Enhanced forest inventory information is needed to increase potential economic benefits from wood... more Enhanced forest inventory information is needed to increase potential economic benefits from wood raw materials and create opportunities to expand or develop new forest products. It has been shown that a wide range of forest inventory attributes can be mapped at operational scales using airborne laser scanner (ALS) data. Common attributes have included a suite of structural variables, and more recently, internal wood fiber attributes. However, when wall-to-wall ALS coverage is logistically challenging or cost prohibitive, alternative approaches are needed for mapping across large areas. Accordingly, the key objective of this study was to evaluate a multi-scale approach for spatially estimating wood fiber attributes using ground, airborne and satellite data acquired for the boreal forests of Newfoundland, Canada. The study area of this investigation was the island of Newfoundland, centered around 48° 32’ 30†N and 56° 07’ 30†W and covering an area of 111,390 km2. The respo...
Forestry: An International Journal of Forest Research, 2021
In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud ... more 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 metri...
Information about wood fibre attributes (WFA) is important for optimizing forest resource managem... more 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 struc...
<jats:p> Le secteur forestier canadien a besoin d'information détaillée au sujet de la ... more <jats:p> 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. </jats:p>
Abstract The value of combining Landsat time series and airborne laser scanning (ALS) data to pro... more Abstract 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.
Page 1. 1 Forest structure characterization of balsam fir (Abies balsamea (L.) Mill.) stands with... more Page 1. 1 Forest structure characterization of balsam fir (Abies balsamea (L.) Mill.) stands with terrestrial LiDAR and fine-scale architectural modelling JEAN-FRANÇOIS CÔTÉ * , RICHARD A. FOURNIER , JOAN E. LUTHER AND OLIVIER R. VAN LIER ...
... Yet eventually, like the much later Beothuk, they disappeared for reasons that remain unclear... more ... Yet eventually, like the much later Beothuk, they disappeared for reasons that remain unclear. The exception in Western Newfoundland has been the Mi&amp;amp;amp;#x27;kmaq of relatively recent origin, who have lived primarily in Bay St. ... The explorer Jacques Cartier identified St. ...
Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at opera... more 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% an...
Enhanced forest inventory information is needed to increase potential economic benefits from wood... more Enhanced forest inventory information is needed to increase potential economic benefits from wood raw materials and create opportunities to expand or develop new forest products. It has been shown that a wide range of forest inventory attributes can be mapped at operational scales using airborne laser scanner (ALS) data. Common attributes have included a suite of structural variables, and more recently, internal wood fiber attributes. However, when wall-to-wall ALS coverage is logistically challenging or cost prohibitive, alternative approaches are needed for mapping across large areas. Accordingly, the key objective of this study was to evaluate a multi-scale approach for spatially estimating wood fiber attributes using ground, airborne and satellite data acquired for the boreal forests of Newfoundland, Canada. The study area of this investigation was the island of Newfoundland, centered around 48° 32’ 30†N and 56° 07’ 30†W and covering an area of 111,390 km2. The respo...
Forestry: An International Journal of Forest Research, 2021
In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud ... more 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 metri...
Information about wood fibre attributes (WFA) is important for optimizing forest resource managem... more 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 struc...
<jats:p> Le secteur forestier canadien a besoin d'information détaillée au sujet de la ... more <jats:p> 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. </jats:p>
Abstract The value of combining Landsat time series and airborne laser scanning (ALS) data to pro... more Abstract 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.
Page 1. 1 Forest structure characterization of balsam fir (Abies balsamea (L.) Mill.) stands with... more Page 1. 1 Forest structure characterization of balsam fir (Abies balsamea (L.) Mill.) stands with terrestrial LiDAR and fine-scale architectural modelling JEAN-FRANÇOIS CÔTÉ * , RICHARD A. FOURNIER , JOAN E. LUTHER AND OLIVIER R. VAN LIER ...
... Yet eventually, like the much later Beothuk, they disappeared for reasons that remain unclear... more ... Yet eventually, like the much later Beothuk, they disappeared for reasons that remain unclear. The exception in Western Newfoundland has been the Mi&amp;amp;amp;#x27;kmaq of relatively recent origin, who have lived primarily in Bay St. ... The explorer Jacques Cartier identified St. ...
Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at opera... more 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% an...
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