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Lac St. Jean Research Forest Lac St. Jean is a 20,607 square kilometres boreal forest hub site located in Quebec, north of Quebec City. To gain access to more raw data, please contact Patrice Boucher with the Government of Quebec. [Click on any image to magnify] Site Details Climate LiDAR Products Vector Products Forest Monitoring Anchor 1 Site Details Best Available Pixel (2020) Lac St. Jean Hub Site Boundary Best Available Pixel (BAP) composites use Landsat scenes to develop cloud-free, surface reflectance pixel-based image composites capable of large-area production. When incorporated in a time series, they generate land cover, land cover change, and forest structural attributes information products in a dynamic, transparent, systematic, repeatable, and spatially exhaustive manner. This figure displays the 2020 BAP composite within the Haliburton hub site in Ontario. The acquisition of all pixels for this BAP composite were within 30 days of the first of August, 2020. Lac St. Jean Digital Elevation Model (DEM) at 30 m resolution Lac St. Jean Digital Elevation Model (DEM) at 250 m resolution Anchor 2 Climate Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) Climate data for historical (1981-2020) and future (2050 and 2090) projections Anchor 3 LiDAR Derived Products Digital Elevation Model (1m) Description - An interpolation of last returns classified as 'ground' points using TIN. Pixel Values - Elevation at a 1 metre resolution. Forestry Application - The Digital Elevation Model is important for topographical information, including slope, aspect, and radiation Radiation (1m) Description - A technique to visualize a shaded relief, illuminating it with a hypothetical light Pixel Values - Raster containing a numeric value representing the solar-radiation aspect index. Values range from 0 (land oriented in a northern direction resulting in less solar radiation exposure) and 1 (land oriented with southern slopes) Forestry Application - Land oriented in northerly (values closer to zero) are typically wetter and cooler compared to land oriented in southerly (values closer to one) Slope (1m) Description - Steepness or the degree of incline of a surface based on the DEM model Pixel Values - Raster containing numeric values representing degrees of incline Forestry Application - Slope influences tree stability, harvesting solutions, productivity of harvesting and collection means, architecture of the road networks, road characteristics, and solutions related to the reclamation of degraded forested land. 20th Height Percentile (20m) Description - Height at which 20% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 20% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Aspect (1m) Description - Orientation of slope, measured clockwise in radians based on the DEM model Pixel Values - Raster containing a numeric value representing the radians of orientation Forestry Application - Land oriented in northerly are typically wetter and cooler compared to land oriented in southerly 95th Height Percentile (20m) Description - Height at which 95% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 95% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Canopy Height Model (1m) Description - Based on an interpolation of the height of the top of trees (using the pitfree algorithm) Pixel Values - Raster containing a numeric value for the distance between the ground and the top of trees Forestry Application - Helpful for determining the distribution of canopy coverage Entropy (20m) Description - Shannon entropy quantifies the diversity and evenness of an elevation distribution of LiDAR points from 2m above the ground Pixel Values - Entropy results range from 0 to 1. Random data has a Shannon entropy value of 1 Forestry Application - Useful for describing and quantifying species diversity in biological systems. Canopy Cover > 2m (20m) Description - Canopy cover at a height greater than 2 metres Pixel Values - Ratio from the sum of first returns > 2 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 2 metres LAIe (20m) Description - A measurement of the gap fraction through the probability of beam penetration of sunlight through the vegetation. Pixel Values - Ratio of one-sided green leaf area per unit ground surface area Forestry Application - Important growth index for the status of crop populations Mean Height (20m) Description - Mean height of first returns above 2m from "ground" (last return data) Pixel Values - Mean height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the mean height of all objects (trees) that are at least 2m tall Mean Standard Deviation (20m) Description - Standard Deviation of height distributions above 2m Pixel Values - Standard deviation height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the standard deviation for the height of all objects (trees) that are at least 2m tall Skewness (20m) Description - A measure of the distribution's symmetry from 2m above the ground Pixel Values - A normal distribution would produce skewness results of zero. Negative values indicate that data is skewed to the left, and positive values indicate that data is skewed to the right. Forestry Application - Skewness is often used with kurtosis to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 5m (20m) Description - Canopy cover at a height greater than 5 metres Pixel Values - Ratio from the sum of first returns > 5 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 5 metres Kurtosis (20m) Description - The size of the tails of a distribution (likelihood that the distribution will produce outliers) from 2m above the ground Pixel Values - A normal distribution would produce kurtosis results of 3. Distributions with kurtosis less than 3 are platykurtic (fewer and less extreme outliers) and distributions with kurtosis greater than 3 are laptokurtic (produce more outliers) Forestry Application - Kurtosis is often used with skewness to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 15m (20m) Description - Canopy cover at a height greater than 15 metres Pixel Values - Ratio from the sum of first returns > 15 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 15 metres Rumple (20m) Description - Crown Surface Roughness from 2m above the ground Pixel Values - A ratio of canopy outer surface area to ground surface area Forestry Application - Higher rumple values indicate more vertical and horizontal heterogeneity Anchor 4 Vector Products

