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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.

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 2 m for balsam fir-dominated forests. This result may provide for operational efficiencies in implementing enhanced forest inventories in this particular forest environment.

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.

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% (R2 = 0.68) using in situ ground measurements and 24% (R2 = 0.51) using inputs estimated with ALS. Some attributes, such as leaf area index, knot surface, and fractal dimension, were well predicted (R2 > 0.80), whereas others, like crown asymmetry and lacunarity, had weak correspondence (R2

International audience; A model comparison approach, based on the 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/m3, 37 µg/m, 0.20 mm, 3.5 º, 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.

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.

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.

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)

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.

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 relationship 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 classification 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.

Avian species are often highlighted as key indicator species as they are easily and reliably studied, are sensitive to environmental change, and occupy a wide range of ecological niches. A link between structural vegetation indices and avian diversity has been widely established. Remote sensing technologies such as airborne laser scanning (ALS) can quantify forest structure and have been widely used in bird/habitat studies. However, its use to quantify avian habitat suitability in managed boreal forest has received less investigation. We developed models by best subset regression of avian habitat suitability in western Newfoundland. Data included traditional vegetation plot surveys, satellite spectral information, topographic, landscape, climatic and ALS datasets. Models were assessed using goodness of fit to provide insight into how forest structure data can improve the efficacy of habitat suitability models. Additionally, the probability of presence and abundance of several key species relative to key metrics was examined. In 2018, we observed 371 individuals of 38 bird species in 29 plots over a 10 day period. Field plot metrics produced significant (α = 0.05) models of avian presence-absence and abundance. ALS metrics alone produced significant models, but with a poorer fit than field plot metrics, while spectral metrics alone were unable to produce significant models. The addition of the other environmental data produced significant results but also had poorer fit than field plot models. While field plot data may outperform remote sensing metrics from a single sensor; spatial models, combining multi-sensor and environmental data were found to be more significant. Results can aid monitoring the environmental impacts of management decisions and climate change.

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 ~15 years for two sites (R2 = 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 (R2 = 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.

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.

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.

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 increasing computer processing and storage capabilities, have enabled the production of large-area, cloud-free, surface reflectance pixel-based image composites. Best-available-pixel (BAP) composites represent a new paradigm in remote sensing that is no longer reliant on scene-based analysis. A time series of these BAP 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. Herein, we articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how these new image compositing approaches and subsequent derived products can enable us to address these needs. We highlight some of the issues and opportu...

We explore the possibility of predicting wood fiber attributes across Newfoundland for two commercial species: black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). Estimates of key fiber attributes (including wood density, coarseness, fiber length, and modulus of elasticity) were derived from measurements of wood cores taken from sample plots representing a wide structural gradient of forest stands. Candidate models for predicting fiber attributes at plot and landscape scales were developed using an information-theoretical approach and compared based on Akaike’s information criterion. The most influential variables were stand age and the presence of precommercial thinning. Other significant explanatory variables included those that characterize vegetation structure (mean diameter at breast height, dominant height), climate (annual precipitation, mean temperature of the growing season), and geography (elevation, latitude) depending on the species and fiber attribute being modeled. At the plot level, model inference gave root mean square errors of 5.3–11.9% for all attributes. At the landscape level, prediction errors were similar (5.4–12.1%), with the added benefit of being suitable for mapping fiber attributes across the landscape. The results obtained demonstrate the potential for predicting and mapping fiber attributes over a large region of boreal forest in Newfoundland, Canada.

