Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry.

In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud metrics and the estimation of forest attributes in balsam fir (Abies balsamea (L.) Mill.) dominated forests of western Newfoundland, Canada. We collected calibration data from ground plot locations representing varying scan angles from two flight lines: within 4° of nadir in one flight line, and either 11–20° from nadir (low scan angle plots: L), or 21–30° from nadir (high scan angle plots: H) in an adjacent flight line. We computed three sets of ALS point cloud metrics for each ground plot using ALS data from: individual flight lines (near-nadir and off-nadir) and data from all available flight lines (up to 4) combined (aggregated, as commonly used in an operational inventory context). We generated three sets of models for each of the L and H plots using the ALS metric sets, and applied the models to independent validation data. We analysed the effect of scan angle on both the ALS metrics and performance statistics for area-based models generated using the L and H datasets. Our results demonstrate that off-nadir scan angles significantly affected (P < 0.05) specific metrics from both L (i.e. coefficient of variation (COVAR)) and H (i.e. maximum height, 95th percentile of height, mean height) plots, although the effects were trivial (mean absolute differences were ≤ 0.01 for COVAR and < 0.3 m for the height metrics). Forest attribute predictions using these and other metrics were also significantly affected (P < 0.05), namely gross merchantable volume (GMV), total volume (TVOL) and aboveground tree biomass (AGB) from L; and Lorey’s mean height (HGT), mean diameter at breast height (DBH), and GMV from H. We further demonstrated that combining ALS data from all available flight lines significantly increased errors for the predictions of HGT, GMV, and TVOL using L, and significantly reduced errors of HGT using H when compared to errors resulting from models developed with near-nadir data. While the differences in prediction errors were significant, they were small, with differences in mean absolute prediction errors all <1.3 per cent. Based on our results, we concluded that the effects of large scan angles, up to 30° off-nadir, on area-based forest attribute predictions were minimal in this study, which used ALS metrics calculated from ALS returns with a height above ground >2 m for balsam fir-dominated forests. This result may provide for operational efficiencies in implementing enhanced forest inventories in this particular forest environment.

Airborne laser scanning (ALS) has emerged as a technology capable of generating descriptors of vegetation structure and best available terrain information. Research and operational implementations of ALS data have highlighted their value for characterizing forest structure and generating spatially explicit and objective spatial coverages and mapping products for forest management. Continued emphasis to enhance forest stewardship is promoting novel methods to integrate ALS to detail non-timber ecosystem values like habitat, soil, and water. Standardized criteria and indicator frameworks such as the Canadian Council of Forest Ministers provide a reliable starting point for where ALS has opportunities to characterize ecosystems objectively regardless of location. In this review of primarily Canadian work, we highlight how ALS is becoming an increasingly viable technology for deriving meaningful indicators to meet sustainable forest management criteria. We review and highlight the value of ALS for quantifying indicators of biological diversity, ecosystem condition and productivity, soil and water, and the role of forests in global ecological cycles. We conclude by highlighting the need for increased education, tech transfer, flexible software, and reporting frameworks alongside five key considerations for using ALS to derive meaningful indicators of sustainable forest management.

Information about wood fibre attributes (WFA) is important for optimizing forest resource management and increasing the competitiveness of the sector. Many factors influence WFA at both the plot (e.g., age, stand density, climate, and disturbance) and tree (e.g., crown development, stem shape, branchiness) levels. Recently, the use of terrestrial lidar (t-lidar) systems in forest inventory has enabled the measurement of forest structural attributes, which were almost impossible to acquire with traditional field measurements. Using t-lidar scans of individual trees and the architectural model L-Architect, we reconstructed the structure of trees and plots comprising balsam fir and black spruce in insular Newfoundland, Canada. Core samples extracted from concomitant trees were analyzed for a series of nine WFA. The impact of fine-scale structure on predictive models of WFA was assessed with parametric and non-parametric approaches. A variable importance analysis demonstrated that structural attributes derived from L-Architect describing the tree crown geometry, branching structure, stem form, spatial competition and canopy material distribution were highly important in the resulting models. The cross-validated percentage of variance explained for the WFA predictive models ranged from 12-56% and 5-80% at tree- and plot-levels respectively. The addition of fine-scale structure improved the models by 10-31% and 0-53% when compared to models developed using only in situ measurements at tree and plot-levels respectively. Information on species (at tree level) and composition (at plot level) did not improve the predictive capability of models developed with L-Architect fine-scale structure. The results indicate that better characterisation of forest structure using t-lidar and an architectural model can lead to improved WFA prediction and their combination opens opportunities to significantly enhance forest inventory.