< Retour Optimization of the characterization of burning patterns OB 1b Hub: Lac-St-Jean, QC Year: 2021-2022 Gabrielle Thibault, MSc Despite being less frequent in the East, fires remain the main cause of natural disturbances in the boreal forests of Quebec (QC) and Ontario (ON) and regeneration success is highly variable in forests dominated by black spruce. An MSc student will collaborate with the PhD student (OB.1a) and adapt the workflow to the LSJ and RM boreal Hubs. Using metrics of natural stand establishment that characterize failures or successes, the objective will be to improve our understanding the ecological factors that lead to black spruce regeneration after fire disturbance. Outcome (OB.1b): Identification of key stand- and landscape-level characteristics that favour regeneration success and improve forest resilience. Gabrielle Thibault, MSc at Université Laval Main Partner: Ministère des Forêts, de la Faune et des Parcs (Québec) Professor Alexis Achim ​

< Retour Seasonal mosaics of forest cover OB 3a Hub: All Year: 2021-2023 Micheal Burnett, RA There is a critical need for accurate and timely monitoring for near real time forest condition assessment. The increasing availability of moderate and high-resolution imagery, with daily or weekly repeat, is enabling the regular characterization of entire forest areas. Micheal Burnett will apply and adapt approaches to image fusion and compositing to Sentinel-2, Landsat and Planet multispectral data to produce seasonal composites over large forest areas. Outcome (OB.3a): For each Hub site, an operational coding framework on how to derive seasonal mosaics of forest cover and condition and implement a change detection approach. Micheal Burnett, RA at University of British Columbia Main Partner: Kruger Inc. Professor Nicholas Coops Collaborator Alexis Achim ​

< Retour Silvicultural practices at the pace of global changes: a public policy challenge AD 8a Hub: All Year: 2022-2023 Anne Bernard, PDF In the face of global changes, the scientific community is actively developing solutions to respond to the pressures on forest ecosystems and, more broadly, on territories. In order to be implemented, the innovative silvicultural practices that result from these research initiatives will have to integrate the various legislative frameworks that govern forestry practices. In Canada, this involves the provincial and federal levels. In this research project, we will attempt to answer the following question: How can new forestry practices be linked to global changes? More specifically, the research objective is to understand the regulatory and social context of forestry practices in Canada. To achieve this, an analysis of public policies in six provinces will be conducted. We will also look at the influence of international organizations on national and regional forest policies. This research is part of the Silva21 partnership, which includes 11 study areas in six provinces (British Columbia, Ontario, Quebec, New Brunswick, Nova Scotia and Newfoundland-Labrador). For these areas, we will develop different socio-economic portraits for each forest case study. The methodologies envisioned to meet the project's objectives are part of public policy analysis. The expected results will take various forms. First, it is expected to produce scientific articles for Canadian forestry professionals (approximately one per province or per region: West, Ontario-Quebec and Maritimes). The case studies will document local realities for use by all Silva 21 members to present the social and political context of the study areas. Finally, one or two scientific articles will be developed for the scientific community specialized in public policy and/or forest governance. Anne Bernard, PDF at Université Laval Main Partner: Ministère des Forêts, de la Faune et des Parcs (Québec) Professor Alexis Achim Collaborator Maude Flamand-Hubert Canuel, C-M., Bernard, A., Thiffault, N., et al. (2022). Analysis of a wood production strategy from expert perspectives. The Forestry Chronicle, 98(1): 1-9. https://pubs.cif-ifc.org/doi/pdf/10.5558/tfc2022-004 Bernard, A., Gélinas, N. (2022) La multifonctionnalité du territoire forestier dans tous ses états. In: Une économie écologique pour le Québec: Comment opérationnaliser une nécessaire transition, PUQ.

< Retour Identifying and assessing the impact of non-stand-replacing disturbances within a near- real-time context OB 5c Hub: Quesnel Year: 2023-2024 Madison Brown, M.Sc. Once a robust continuous forest inventory framework has been developed (OB 5a), we will implement the proposed methodologies through two MSc projects at the Quesnel hub site. This third MSc student will have the objective to apply the framework and develop tools to assess forest stand stresses, such as infestation, wind damage or disease. The MSc student will use a series of indicators of forest health derived at the 20 x 20 m resolution and assess which predicted metrics are sufficiently reliable to be considered into silvicultural prescriptions for a range of stand ages and composition. Outcome (OB.5c): Recommendations of a series of metrics that can be derived at a 30 x 30 m resolution to guide silvicultural interventions dependent on stand condition. Madison Brown, MSc at University of British Columbia Main Partner: Department of Energy and Resource Development (New Brunswick) Professor Nicholas Coops ​