The objective of this study was to determine whether a suite of wood quantity and quality attributes of balsam fir and black spruce forests could be predicted using airborne laser scanner data.In situ estimates of stand structure and wood fibre attributes were derived from measurements at sample plots covering a wide range of forest conditions of insular Newfoundland. Models developed to predict field estimates explained 52 –90 per cent of the variation in structure attributes, including mean and quadratic mean diameter at breast height, average and dominant height, stem density, basal area, total and merchantable volume and above-ground total biomass. Crossvalidated root mean square errors were ,24 per cent of mean values, with the exception of stem density, for which errors were 27– 32 per cent. Models of fibre attributes explained 18 –53 per cent of the variation in fibre length, wood density, radial diameter, coarseness, microfibril angle, modulus of elasticity, wall thickness and specific surface with cross-validated root mean square errors of ,14 per cent of mean values. Similar results were achieved for fibre attribute models derived using geographic, climate and vegetation variables. The results demonstrate potential for inventory of quantity and quality attributes over a large region of boreal forests in Newfoundland, Canada.

The fine reconstruction of tree structure provides important information that is relevant to forest ecological processes and may be linked to wood quality attributes. Terrestrial laser scanners (TLS) provide detailed and accurate 3-D data and have the potential to enhance forest inventories with fine-scale information on structure. However, in forests, TLS data are limited by the effects of object occlusion and wind. Therefore, adequate tree reconstruction is not possible. In previous studies, we used the architectural model L-Architect (LiDAR data to tree Architecture) to overcome the limitations of TLS to characterize the 3-D tree structure. In this study, L-Architect was validated using structural measurements for two coniferous species found in Newfoundland (Canada), namely, balsam fir (Abies balsamea) and black spruce (Picea mariana). The model reproduced realistic tree structures using TLS data and allometric relationships to define the total amount of foliage following two main steps: branch growth...

As airborne laser scanning (ALS) gains wider adoption to support forest operations in Canada, the consistency and quality of derivative products that support long-term monitoring and planning are becoming a key issues for managers. The Canadian Consortium for Lidar Environmental Applications Research (C-CLEAR) has supported almost 200 projects across Canada since 2000, with forest-related studies being a dominant theme. In 2010 and 2011, field operations were mobilized to support 13 ALS projects spanning almost the full longitudinal gradient of Canada’s forests. This paper presents case studies for seven plus an overview of some best practices and data processing workflow tools that have resulted from these consortium activities. Although the projects and research teams are spread across Canada, the coordination and decade of experience provided through C-CLEAR have brought common methodological elements to all. It is clear that operational, analytical and reporting guidelines that adhere to community accepted standards are required if the benefits promised by ALS forestry are to be realized. A national Lidar Institute that builds upon the C-CLEAR model and focuses on developing standards, guidelines, and certified training would address this need.

A shadow fraction method was developed previously for mapping forest attributes of northern black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) forests. This paper evaluates application of the method for (i) balsam fir stands (Abies balsamea (L.) Mill.), (ii) stands with higher volume and biomass than those of previous studies, and (iii) stands with a higher composition of deciduous trees and steeper slopes. Models developed for new test sites in (i) central Labrador and (ii) western Newfoundland were not statistically different from previous models for biomass, volume, and basal area. Rela- tive root mean square errors (RMSEr) for central Labrador were slightly lower than those found in other test sites (RMSEr: 24%-29%) but higher for western Newfoundland (RMSEr = 37%-43%), attributed to the higher upper limit of measured at- tributes and increased presence of deciduous trees. Results suggest that reasonable estimates can be generated for conifer forests of northeastern Canada; however, an alternative solution is needed where mixed and deciduous stands are prevalent. Measurement of ground plots over a wider range of species composition and forest structure is recommended for broader ap- plication to northern boreal forests and to further assess the potential role of the shadow fraction method in national-scale inventory programs. Resume : Une methode basee sur la fraction d'ombre a ete developpee precedemment pour cartographier les attributs des peuplements nordiques d'epinette noire (Picea mariana (Mill.) Britton, Sterns & Poggenb.). Cet article evalue l'application de la methode pour (i) les peuplements de sapin baumier (Abies balsamea (L.) Mill.), (ii) les peuplements dont le volume et la biomasse sont plus eleves que ceux des etudes precedentes et (iii) les peuplements situes sur des pentes plus abruptes et dont la proportion d'essences feuillues est plus elevee. Les modeles developpes pour les nouveaux sites d'essai dans (i )l e centre du Labrador et (ii )l 'ouest de Terre-Neuve n'etaient pas statistiquement differents des modeles precedents dans le cas de la biomasse, du volume et de la surface terriere. Les erreurs quadratiques moyennes relatives pour le centre du Labrador etaient legerement inferieures a celles qui ont ete calculees dans d'autres sites d'essai (24%-29%), mais elles etaient plus elevees pour l'ouest de Terre-Neuve (37%-43%). Cela serait du a la limite superieure plus elevee des attributs qui ont ete mesures et a la presence accrue de feuillus. Les resultats indiquent que des estimations raisonnables peuvent etre generees pour les forets de coniferes du nord-est du Canada, mais une solution alternative est necessaire la ou les peuplements mixtes et feuillus sont repandus. La mesure de placettes terrestres representant une large gamme de composition d'especes et de structure forestiere est recommandee pour permettre une application plus large aux forets boreales nordiques et continuer a evaluer le role potentiel de la methode proposee dans les programmes nationaux d'inventaire. (Traduit par la Redaction)