The Canadian forest sector requires detailed information regarding the amount and characteristics of the forest resource. To address these needs, inventory systems that spatially quantify timber and other forest related ecosystem services are required, that are accurate, comprehensive and timely. The Assessment of Wood properties using Remote Sensing (AWARE) was a five-year project involving collaboration between seven Canadian universities, and seven forest companies with support provided by provincial and federal forestry agencies and other non-for-profit forestry focused organisations. AWARE provided methods and tools to enhance the characterization of forests at national, landscape and individual tree scales. The project supported 24 post-doctoral fellows, PhD and MSc students that examined the roles that advanced three-dimensional remote sensing technologies can play in the development of accurate forest inventory systems across Canada. In this review we examine the AWARE research project, review research highlights, key outcomes, future research needs, and provide an assessment of successes and challenges the project faced over its five-year lifetime.

Light detection and ranging (lidar) data acquired from airborne or spaceborne platforms have revolutionized measurement and mapping of forest attributes. Airborne data are often either acquired using multiple overlapped flight lines to provide complete coverage of an area of interest, or using transects to sample a given population. Spaceborne lidar datasets are unique to each sensor and are sample- or profile-based with characteristics driven by acquisition mode and orbital parameters. To leverage the wealth of accurate vegetation structural data from these lidar systems, a number of approaches have been developed to extend these observations over broader areas, from local landscapes to the globe. In this review we examine studies that have utilised modelling approaches to extend air- or space-based lidar data with the aim of communicating methods, outcomes, and accuracies, and offering guidance on linking lidar metrics and lidar-derived forest attributes with broad-area predictors. Modelling approaches are developed for a variety of applications. In some cases, generation of spatially-exhaustive layers may be useful for forest management purposes, driving management and inventory decisions over smaller focus areas or regions. In other cases, outputs are designed for monitoring at regional or global scales, and may be - due to the spatial grain of the structural estimates - insufficiently accurate or reliable for management. From the reviewed studies, we found height, aboveground biomass and volume, derived from either upper proportions of a large-footprint full-waveform lidar profiles, or statistically modelled from discrete return small-footprint lidar point clouds, to be the most commonly extended forest attributes, followed by canopy cover, basal area and stand complexity. Assessment of the accuracy and bias of the extrapolated forest attributes varied with both independent and model-derived estimates. The coefficient of determination (R2) was the most often reported, followed by absolute and relative (i.e., as a proportion of the mean) root mean square error (RMSE and RMSE% respectively). Compilation of the stated accuracies suggested that the variance explained in predictions of forest height ranged from R2 = 0.38 to 0.90 (mean = 0.64), RMSE from 2 to 6m and RMSE% from 12 to 34%. For volume, R2 ranged from 0.25 to 0.72 (mean = 0.53) and RMSE from 60 to 87 m3/ha and for aboveground biomass (AGB) R2 ranged from 0.35 to 0.78 (mean = 0.55) and RMSE from 28 to 44 Mg/ha. There was no consensus on the level of accuracy required to support successful extension over larger areas. Ultimately, the review suggests that the information need motivating the spatial extension over larger areas drives the choice of the type of lidar data, spatial datasets and related grain. We conclude by discussing future directions and the outlook for new approaches including new lidar-derived response variables, advances in modelling approaches, and assessment of change.

