can be derived from airborne laser scanning (ALS) remote sensing data. A prediction model is calibrated between local point cloud statistics and forest parameters measured on field plots. Unfortunately, inaccurate positioning of field measures lead to a bad matching of forest measures with remote sensing data. The potential of using tree diameter and position measures in cross-correlation with ALS data to improve co-registration is evaluated. The influence of the correction on ALS models is assessed by comparing the accuracy of basal area prediction models calibrated or validated with or without the corrected positions. In a coniferous, uneven-aged forest with high density ALS data and low positioning precision, the algorithm co-registers 91% of plots within two meters from the operator location when at least the five largest trees are used in the analysis. The new coordinates slightly improve the prediction models and allow a better estimation of their accuracy. In a forest with various stand structures and species, lower ALS density and differential Global Navigation Satellite System measurements, position correction turns out to have only a limited impact on prediction models

Sexual size dimorphism in anurans

International audienceSeveral 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

Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning

International audienceAirborne 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

Assessing object-oriented LiDAR metrics for characterizing bird habitat in a management perspective

Light Detection and Ranging (LiDAR) provides detailed information on the three dimensional structure of the environment, and is increasingly used in habitat modeling for a wide variety of species including birds. LiDAR has been shown to improve predictive performance of species distribution models. It is recommended that explanatory variables in habitat models should be meaningful from the species point of view in order to best explain species distribution within a landscape [2]. However, is good predictive performance of a habitat suitability model sufficient to impact local conservation actions? In order to take appropriate and more efficient management decisions, we hypothesize that the metrics explaining the species distribution need to be also meaningful for managers. Some LiDAR point clouds metrics such as the standard-deviation of penetration ratio between 0.5-10m [1] are not easy to interpret. However, metrics extracted using object-oriented methods may fill this gap by giving metrics based on existing landscape components. Instead of calculating metrics over a surface unit (the pixel), an object-based classification group together neighboring points because they belong to the same overall structure which define an object type (tree, road, building, gap). The aim of this study is to improve forest management planning by using LiDAR predictors meaningful for both the species and managers. We are here focusing on the case of the Capercaillie (Tetrao urogallus), an avian species of conservation concern occurring in the French Jura Mountains. Capercaillies favor old mixed forest with a mosaic of structurally different habitats (gap openings, moderate canopy cover area, isolated resting trees, presence of shelters) and the species is threatened by habitat loss and degradation. Habitat restoration planning is thus a fundamental aspect of species conservation actions. We extracted a range of object-oriented metrics from LiDAR datasets, defined with the support of conservation experts and forest managers. We then compare habitat suitability models based on inhomogeneous point process models, such as Maxent, fitted with either commonly used “points cloud” or new “object-oriented” LiDAR metrics. Preliminary results indicate that both categories of metrics yield similarly accurate predictions of Capercaillie habitat suitability. Thus, we hope that the use of object-oriented metrics, with their likely improved interpretability, will allow for more practical recommendations supporting forest management planning in favor of Capercaillie conservation. [1] Bae, Soyeon, Bjoern Reineking, Michael Ewald, and Joerg Mueller. 2014. “Comparison of Airborne Lidar, Aerial Photography, and Field Surveys to Model the Habitat Suitability of a Cryptic Forest Species–the Hazel Grouse.” International Journal of Remote Sensing 35 (17): 6469–89. [2] Johnson, Chris J, and Michael P Gillingham. 2005.peerReviewe