This study demonstrates a method to map forest stand polygons based on four forest attributes (volume, basal area, height, and crown closure) using shadow fraction values estimated from high spatial resolution QuickBird panchromatic images. The method was tested over three test sites in northeastern Canada that were largely dominated by black spruce (Picea mariana (Mill.) BSP.). The method involved four sets of procedures: (i) estimating shadow fraction from the panchromatic band of QuickBird images, (ii) generating site-specific and global regression models linking shadow fraction with each of the four forest attributes, (iii) mapping the forest attributes as a grid layer (30 ! 30 m) for each test site using the global regression models, and (iv) generating stand polygons from the raster layers. Each test site was covered by a QuickBird satellite image with 0.6 ! 0.6 m of spatial resolution. Between 2002 and 2004, 108 ground sample plots were acquired to develop local regression models. Goodness of fit (R 2 ) of the linear regression models between shadow fraction and stand attributes ranged from 0.55 to 0.79. Relative root mean square error and bias estimates were, respectively, 0.32 and 0.15 for volume; 0.29 and 0.14 for basal area; 0.32 and " 0.04 for crown closure; and 0.17 and 0.05 for height. Stand volume maps were produced from both the shadow fraction method and conventional forest stand maps (derived from aerial photo-interpretation) for a test site. Volume patterns were similar, and total volume for the test site differed by only 5.6% between the two maps. Lastly, the raster images derived from the shadow fraction method were used to produce a stand map following guidelines similar to those used by provincial inventory. In all cases, our results suggest that the shadow fraction method is a reliable and convenient way to map forest stand polygons and related attributes of black spruce stands of northeastern forests of Canada.

Important and complex spatial decisions, such as allocating land to development or conservationoriented goals, require information and tools to aid in understanding the inherent tradeoffs. They also require mechanisms for incorporating and documenting the value judgements of interest groups and decision makers. Multiple-criteria decision analysis (MCDA) is a family of techniques that aid decision makers in formally structuring multi-faceted decisions and evaluating the alternatives. It has been used for about two decades with geographic information systems (GIS) to analyse spatial problems. However, the variety and complexity of MCDA methods, with their varying terminologies, means that this rich set of tools is not easily accessible to the untrained. This paper provides background for GIS users, analysts and researchers to quickly get up to speed on MCDA, supporting the ultimate goal of making it more accessible to decision makers. A number of factors for describing MCDA problems and selecting methods are outlined then simplified into a decision tree, which organises an introduction of key methods. Approaches range from mathematical programming and heuristic algorithms for simultaneously optimising multiple goals, to more common single-objective techniques based on weighted addition of criteria values, attainment of criteria thresholds, or outranking of alternatives. There is substantial research that demonstrates ways to couple GIS with multi-criteria methods, and to adapt MCDA for use in spatially continuous problems. Increasing the accessibility of GIS-based MCDA provides new opportunities for researchers and practitioners, including web-based participation and advanced visualisation of decision processes.

