Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able -for the first time -to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed -specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new Correspondence: Tommaso Jucker, tel. +44 1223 333911, fax: +44 1223 333953,

A best practices guide for the use of airborne laser scanning data (ALS; also referred to as Light Detection and Ranging or LiDAR) in forest inventory applications is now available for download from the Canadian Forest Service bookstore (White et al., 2013; http://cfs.nrcan.gc.ca/publications?id= 34887). The guide, produced by the Canadian Forest Service, Natural Resources Canada, brings together state-of-the-art approaches, methods, and data to enable readers interested in using ALS data to characterize large forest areas in a costeffective manner. The best practices presented in the guide are based on more than 25 years of scientific research on the application of ALS data to forest inventory. The guide describes the entire process for generating forest inventory attributes from ALS data and recommends best practices for each step of the process-from ground sampling through to metric generation and model development. The collection of ground plot data for model calibration and validation is a critical component of the recommended approach and is described in detail in the guide. Appendices to the guide provide additional details on ALS data acquisition and metric generation.The area-based approach is typically accomplished in two steps ( Fig. 1). In the first step, ALS data are acquired for the entire area of interest (wall-to-wall coverage), tree-level measures are acquired from sampled ground plots and summarized to the plot level, and predictive models are developed (e.g., using regression or non-parametric methods). For the purposes of model development, the ALS data is clipped to correspond to the area and shape of each ground plot. A set of descriptive statistics (referred to as "metrics") are calculated from the clipped ALS data and include measures such as mean height, height percentiles, and canopy cover (Woods et al. 2011). Inventory attributes of interest are either measured by ground crews (i.e., height, diameter) or modelled (i.e., volume, biomass) for each ground plot. It is critical that ground plots represent the full range of variability in the attribute(s) of interest and to accomplish this, the use of a stratified sampling approach is recommended, preferably with strata that are defined using the ALS metrics themselves. Thus, the ALS data must be acquired and processed prior to ground sampling.Finally, predictive models are constructed using the ground plot attributes as the response variable and the ALS-derived metrics as predictors.In the second step of the area-based approach, models that were developed using co-located ground plots and ALS data are then applied to the entire area of interest to generate the desired wall-to-wall estimates and maps of specific forest inventory attributes. The same metrics that are calculated for the clipped ALS data (as described above) are generated for the wall-to-wall ALS data and the predictive equations developed from the modelling in the first step are applied to the entire area of interest using the wall-to-wall metrics. The prediction unit for this...

Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated OPEN ACCESSForests 2013, 4 519 with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.

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.