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

One to rule them all? Assessing the performance of Forest Europe’s biodiversity indicators against multitaxonomic data

Most broad-scale forest biodiversity indicators are based on data from national forest inventories and are used to assess the state of biodiversity through several regional initiatives and reporting. Although valuable, these indicators are essentially indirect and evaluate habitat quantity and quality rather than biodiversity per se. Besides, most of these indicators are applicable at regional or national scales, while their use at a more local level is difficult. Therefore, their link to biodiversity may be weak, which decreases their usefulness for decision-making. For several decades, Forest Europe indicators assessed the state of European forests, in particular its biodiversity. However, no extensive study has been conducted to date to assess the performance of these indicators against multitaxonomic data. We hypothesized that – as implied by the reporting process – no single biodiversity indicator from Forest Europe can represent overall forest biodiversity, but that several – eventually combined – indicators would reflect habitat quality for at least some taxa in a comprehensive way. We tested the set of indicators proposed by Forest Europe against the species richness of six taxonomic and functional groups (tracheophytes, epixylic and epiphytic bryophytes, birds, saproxylic beetles, saproxylic non-lichenized fungi and epixylic and epiphytic lichenized fungi) across several hundreds of plots over Europe. We showed that, while some indicators perform relatively well across groups (e.g. deadwood volume), no single indicator represented all biodiversity at once, and that a combination of several indicators performed better. Surprisingly, some indicators showed weak links with the biodiversity of the six taxonomic and functional groups. Forest Europe indicators were chosen for their availability and ease of understanding for most people. However, our analyses showed that there are still gaps in the monitoring framework, and that surveying certain taxa along with stand structure is necessary to support policymaking and tackle forest biodiversity loss at the large scale

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Projet PROTEST - Prospective Territoriale Spatialisée

International audienceLe projet PROTEST : mise en œuvre sur le PNR du Massif des Bauges d'une méthode d'analyse territoriale combinant cartographie de la ressource forestière par télédétection LiDAR, démarche participative de prospective, simulations d’évolution forestière et comptabilité carbon

Cartographie par télédétection LiDAR des forêts du canton Uri (Suisse)

This report presents the forest resource mapping in kanton Uri with field data from the Swiss National Forest Inventory (NFI) and the LiDAR data from Swisstopo. This work was funded by Kanton Uri, and implemented in 2024 by LESSEM, INRAE. The main work tasks were: 1/ LFI and LiDAR data preparation 2/ Tree-level comparison of LiDAR and NFI data for co-registration improvement 3/ Calibration of estimation models for basal area, volume, mean and dominant diameter, stem density 4/ Production of raster maps for each forest parameter (estimate and confidence interval) 5/ Inference of estimates and confidence intervals for administrative entities. The project deliverables were: 1/ Rasters maps for each forest parameter (estimated value and confidence interval) within the forest area of kanton Uri. 2/ Project report, including description of data, methods and results. 3/ Scripts of R code used for analysis and processing (https://forge.inrae.fr/lidar/kanton_uri)Une cartographie des ressources forestières dans le canton Uri a été réalisée à l'aide des données de terrain de l'Inventaire forestier national (LFI) suisse et des données LiDAR de Swisstopo. Ce travail a été financé par le canton Uri et mis en œuvre en 2024 par le LESSEM (INRAE). Les principales tâches étaient les suivantes 1/ Préparation des données LFI et LiDAR 2/ Comparaison des données LiDAR et LFI pour l'amélioration de la géolocalisation 3/ Calibration des modèles d'estimation pour la surface terrière, le volume, le diamètre moyen et dominant, la densité des tiges 4/ Production de cartes raster pour chaque paramètre forestier (estimation et intervalle de confiance) 5/ Inférence des estimations et des intervalles de confiance pour les entités administratives. Les résultats du projet sont les suivants 1/ Cartes raster pour chaque paramètre forestier (valeur estimée et intervalle de confiance) dans la zone forestière du canton Uri. 2/ Rapport de projet, comprenant une description des données, des méthodes et des résultats. 3/ Scripts du code R utilisé pour l'analyse et le traitement (https://forge.inrae.fr/lidar/kanton_uri

lidaRtRee: un package R pour l'analyse forestière avec des données LiDAR

lidaRtRee provides functions for forest objects detection, structure features computation, model calibration and mapping:- co-registration of field plots with LiDAR data (Monnet and Mermin, 2014); - tree detection (method 1 in Eysn et al., 2015) and segmentation; - forest parameters estimation with the area-based approach: model calibration with ground reference, and maps export;- extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful for e.g. habitat suitability modeling (Glad et al., 2020) or forest maturity mapping (Fuhr et al., 2022).lidaRtRee est un package R pour l'analyse de la structure des forêts à partir de données acquises par scanner laser (LiDAR) aéroporté :- détection d'arbres (méthode 1 dans Eysn et al., 2015) et segmentation ;- estimation de variables forestière et cartographie par approche surfacique.Il propose des fonctions additionnelles telles que :- géoréférencement des données de terrain avec les données LiDAR (Monnet and Mermin, 2014);- extraction de statistiques et d'objets (trouées, lisières, arbres) utilisables par exemple pour la modélisation d'habitat (Glad et al., 2020) et la cartographie de la maturité des forêts (Fuhr et al., 2022);- calibration de modèles avec des données de terrrain ;- production de cartes.Le package est disponible sur CRAN. Des tutoriels sont disponibles dans la documentation

COMPARAISON DE MÉTHODES DE SPATIALISATION POUR L’AGRÉGATION PAR PARCELLE DES ESTIMATIONS DE PARAMÈTRES FORESTIERS PAR LIDAR AÉROPORTÉ

Aors que les méthodes de modélisation des paramètres forestiers à partir de placettes de terrain et de données LiDAR aéroporté ont fait l’objet de nombreuses publications dans la dernière décennie, la question de leur utilisation et de leur évaluation à l’échelle de la parcelle forestière (surface de l’ordre de quelques hectares) reste peu documentée. La présente étude s’appuie sur un jeu de donnée d’inventaire en plein de 35 parcelles forestières sur 380 ha pour comparer différentes stratégies de spatialisation lors de l’application des modèles et d’agrégation des estimations de paramètres forestiers par parcelle. Les résultats montrent une diminution des erreurs entre les estimations à l’échelle de la placette et celles à l’échelle de la parcelle de 15 à 6.4% pour la surface terrière, de 26 à 7.7% pour la densité de tiges et de 6.5 à 3.4% pour le diamètre. À l’échelle de la parcelle, la précision de l’inventaire basé sur les données LiDAR se révèle similaire à celle d’un inventaire en plein, pour la surface terrière. Pour la spatialisation lors de l’application des modèles, le plus important est de respecter la taille des placettes de calibration terrain, alors que pour l’agrégation par parcelle le traitement des bordures reste délicat quel que soit le paramètre forestier.</jats:p

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