The increasing importance and complexity of land and natural resource management are creating a need for ecosystem-based management (EBM). Multiple criteria decision analysis (MCDA) combined with geographic information systems (GIS) can integrate factors related to the triple bottom line of ecological, economic, and social perspectives required by EBM. However, GIS-based MCDA is limited in this role because (i) it rarely integrates or encourages an exploration phase in preparation for structured evaluation and (ii) inexperienced users may find MCDA methods and GIS software difficult to use. This paper presents a novel approach for (i) supporting an exploration phase to help structure a problem and (ii) integrating the exploration and evaluation phases in an easy-to-use software system. The approach was validated through a land-management case study in a forest-dominated landscape with a variety of stakeholders. Case-study participants used the approach to rate areas within a timber harvest plan based on their potential for conflict with conservation values. The case-study decision analysis determined that between 1.3% and 6.6% of the harvest plan area had a conservation rating of 0.30 or higher on a scale of 0–1. The system was made available to the forest industry and other stakeholders to support harvest plan adjustments, demonstrating how such tools can be used to improve and integrate our knowledge of forest ecology and management. Assessment of participant feedback reveals that an exploration phase is effective in helping understand a problem and prepare for multiple criteria evaluation (MCE).

Capture of land cover information is a key requirement for supporting forest monitoring and management. In Canada, provincial and territorial forest stewards use land cover information to aid in management and planning activities. At the federal level, land cover information is required to aid in meeting national and international reporting obligations. To support monitoring of Canada’s forests, the Earth Observation for Sustainable Developments of Forests (EOSD) project was initiated as a partnership between the Canadian Forest Service (CFS) and the Canadian Space Agency (CSA), with provincial and territorial participation and support. The EOSD project produced a 23 class land cover map of the forested area of Canada representing circa year 2000 conditions (EOSD LC 2000). Including image overlap outside of the forested area of Canada, over 480 Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images were classified and more than 80% of Canada was mapped, culminating in the production of 630 1:250 000 map sh...

Forest inventory data often provide the required base data to enable the large area mapping of biomass over a range of scales. However, spatially explicit estimates of above-ground biomass (AGB) over large areas may be limited by the spatial extent of the forest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), or by the omission of inventory attributes required for biomass estimation. These spatial and attributional gaps in the forest inventory may result in an underestimation of large area AGB. The continuous nature and synoptic coverage of remotely sensed data have led to their increased application for AGB estimation over large areas, although the use of these data remains challenging in complex forest environments. In this paper, we present an approach to generating spatially explicit estimates of large area AGB by integrating AGB estimates from multiple data sources; 1. using a lookup table of conversion factors applied to a non-spatially exhaustive forest inventory dataset (R2 = 0.64; RMSE = 16.95 t/ha), 2. applying a lookup table to unique combinations of land cover and vegetation density outputs derived from remotely sensed data (R2 = 0.52; RMSE = 19.97 t/ha), and 3. hybrid mapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our 714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated from the forest inventory was approximately 40 Mega tonnes (Mt); however, the inventory estimate represents only 51% of the total study area. The AGB estimate generated from the remotely sensed outputs that overlap those made from the forest inventory based approach differ by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt) than the estimate generated from the forest inventory when the entire study area is accounted for. Finally, using the hybrid approach, whereby the remotely sensed inputs were used to fill spatial gaps in the forest inventory, the total AGB for the study area was estimated at 62 Mt. In the example presented, data integration facilitates comprehensive and spatially explicit estimation of AGB for the entire study area.