Le secteur forestier canadien a besoin d’information détaillée au sujet de la quantité et des caractéristiques des ressources forestières. Pour répondre à de tels besoins, des systèmes d’inventaire exacts, complets et opportuns qui quantifient spatialement le bois d’œuvre et les autres services écosystémiques liés aux forêts sont nécessaires. Le projet quinquennal AWARE (Assessment of Wood Attributes using Remote sEnsing [évaluation des attributs du bois à l’aide de la télédétection]) était une collaboration entre sept universités canadiennes et sept entreprises forestières soutenue par des organismes forestiers provinciaux et fédéraux et d’autres organismes sans but lucratif-axés sur la foresterie. AWARE a fourni des méthodes et des outils pour améliorer la caractérisation des forêts à une échelle nationale, du paysage et de l’arbre individuel. Vingt-quatre boursiers de recherches postdoctorales et étudiants au doctorat et à la maîtrise se sont associés au projet et ont examiné les rôles que les technologies de télédétection tridimensionnelle (3D) de pointe peuvent jouer dans la conception de systèmes d’inventaire forestier précis partout au Canada. Dans le présent article de revue, nous nous penchons sur le projet de recherche AWARE, les points saillants de la recherche, les résultats clés et les besoins futurs en recherche et présentons une évaluation des réussites et des défis auxquels le projet a été confronté au cours de ses cinq ans.

Maps of ecosystem services are becoming increasingly useful for reporting on the potential impacts of human activity on the environment. However, interactions in watersheds are complex, and mapping hydrological ecosystem services (HES) requires indicators which accurately measure underlying processes. The main objective of this study was to take advantage of the Soil and Water Assessment Tool (SWAT) and Light detection and ranging (LiDAR) data to map the erosion regulation service for a managed boreal forest watershed. To do so, SWAT and partial least-squares (PLS) regression were used to select explanatory variables for sediment yield. Variables of importance in projection (VIP) with a score > 1 were selected to develop LiDAR-based ecological indicators. Four categories of variables were identified as VIP from the PLS: (i) climate: annual precipitation, (ii) land use: forest, cutovers; (iii) land use patterns: cutover patch cohesion index, and (iv) morphometric: main channel length, channel length and sub-watershed area. The height of the 95th percentile of LiDAR returns (p95) < 5 m provided the most accurate spatial representation of cutovers and the optimal cutover patch cohesion index. Other morphometrics were obtained from a LiDAR-based digital terrain model. Explanatory variables for sediment yield were combined in a sediment erosion control (SEC) index, except for yearly average precipitation because the SEC index is not actually used as a temporal index. As expected, a negative relationship was found between sediment yield and SEC index rankings for the 2006-2015 period (Spearman, rho = -0.6, p < 0.05). Moreover, the overall agreement between SWAT and SEC index classes was 87% for 31 sub-watersheds. The study provides a list of relevant explanatory variables for modelling sediment yield in a boreal forest watershed where timber harvest activities occur. It also demonstrates the use of LiDAR data for deriving an index of the erosion regulation ecosystem service in a proxy-based approach as it had not been demonstrated previously at the watershed level. The validation method applied here fills a gap in ecosystem services mapping that could benefit studies in other watershed contexts.

Mapping the supply of ecosystem services (ESs) is essential for communicating the importance of ESs to policy makers and to demonstrate that sustainable development is being applied with due diligence by forest managers. ES mapping facilitates the integration of ES assessments into the decision-making process. To that end, the main objective of this study was to provide a mapping framework for a specific water-related regulating ES - the sediment control service (SCS) - developed for a forest-dominated watershed in western Newfoundland, Canada (640 km2). The mapping framework proposes the development of composite indicators (referred to as an index), based on a proxy variables causal re-lationship approach, which is less complex to implement than physical models and less subjective than expert opinions. Two composite indicators used to rank sub-watersheds on a relative scale were developed: one using equal weights among proxies and a second using expert opinion to assign weights to proxies. The use of an index for mapping addresses the multidimensional and complex nature of ESs. The proxy variables represent ecosystem function indicators (FI) that are necessary to describe the causal relationship between ecological functions and SCS. The validation of ESs map results is often lacking in the literature. Therefore, we propose comparing the SCS relative scales with a sediment-yield clas-sification that is simulated using the hydrological model SWAT (Soil Water Assessment Tool). To that end, the semi -distributed hydrological SWAT model has been developed. The overall agreement of the classification ranged from 35 to 81% depending upon the timeframe and weighting schemes that were applied, with better results being obtained for periods with more forest management operations and the expert-based weighting scheme. Results of the framework implementation show the potential supply of SCS at a sub-watershed scale and highlight those sub-watersheds most likely to be affected by forest management operations.

