Responsable
Osvaldo Valeria
Collaborateurs
Maxence Martin, Nicole J. Fenton, Richard Fournier, Yan Boucher
Problématique
La gestion durable des paysages forestiers ne peut être atteinte qu’à la condition d’une connaissance très fine des caractéristiques de ces territoires. Ainsi, l’un des défis récurrents que doivent affronter les forestiers est celui de l’atteinte d’un équilibre entre la qualité et le coût des inventaires. Les inventaires basés sur les photographies aériennes souffrent de plusieurs limites, notamment d’une difficulté à bien identifier et différencier les vieilles forêts, c’est à-dire les peuplements multi-étagés à structure complexe par rapport aux peuplements à structure équienne. Les inventaires sont aussi déficients dans la caractérisation structurelle de jeunes peuplements issus de traitements sylvicoles.
Objectifs
L’objectif de ce projet est d’évaluer l’efficacité des relevés aériens LiDAR à identifier et discriminer les vieilles forêts boréales et de caractériser les fonctions d’estimation de volume pour les jeunes peuplements issus de traitement sylvicole au Québec.
Méthodologie
Des relations entre les variables structurelles de terrain et les variables dérivées du LiDAR seront analysées à travers des régressions linéaires et logistiques. Les modèles seront considérés valides en fonction de leur significativité (p<0.05), des caractéristiques des résidus (normalité) et de l’efficacité prédictive du modèle (R2, pseudo-R2, Receiver Operating Characteristics). Aussi, des analyses d’ordination et de regroupement (ex. k-means clustering) seront réalisées à partir des données dérivées du LiDAR de manière à déterminer si cette méthode d’inventaire permet d’identifier des structures homogènes des peuplements matures et aussi de jeunes peuplements dans les forêts boréales du Québec. La calibration et validation du modèle sera ensuite réalisée sur l’ensemble de placettes disponibles.
Retombées escomptées
Le projet proposé permettra d’améliorer la cartographie de la complexité de vieilles forêts ainsi que la description des attributs quantitatifs et identifiables pour les jeunes peuplements par l’usage des données Lidar et d’un modèle permettant l’extrapolation. Une meilleure identification des vielles forêts et une meilleure quantification du volume de jeunes peuplements issus de traitements sylvicoles permettra une meilleure planification de l’aménagement et une meilleure quantification des ces milieux pour contrer l’érosion de la biodiversité et alimenter les modèles de calcul de la possibilité forestière.
Applicabilité
Forêt boréale aménagée
Livrables
Maurane Bourgouin-Couture, Osvaldo Valeria, Nicole J. Fenton. Predictive mapping of bryophyte diversity associated with mature forests using LiDAR-derived indices in a strongly managed landscape. 2022. Ecological Indicator 136:108585
DOI : 10.1016/j.ecolind.2022.108585
Recovery of bryophyte diversity following silvicultural treatments depends upon the reestablishment of favorable microhabitats and microclimatic conditions. Without sources of propagules (reproductive structures) within the managed landscape, however, even optimal habitat conditions would not be sufficient to ensure bryophyte diversity. To identify sources of propagules and ensure their protection, we used indices that were derived from a Digital Elevation Model (DEMs) and an airborne point cloud (LiDAR; Light Detection and Ranging) as explanatory variables to predict bryophyte biodiversity. Bryophytes were collected in the intensively managed Black Brook District of New Brunswick, Canada, in eight mature managed and unmanaged forest types (n = 38). Our results show a strong bryophyte community gradient between wetter stands (Cedar, riparian zone and Spruce-Fir) and drier stands (Tolerant Harwood and Plantation) forming two distinctive groups. Indices explaining bryophyte composition and richness were related to moisture (closest distance to a stream), canopy (canopy relief ratio, canopy closure and density) and microtopography (Topographic Position Index). Models obtained from these indices explained 75% of bryophyte composition and predicted composition with a certainty of 71% The predominance of the closest distance to a stream in our model reinforces the great importance of buffer along the hydrological network. Buffers represent a substantial propagule source for the landscape and notably increase its ecological connectivity. Although wetter sites had greater richness, the completely different composition find at drier sites suggest that biodiversity management efforts to maintain bryophytes should not be restricted to wetter stands. Our model demonstrates the potential of airborne LiDAR-derived indices as surrogates for field data in estimating and mapping bryophyte compositions to understand the variation in diversity across the managed landscape. This model can be used as a dynamic tool to target areas that represent the overall bryophyte diversity of the managed landscape to ensure protection of propagule sources and favors reestablishment.
Maxence Martin, Carlos Cerrejon Lozano, Osvaldo Valeria. Complementary airborne LiDAR and satellite indices are reliable predictors of disturbance-induced structural diversity in mixed old-growth forest landscapes. 2021. Remote Sensing of Environment 267:112746
DOI : 10.1016/j.rse.2021.112746
In old-growth forests, natural disturbances form a complex mosaic of structures, providing a wide diversity of habitats and functions of great importance. Old-growth forests are still often seen as a homogeneous whole and few remote-sensing approaches have been tested to identify their structural diversity, especially in boreal forests. The aim of this study is to use a combination of airborne LiDAR and satellite imagery to identify and discriminate old-growth forest structures resulting from different disturbance histories. The study area, which was located in the mixed boreal forest of Quebec (Canada), is Monts Valin National Park and adjacent managed territories. Balsam fir (Abies balsamea (L.) Mill) is the dominant species in the study area, but hardwood species such as white birch (Betula papyrifera Marsh.) and trembling aspen (Populus tremuloides Michx.) can also be abundant in the early succession stages. Four forest classes were studied: second-growth (logged between 1970 and 1980); transition old-growth (burned in 1920); undisturbed old-growth (unburned for at least 125 years); and disturbed old-growth forest (unburned for at least 125 years, but severely disturbed by an insect outbreak around 1980). A multivariate Random Forest model was used to discriminate the classes on 6466 1 ha tiles, based on 11 complementary LiDAR and satellite-derived indices describing stand vertical and horizontal structure, together with “greenness” and disturbance history over the last 30 years. This model had high predictive efficiency (AUC = 94.2), with 81.8% of the tiles accurately classified. Interestingly, undisturbed old-growth forests exhibited intermediate characteristics compared to transition and disturbed old-growth forests. This emphasizes that some structural attributes recognized as important for the classification of temperate and tropical old-growth forests, such as high vertical complexity, are of lesser relevance for boreal old-growth forests. In comparison to undisturbed old-growth forests, transition old-growth forests had a taller canopy of high “greenness” due to a greater hardwood abundance; disturbed old-growth forests had a higher gap fraction and heterogeneity in tree size; second-growth forests exhibited a lower and more even canopy. Misclassified tiles were explained by spatial variation in disturbance severity or different levels of forest resistance and resilience to disturbance. These misclassifications are also of ecological interest, as they highlight the nuances in structural diversity that are rarely identified by disturbance mapping. A reasonable combination of LiDAR and satellite indices was effective not only in discriminating old-growth forests from second-growth forests, but also identifying their different structures, which result from specific disturbance histories. This method could contribute to effective monitoring of changes in the areas and characteristics old-growth forest that are caused by anthropogenic and natural disturbances.
À venir
Avancement
En phase de démarrage (demande finale)
Organismes subventionnaires
MFFP, Coopérative, CRSNG-Alliance (en évaluation), RYAM, résolu Produits forestiers
Financement annuel
200 776 $
Durée
2020-2025
Dernière mise à jour :
2022-04-03 07:27:41