We have developed and tested a method for mapping above-ground forest biomass of black spruce (Picea mariana (Mill.) B.S.P.) stands in northern boreal forests of eastern Canada. The method uses QuickBird images and applies image processing algorithms to extract tree shadow fraction (SF) as a predictive variable for estimating biomass. Three QuickBird images acquired over three test sites and 108 ground sample plots (GSP) were used to develop and test the method. SF was calculated from the fraction of tree shadow area over the area of a reference square overlaid on the images. Linear regressions between biomass of GSP and SF from the images for each test site resulted in R 2 in the range from 0.85 to 0.87 (except one case at 0.41), RMSE of 11 to 18 t/ha and bias of 2 to 5 t/ha. Statistical tests demonstrated that local regressions for the three test sites were not statistically significantly different. Consequently, a global regression was calculated with all GSP and produced R 2 , RMSE, and bias of 0.84, 14.2 t/ha and 4.2 t/ha, respectively. While generalization of these results to extended areas of the boreal forest would require further assessment, the SF method provided an efficient means for mapping biomass of black spruce stands for three test areas that are characteristic of the northern boreal forest of eastern Canada (boreal and taiga shield ecozones).

A method for mapping forest biomass was developed and tested on a study area in western Newfoundland, Canada. The method, BIOmass from Cluster Labeling Using Structure and Type (BioCLUST), involves: (i) hyperclustering a Landsat TM image, (ii) automatically labeling the clusters with information about forest type and structure, and (iii) applying stand-level equations that estimate biomass as a function of height and crown closure within forest species-type classes. BioCLUST was validated with biomass values measured at geo-referenced field plots and mapped across the study area using an existing forest management photo-inventory. Root mean square error (RMSE) values ranged from 43 to 79 tonnes/ha, and were lowest for intermediate height classes when validated with field plots. Overall bias was negative at 10 tonnes/ha compared with a negative bias of 3 tonnes/ha estimated for the photo-inventory. Validation of the biomass map gave RMSE values of 37–47 tonnes/ha and overall landscape biomass estimates within 0.4% of biomass mapped by the photo-inventory. BioCLUST offers an alternative to other biomass mapping methods when scene-specific plot data are limited and a photo-inventory is available for a representative portion of a Landsat scene.

Spatial measures of forest biomass are important to implement sustainable forest management, monitor global change, and model forest productivity. Several methods for estimating forest biomass by remote sensing have been developed, but their comparative advantages have not been evaluated for large areas in Canada. This study compares four methods to map forest biomass on an extended pilot region (20,000 km 2 ) located in western Newfoundland. The methods include: (i) Direct Radiometric Relationships (DRR), (ii) k-Nearest Neighbors (k-NN), (iii) Land Cover Classification (LCC), and (iv) Biomass from Cluster Labeling Using Structure and Type (BioCLUST). The results of each method were evaluated using an independent set of ground survey plots and compared with a baseline biomass map generated from biomass tables applied to forest inventory stand maps. Considering the root mean square error (RMSE) assessed with the inventory plots, the DRR, k-NN, and BioCLUST methods provided similar results, with average RMSE values of 59, 59, and 58 t/ha, respectively. Bias values were lowest for the k-NN method followed by DRR, BioCLUST, and LCC (6, � 8, 17, and 42 t/ha, respectively). Assessed with the baseline map, the BioCLUST method produced the lowest RMSE (41 t/ha) and bias (� 4 t/ha) followed by the DRR and k-NN methods, with RMSE values of 47 and 54 t/ha and bias values of 9 and 23 t/ha, respectively. The method using biomass tables applied on the classified TM image (LCC) provided the greatest RMSE and bias, but may be suitable for applications that do not require a high level of precision. The BioCLUSTand LCC methods provided practical advantages for the type of data sets available. Overall, the choice of a method rests on both the availability of data sets and the level of precision of the results required.