We developed a methodology for extending estimates of the presence-absence of trees and several tree species contained in the Canadian National Forest Inventory using nationally consistent Landsat data products. For a prototype boreal forest region of Newfoundland and Labrador, Canada, we modeled and assessed changes in the presence-absence of trees and tree species distributions over a 25-year period. Random Forest models of presence-absence of trees had an overall classification accuracy of 0.87 ± 0.019. For five tree species, overall classification accuracies were: 0.74 ± 0.017 for balsam fir; 0.75 ± 0.028 for black spruce; 0.64 ± 0.085 for trembling aspen; 0.64 ± 0.035 for tamarack; and 0.77 ± 0.041 for white birch. While the proportion of treed area increased by 8.5% over the 25-year period, the area occupied by black spruce declined by 13.5%. The area of balsam fir and white birch increased by 9.9% and 28.2%, respectively, while trembling aspen and tamarack changed by less than 5%. The map products developed and trends observed offer baseline information in support of long-term monitoring of treed area and tree species distributions. The demonstrated methods encourage development of spatially-explicit map products to complement spatially or temporally limited forest inventory datasets.

Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada

The value of combining Landsat time series and airborne laser scanning (ALS) data to produce regional maps of forest structure has been well documented. However, studies are often performed over single study areas or forest types, preventing a robust assessment of the approaches that produce the most accurate estimates. Here, we use Landsat time series data to estimate forest attributes across six Canadian study sites, which vary by forest type, productivity, management regime, and disturbance history, with the goal of investigating which spectral indices and time series lengths yield the most accurate estimates of forest attributes across a range of conditions. We use estimates of stand height, basal area, and stem volume derived from ALS data as calibration and validation data, and develop random forest models to estimate forest structure with Landsat time series data and topographic variables at each site. Landsat time series predictors, which were derived from annual gap-free image composites, included the median, interquartile range, and Theil Sen slope of vegetation indices through time. To investigate the optimal time series length for predictor variables, time series length was varied from 1 to 33 years. Across all six sites, increasing the time series length led to improved estimation accuracy, however the optimal time series length was not consistent across sites. Specifically, model accuracies plateaued at a time series length of similar to 15 years for two sites (R-2 = 0.67-0.74), while the accuracies continued to increase until the maximum time series length was reached (24-29 years) for the remaining four sites (R-2 = 0.45-0.70). Spectral indices that relied on shortwave infrared bands (Tasseled Cap Wetness and Normalized Burn Ratio) were frequently the most important spectral indices. Adding Landsat-derived disturbance variables (time since last disturbance, type of disturbance) did not meaningfully improve model results; however, this finding was largely due to the fact that most recently disturbed stands did not have predictions of forest attributes from ALS, so disturbed sites were poorly represented in the models. As model accuracies varied regionally and no optimal time series length was found, we provide an approach that can be utilized to determine the optimal time series length on a case by case basis, allowing users to extrapolate estimates of forest attributes both spatially and temporally using multispectral time series data.

Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13–49% and decreased prediction errors by 8–28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data.

