Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.
). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Received Nov. 11, 2003. Accepted Aug. 10, 2004. Scand. J. For. Res. 19: 558 Á/570, 2004 A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r 2 ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in rootmean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha(1 , respectively.
An existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data. Despite suboptimal calibration data, stand inventory variables such as top height, average height, basal area, gross total volume, gross merchantable volume, and above-ground biomass were estimated from 136 calibration plots and validated on 138 independent plots, with root mean square errors generally less than 20% of mean values. Stand densities (trees per ha) were estimated with less precision (30%). These relationships were used as regression estimators to predict the suite of variables for each 400-m 2 tile on the 630 000-ha forest, with predictions capable of being aggregated in any user-defined manner-for a stand, block, or forest-with appropriate estimates of statistical precision. This pilot study demonstrated that LiDAR data may satisfy growing needs for inventory data to scale operational/tactical, through strategic needs, as well as provide spatial detail for planning and the optimization of forest management activities.Key words: forest inventory, Light Detection and Ranging (LiDAR), models, Seemingly Unrelated Regression RÉSUMÉ Un ensemble de données LiDAR (télédétection par laser) recueillies pour la Forêt Roméo Malette près de Timmins en Ontario, a été utilisé pour étudier et démontrer la possibilité d'utiliser ces données pour enrichir les données existantes d'inventaire des ressources forestières de premier plan. Malgré une calibration des données inférieure à ce qui était souhaité, les variables d'inventaire des peuplements comme la hauteur moyenne supérieure, la hauteur moyenne, la surface terrière, le volume brut total, le volume marchand total et la biomasse au-dessus du sol ont été estimées à partir de 136 parcelles de calibration et validées pour 138 parcelles indépendantes, avec une erreur quadratique moyenne géné-ralement inférieure à 20 % des valeurs moyennes. La densité des peuplements (arbres par hectare) a été estimée avec moins de précision (30 %). Ces relations ont été utilisées comme estimateurs de régressions utilisées pour générer une série de variables pour chaque unité de 400 m 2 de la forêt de 630 000 ha, avec des prédictions cumulables selon la requête de l'utilisateur-pour un peuplement, pour un bloc ou pour la forêt-avec des estimations appropriées d'une précision statistique. Ce projet pilote a démontré que les données LiDAR pourraient répondre aux besoins sans cesse croissants en matière de données d'inventaire pour définir les plans opérationnels/tactiques, bien que stratégiques, ainsi que pour établir les détails spatiaux requis pour la planification et l' optimisation des activités d'aménagement forestier.
Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m −2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m −2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m −2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.
This study investigates the ability to predict forest diameter distributions from light detection and ranging (LiDAR) data using Weibull modelling for forest stands in central Ontario. Results suggest that the unimodal 2-parameter Weibull model is a promising technique for the prediction of diameter class distributions, with strong relationships evident for several subgroups (at 95% confidence, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83, and 0.65, with validation RMSE of 4.09 m 2 /ha, 0.61 stems/ha, 6.05, 0.64, 4.73, and 0.09 for basal area, stem density, and the Weibull a and b parameters for basal area and stem density, respectively). The unimodal models were found to be least effective for the irregularly shaped diameter distributions, particularly for low-density coniferous plots that have undergone shelterwood treatment. A significant improvement in results for these irregular plots was found with a finite mixture modelling approach, suggesting that finite mixture models may extend our ability to predict diameter distributions over large portions of the landscape.Key words: LiDAR, Weibull, finite mixture modeling, diameter class distributions, multiple linear regression RÉSUMÉCette étude porte sur la capacité de prédiction de la distribution des diamètres d'arbres à partir de données de détection de la lumière et de calcul de la distance (LiDAR) utilisées dans un modèle Weibull pour des peuplements forestiers du centre de l'Ontario. Les résultats laissent entendre que le modèle Weibull unimodal à 2 paramètres constitue une technique prometteuse de la prédiction de la distribution des classes de diamètre comportant de fortes corrélations évidentes pour certains sous-groupes (à un niveau de confiance 95%, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83 et 0.65, une variance de 4,09 m 2 /ha, 0,61 tiges/ha, 6,05, 0,64, 4,73 et de 0,09 respectivement pour la surface terrière, le nombre de tige par hectare et pour les paramètres a et b de Weibull portant sur la surface terrière et le nombre de tige). Les modèles unimodaux se sont avérés être moins efficaces dans le cas des distributions de forme irrégulière des diamètres, particulièrement dans le cas de parcelles de faible densité de résineux ayant subi des traitements de coupe progressive. Une amélioration significative des résultats de ces parcelles irrégulières est apparue à la suite d'une approche de modélisation pour peuplement mélangé spécifique, laissant entendre que les modèles pour peuplement mélangé spécifique pourrait accroître notre capacité de prédire la distribution des diamètres pour de grandes portions de l' écosystème.Mots clés : LiDAR, Weibull, modélisation de peuplement mélangé spécifique, distributions des classes de diamètre, régression linéaire multiple
Models were developed to predict forest stand variables for common species of the Great Lakes -St. Lawrence forest of central Ontario, Canada from light detection and ranging (LiDAR) data. Stands that had undergone various ranges of partial harvesting or initial spacing treatments from multiple geographic sites were considered. A broad forest stratification was adopted and consisted of: (i) natural hardwoods; (ii) natural conifers; and (iii) plantation conifers. Stand top height (R 2 = 0.96, 0.98, and 0.98); average height (R 2 = 0.86, 0.76, and 0.98); basal area (R 2 = 0.80, 0.80, and 0.85); volume (R 2 = 0.89, 0.81, and 0.91); quadratic mean diameter (R 2 = 0.80, 0.68, and 0.83); and density (R 2 = 0.74, 0.71, and 0.73) were predicted from low density (i.e., 0.5 point m -2 ) LiDAR data for these 3 strata, respectively. Mots clés : détection de la lumière et calcul de la distance, LiDAR, numérisation par laser aéroporté, modélisation forestière, télédétection, variables de peuplement forestier, forêt des Grands Lacs et du Saint-Laurent.
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