Identifying appropriate validation sources for large-area land cover products is a challenge, with logistical constraints frequently necessitating the use of preexisting data sources. Several issues exist when comparing polygon (vector-based) datasets to raster imagery: geolocational mismatches, differences in features or classes mapped, disparity between the scale of polygon delineation and the spatial resolution of the image, and temporal discrepancies. To evaluate the potential impact of using vector coverages to assess the accuracy of pixel-based land cover maps, five evaluation protocols are applied to test sites located in British Columbia and Newfoundland and Labrador, Canada. One protocol directly compared the land cover of the sample unit to the land cover of the forest inventory polygon within which the sample unit fell, two protocols used different regions around the sample unit to define the land cover class, and two protocols were based on homogeneity criteria that restricted the selection of...

International audience; Analysis of vegetation spectra is often characterized by an adverse ratio of sample size to number of wavelengths. A reduction in the dimensionality of the spectra is needed to ensure consistent estimates. We propose a reduction based on a non-linear mixed modeling of power spectra transforms of truncated Fourier series representations of vegetation spectra. Two sets of foliage spectral data obtained from balsam fir (Abies balsamea) exposed to different silvicultural regimes and three eucalypt species (Eucalyptus spp.) demonstrate the method. Only the first 42 frequencies in a power spectrum contributed significantly to the variance of a spectrum. Power spectra were dominated by a small number of low frequencies; the influence of frequency was described well by an exponentiated quadratic polynomial model with significant fixed and random effects. Model parameters can be subject to physiological inference and hypothesis testing.; Méthode d'analyse des spectres de végétation par modélisation mixte non linéaire des spectres de puissance tronqués. L'analyse des spectres de végétation est souvent caractérisée par un rapport négatif entre la taille de l'échantillon et le nombre de longueurs d'ondes. Une réduction de la dimension des spectres est nécessaire pour garantir des estimations uniformes. Nous proposons une réduction fondée sur une modélisation mixte non linéaire des transformées de puissance spectrale des représentations de séries de Fourier tronquées visant des spectres de végétation. Pour ce faire, nous utilisons deux ensembles de données spectrales du feuillage de sapins baumiers (Abies balsamea) exposés à différents traitements sylvicoles et de trois espèces d'eucalyptus (Eucalyptus spp.). Seules les 42 premières fréquences de puissance spectrale ont contribué de façon appréciable à sa variance. Un petit nombre de basses fréquences dominaient les puissances spectriques ; l'effet de la fréquence a été bien décrit à l'aide d'un modèle polynomial quadratique d'exponentiation comportant des effets fixes et aléatoires appréciables. Les paramètres du modèle peuvent faire l'objet d'analyse de l'hypothèse et d'une inférence physiologique.

A priority of the Canadian Forest Service and Canadian Space Agency joint project, Earth Observation for Sustainable Development of Forests (EOSD), is the production of a land cover map of the forested area of Canada based upon Landsat data. The land cover will be produced through a partnership of federal, provincial and territorial governments, universities, and industry. The short-term goal of EOSD is to complete a land cover map representing year 2000 forested area conditions by early 2006. Over the longer term, EOSD will aim to produce land cover products to capture changes in forest conditions over time to support national and international reporting requirements. The forested area of Canada represents approximately half of Canada's landmass, requiring over 450 scenes for complete coverage (with overlap minimized). EOSD is working with provincial and territorial mapping agencies that have on-going land cover mapping programs to optimize production capacity. It is envisioned that the combined output of EOSD and provincial and territorial land cover mapping programs will be integrated with maps developed by other sectors and agencies (such as agriculture) to produce a complete representation of the land cover of Canada. Large-area land cover mapping using remote sensing is a relatively new phenomenon. Advances in data storage capabilities, computing power, and increases in the affordability of data have allowed for large-area projects to be undertaken in ways previously not possible. The manner in which a large-area mapping project is approached is related to a number of factors including the spatial extent of the area of interest, the spatial resolution of the selected sensor, and the products which are to be generated. In this communication we report on the strategy, methods, and status of the EOSD land cover mapping program of the forested area of Canada. Key words: Canada, land cover, forest inventory, EOSD, Landsat, unsupervised classification, NFI