A model comparison approach, based on Akaike's information criterion, was used to evaluate the contribution of terrestrial laser scanning (TLS) to the estimation of wood fiber attributes at the tree level for black spruce (Picea mariana (Mill.) B.S.P.) trees growing in Newfoundland, Canada. Substantial efforts were made to acquire, process, and develop accurate and detailed metrics of the tree, its crown, and its immediate environment. Based on the resulting data set, significant relationships were found, and models were successfully developed, using only TLS metrics, for predicting wood fiber attributes. The models accounted for 47%, 33%, 51%, 44%, and 52% of variance in wood density, coarseness, fiber length, microfibril angle, and modulus of elasticity, respectively, with root mean square error values of 46 kg.m(-3), 37 mu g.m(-1), 0.20 mm, 3.5 degrees, and 2.3 GPa. Our ability to estimate the wood fiber attributes was improved by combining TLS metrics with conventional field measurements. This study demonstrates that the use of TLS metrics improves the estimation of the wood fiber attributes at the tree level beyond that possible with conventional field measurements.

Improving the quality of information that can be obtained from forest inventories can enhance planning for the best use of forest resources. In this study, we demonstrate the capability to improve the characterization of forest inventory attributes using terrestrial laser scanner (TLS) data, a fine-scale architectural model (L-Architect), and airborne laser scanner (ALS) data. Terrestrial laser scanning provides detailed and accurate three-dimensional data and has the potential to characterize forest plots with comprehensive structural information. We use TLS data and in situ measurements as input to L-Architect to create reference plots. The use of L-Architect for modeling was validated by comparing selected attributes of the reference plots with validation plots produced using simulated TLS data, with normalized root-mean square error (NMRSE) values below 17%. Surrogate plots were then created using a library of tree models where individual trees were selected according to three attributes tree height, diameter at breast height, and crown projected area either measured from in situ plots or derived from ALS data. The accuracy of the surrogate plots was assessed by comparing several key forest attributes from the reference plots, including branching structure (e.g., number of whorls, knot surface), crown shape and size (e.g., base height, asymmetry), heterogeneity (e.g., lacunarity, fractal dimension), tree volume, and the spatial distribution of material (e.g., Weibull fit, leaf area index). Overall, the surrogate plots reproduced the attributes of the reference plots with NRMSE mean value of 17% (R-2 = 0.68) using in situ ground measurements and 24% (R-2 = 0.51) using inputs estimated with ALS. Some attributes, such as leaf area index, knot surface, and fractal dimension, were well predicted (R-2 > 0.80), whereas others, like crown asymmetry and lacunarity, had weak correspondence (R-2 < 0.16). The ability to create surrogate forest plots with L-Architect makes it possible to estimate detailed structural attributes that are difficult to measure with conventional forest mensuration techniques and that can be used for model calibration with above-canopy remote-sensing data sets.

Improving the quality of information that can be obtained from forest inventories can enhance planning for the best use of forest resources. In this study, we demonstrate the capability to improve mapping of forest inventory attributes using terrestrial laser scanner (TLS), a fine scale architectural model (L-Architect) and airborne laser scanner (ALS). TLS provides detailed and accurate 3D data and has the potential to characterize forest plots with comprehensive structural information. We use TLS data and in situ measurements to develop and validate reference plots using L-Architect. Then we create surrogate plots using three attributes – tree height, diameter at breast height and crown projected area – measured in situ or derived from ALS data. We assess the accuracy of the surrogate plots by comparing several key forest attributes derived from the surrogate plots with those of the reference plots including branching structure (e.g. number of whorls, knot surface), crown shape and size (e.g. base height, asymmetry), heterogeneity (e.g. lacunarity, fractal dimension), tree volume and the spatial distribution of material (e.g. Weibull fit, leaf area index). Overall, the surrogate plots reproduced the attributes of the reference plots with normalized root-mean square error (NRMSE) mean value of 17% (R = 0.68) using in situ ground measurements and 24% (R = 0.51) using inputs estimated with ALS. Some attributes such as leaf area index, knot surface and fractal dimension were well predicted (R > 0.80) while others like crown asymmetry and lacunarity had weak correspondence (R < 0.16). The ability to create surrogate forest plots with L-Architect makes it possible to estimate detailed structural attributes that are difficult to measure with conventional field methods and which can be used to calibrate above-canopy remote sensing datasets. We further used the structural attributes estimated with L-Architect to model a series of nine wood fibre attributes (WFA) measured from wood cores. WFA estimation was improved at both treeand plot-levels when fine-scale structure information was added to the set of predictor variables compared to using attributes available from conventional inventory measurements. On average R values improve by 33% and NRMSEavg reduced by 15%. Similarly plot-level WFA estimates were improved (R increased by 19% and NRMSEavg reduced by 20%) when compared with structural predictors represented by conventional height metrics derived from high density ALS data. The results indicate that better characterisation of forest structure using TLS, ALS and an architectural model improves significantly