A method of estimating and mapping aboveground tree biomass (AGTB) was developed using provincially available forest inventory databases. More specifically, AGTB conversion tables were devised to estimate biomass for stand attributes that are commonly mapped in provincial inventories over the Canadian landscape, i.e., species composition, projected crown density, and dominant tree height. AGTB is first estimated at the tree level using allometric relationships and measured stem distributions that are subsequently summed to estimate plot-level biomass. AGTB conversion tables are then computed from regression models that relate the plot-level biomass values to stand attributes. AGTB can then be mapped over the landscape by assigning the plot-level biomass values to the mapped stands. The method was developed using two provinces, Newfoundland and Labrador (N.L.) and Quebec, as test cases to assess the adaptation required between different management units. Models used to develop conversion tables from the test areas provided estimates of biomass with R2 ranging from 0.22 to 0.35 and from 0.31 to 0.64 and root mean square errors of 38 to 47 t/ha and 21 to 41 t/ha for N.L. and Quebec, respectively, based on an independent validation data set not used in the development of the models. Mapping errors and potential improvements to the models are discussed. To extend the methods developed in this study to a national map of forest AGTB will require significant adjustments to account for differences in regional inventory specifications. While the method for AGTB mapping can fulfil an important monitoring requirement in forestry, applying it to all provinces, as well as including alternate data sources for areas where inventories do not exist, such as satellite remotely sensed images, requires further research, some of which is currently in progress.

The potential to measure indices of forest vigor from foliar spectral reflectance was assessed in a range of balsam fir conditions achieved through stand manipulations. In order of increasing vigor, treatments consisted of root pruning, control, thinning, and thinning in combination with fertilization. Concentrations of chlorophyll and nitrogen increased significantly from the low to high vigor plots, and shoot lengths increased by more than three times. Balsam fir shoots from each treatment were measured for reflectance under laboratory conditions by a portable spectroradiometer with a spectral range from 350 nm to 2500 nm. Reflectance decreased consistently with vigor throughout the spectral region analyzed. Most wavebands showed significant effects of the treatments, with the most significant wavebands in the region of chlorophyll absorption between 500 nm and 740 nm. Foliage age class and sample date also affected reflectance in these wavebands. Reflectance indices, defined as the ratio and normalized difference of the most significant waveband at 711 nm and the least significant waveband at 913 nm, were affected by the treatments (P

RESUMELa reflectance spectrale du feuillage de l'orme a ete mesuree avec un spectroradiometre dans des conditions de laboratoire pour examiner les caracteristiques de reflectance associees avec les differents stades de la maladie hollandaise de l'orme. Des conditions precoces de la maladie resultent en un accroissement rapide de la reflectance dans le vert et le rouge et une reduction de la reflectance dans le proche infrarouge. Les symptomes tardifs incluent une diminution de la reflectance dans le vert et le proche infrarouge et un accroissement de la reflectance dans l'infrarouge a ondes courtes. On propose des recommandations pour la mise sur pied de programmes de detection precoce et de suivi en milieu naturel et urbain.

Adaptive management of social-ecological systems requires integration and collaboration among scientists, policy makers, practitioners, and stakeholders across multiple disciplines and organizations. Challenges associated with such integration have been attributed to gaps between how human systems are organized and how ecosystems function. To address this gap, we explore the application of information ecology as a theoretical basis for integrating human systems and natural systems. First, we provide an overview of information ecology with reference to its relationship with information theory and how we define "information." Principles governing whole-part relationships, i.e., holons and holarchies, are then used to develop a general information flow model for evolutionary, complex adaptive systems. This general model is then applied to examine a number of issues related to science-policy integration and in the development of a reference framework for practical application in adaptive management. A number of additional considerations for practical use of the framework are also discussed.

The potential of remote sensing to aid in the forecast of insect defoliation and the associated impact on forest stands was tested by examining the relationships between i) preoutbreak forest structure and growth characteristics, ii) multispectral values recorded by the Landsat Thematic Mapper (TM) sensor, and iii) subsequent patterns of defoliation and volume loss in the balsam fir ( Abies balsamea L. Mill) forests of Western Newfoundland. Forest structure and growth characteristics were measured in the field in 1992–1993 and reconstructed to represent conditions 3 years prior to an infestation of black-headed budworm, Acleris variana (Fern.), which began in 1988. Preoutbreak Landsat TM data were acquired on 8 August 1985. Spectral values were converted to approximate reflectances using PCI atmospheric correction routines and related to the field measurements. Logistic regression techniques were used to develop models for predicting forest susceptibility (probability of attack) and vulnerability (response to attack) and to assess the accuracy of the susceptibility and vulnerability forecasts. In general, susceptible stands were younger and had lower basal area and tree density, but higher leaf area index than stands that were not attacked. Vulnerable stands were older, had lower vigor and growth efficiency indices (stemwood growth per unit of leaf area), and also displayed the highest near infrared spectral values. The best predictions combined selected spectral measurements with provincial forest inventory data. These models produced classifications with accuracies of 81%, 67%, and 78% for predicting susceptibility and pre- and postoutbreak vulnerability, respectively.

The use of sapwood cross-sectional area at breast height (S) was compared with the use of diameter at breast height (D) and total height (H) for predicting branch weight per tree (B). Data obtained by destructive sampling of 58 balsam fir (Abiesbalsamea (L.) Mill.) trees growing in western and northern Newfoundland were used to fit a simple linear and a nonlinear model using D2H and to fit a simple linear and two nonlinear models using S. Coefficients of determination and standard error of estimates were similar for all equations. There were large differences in estimates of branch weight per hectare between equations for 25 cruised stands. The relationship between B and leaf area was used to evaluate the accuracy of stand estimates. The allometric equation using S as the independent variable provided the most accurate estimates.

RESUMEDes donnees numeriques obtenues avec le capteur thematique (TM) de Landsat et avec le capteur HRV de SPOT ont ete comparees en regard de leurs relations par rapport a des parametres d'accroissement des peuplements de sapins baumiers (Abies balsamea (L.) Mill.), a la structure des peuplements mesuree sur le terrain, a des parametres de peuplements utilises dans l'inventaire forestier provincial ainsi qu'a la defoliation causee par la tordeuse a tete noire de l'epinette (Acleris variana [Fern.]) dans l'Ouest de Terre-Neuve. Ces relations ont ete etudiees au moyen d'analyses de correlation, de regression multiple et de regression logistique, et la valeur de ces relations a ete evaluee en rapport avec l'amenagement forestier a Terre-Neuve. Des relations faibles mais significatives sur le plan statistique ont ete observees entre la reflectance multibande et la surface terriere (donnees SPOT seulement), le diametre (donnees TM seulement), le volume, l'accroissement du volume (donnees TM seulement), la vig...

Knowledge of forest biomass is necessary for reporting on the state of Canada's forests. It is also an indicator of carbon that enables insights on the interaction between forests and the atmosphere. Forest biomass information has largely been aspatial and derived using plot estimates from Canada's National Forest Inventory. Provincial and territorial governments and private industrial organizations have broadened the diversity of information needs and demand for methods that are more spatially explicit. These realities have resulted in a variety of data sources nested within four approaches that can be applied from local to national scales. Earth observation data contribute to each of these approaches to varying degrees and not all approaches result in large area biomass maps. This paper describes the approaches for biomass mapping in Canada, their synergism, and highlights their dynamic nature as new data sources and ongoing developments will continue to refine these approaches to estimate, map, and monitor forest biomass in Canada.