We used a hierarchical approach to examine patterns of black spruce and balsam fir wood density across Newfoundland. Wood density measurements were aggregated at ring, tree, and plot levels. Portions of the variance in wood density at the different levels were explained by predictor variables at multiple levels of a forest structural hierarchy (ring-, tree-, and plot-level variables). Hierarchical fixed effects models accounted for 39%, 61%, and 86% of the variance in wood density of black spruce at ring, tree, and plot levels, respectively, with RMSE values of 62.7, 34.6, and 19.4 kg.m(-3). Corresponding models accounted for 31%, 38%, and 63% of the variance in wood density of balsam fir, with RMSE values of 62.4, 35, and 16.7 kg.m(-3). The hierarchical analysis demonstrated consistent negative associations of wood density to radial growth rate at ring, tree, and plot levels of aggregation. Variables that act as surrogates for radial growth are thus important for understanding patterns of wood density at higher scales and for the practical application of mapping wood density across landscapes.

Knowledge of wood fiber attributes (WFA) is important for evaluating forest resources and optimizing efficiency in the forest industry. To improve our ability to estimate WFA in the forest, we analyzed the relationships between structural metrics derived from terrestrial laser scanner (TLS) data and four key attributes of industrial significance: wood density, fiber length, microfibril angle, and coarseness. We developed a suite of structural metrics that relate to four aspects of the forest: canopy structure, competition, vegetation density, and local topography. We modeled WFA for sites dominated by black spruce (Picea mariana) and balsam fir (Abies balsamea) trees. For black spruce sites, R-2 values ranged from 63% to 72%. Structural metrics that relate to competition were the strongest explanatory variables. For balsam fir sites, R-2 ranged from 37% to 63% using structural metrics that relate mostly to canopy structure. Our results demonstrate that local structural variables are useful explanatory variables for predicting WFA of the dominant coniferous species in Newfoundland. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.

Free and open access to the more than 40 years of data captured in the Landsat archive, combined with improvements in standardized image products and increases to computer processing power have enabled the production of large-area, cloud-free, surface reflectance image composites. Following screening for atmospheric effects (e.g., cloud and related shadows, haze) and consideration of other compositing rules, such as day of year, the "best" pixel observations may be combined to produce image composites. A time series of these image composites affords novel opportunities to generate information products characterizing land cover, land cover change, and forest structural attributes in a manner that is dynamic, transparent, systematic, repeatable, and spatially exhaustive. For a country the size of Canada (approaching 10 million km2), with limited road access and a multiplicity of jurisdictions responsible for resource stewardship, remotely sensed data offers the only viable means, economic or otherwise, to generate national information products. We articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how pixel-based, image compositing approaches and resulting derived information products enable us to address these needs. Canada's terrestrial area is represented by 1285 unique WRS-2 path/rows and approximately 10.7 billion 30 m x 30 m pixels. We provide details on our compositing approach and resulting observation yield, and share lessons learned from the development of annual, national pixel-based image composites for the period 1998 to 2012. Methods for addressing spatial and temporal gaps in the pixel-based composites are presented, including approaches for filtering noise and assigning proxy values. Finally, we will share preliminary results for characterizing land cover, land cover change, and forest structure using pixel-based image composites.