< Retour Tree-level response to thinning AN 7 Hub: Montmorency research forest, QC; Lac-St-Jean, QC Year: 2021-2023 Marilou Yargeau, MSc Stem growth is slow within the boreal forest, resulting in typically smaller stems that yield little value for high operational costs. In these environments, increased volume of individual stems usually results in higher economic return due to higher proportions of sawlogs. However, little knowledge exists on the response of individual stems to silvicultural treatments such as thinning, and how they interact with site and climatic conditions. In addition, clearcutting in the boreal forest has resulted in stand homogenization. Marilou Yargeau, an MSc student, will evaluate different thinning scenarios as a tool to restore old-growth forest attributes. She will conduct case studies in the boreal conditions with the objective to model how the growth of individual stems responds to various forms of thinning. Predictions will be integrated into stand-level stem size distributions, which can then be input into wood processing simulators to optimize revenue estimates against silvicultural costs. Outcome: Integration of stem-size distributions to existing growth and yield models with a possibility to simulate the effects of various thinning scenarios. Marilou Yargeau, MSc at Université Laval Main Partner: Ministère des Forêts, de la Faune et des Parcs (Québec) Professor Evelyne Thiffault Collaborator Miguel Montoro Girona ​

< Retour Metrics for silvicultural prescription OB 5d Hub: Black Brook & Acadia, NB Year: 2023 - 2025 Once a robust continuous forest inventory framework has been developed (OB 5a, b, c), we will implement the proposed methodologies through two MSc projects at the ARF and BB Hub sites. This third MSc student will have the objective to apply the framework and develop tools to assess forest stand stresses, such as infestation, wind damage or disease. The MSc student will use a series of indicators of forest health derived at the 20 x 20 m resolution and assess which predicted metrics are sufficiently reliable to be considered into silvicultural prescriptions for a range of stand ages and composition. Outcome (OB.5c): Recommendations of a series of metrics that can be derived at a 20 x 20 m resolution to guide silvicultural interventions dependent on stand condition. Main Partner: JD Irving Professor: Loïc D'Orangeville ​

Nova Scotia Research Forest Nova Scotia is a 459 square kilometres Acadian forest hub site located in Nova Scotia, west of Halifax. To gain access to more raw data, please contact Bruce Stewart and Kevin Keys with the Nova Scotia Government. [Click on any image to magnify] Site Details Climate LiDAR Products Vector Products Forest Monitoring Anchor 1 Site Details Best Available Pixel (2020) Best Available Pixel (BAP) composites use Landsat scenes to develop cloud-free, surface reflectance pixel-based image composites capable of large-area production. When incorporated in a time series, they generate land cover, land cover change, and forest structural attributes information products in a dynamic, transparent, systematic, repeatable, and spatially exhaustive manner. This figure displays the 2020 BAP composite within the Haliburton hub site in Ontario. The acquisition of all pixels for this BAP composite were within 30 days of the first of August, 2020. Nova Scotia Hub Site Boundary Nova Scotia Digital Elevation Model (DEM) at 30 m resolution Nova Scotia Digital Elevation Model (DEM) at 250 m resolution Anchor 2 Climate Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) during summer months Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) during winter months Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) during summer months Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) during winter months Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) during summer months Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) during winter months Anchor 3 LiDAR Derived Products Digital Elevation Model (1m) Description - An interpolation of last returns classified as 'ground' points using TIN. Pixel Values - Elevation at a 1 metre resolution. Forestry Application - The Digital Elevation Model is important for topographical information, including slope, aspect, and radiation Slope (1m) Description - Steepness or the degree of incline of a surface based on the DEM model Pixel Values - Raster containing numeric values representing degrees of incline Forestry Application - Slope influences tree stability, harvesting solutions, productivity of harvesting and collection means, architecture of the road networks, road characteristics, and solutions related to the reclamation of degraded forested land. Aspect (1m) Description - Orientation of slope, measured clockwise in radians based on the DEM model Pixel Values - Raster containing a numeric value representing the radians of orientation Forestry Application - Land oriented in northerly are typically wetter and cooler compared to land oriented in southerly Radiation (1m) Description - A technique to visualize a shaded relief, illuminating it with a hypothetical light Pixel Values - Raster containing a numeric value representing the solar-radiation aspect index. Values range from 0 (land oriented in a northern direction resulting in less solar radiation exposure) and 1 (land oriented with southern slopes) Forestry Application - Land oriented in northerly (values closer to zero) are typically wetter and cooler compared to land oriented in southerly (values closer to one) 20th Height Percentile (20m) Description - Height at which 20% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 20% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy 95th Height Percentile (20m) Description - Height at which 95% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 95% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Canopy Height Model (1m) Description - Based on an interpolation of the height of the top of trees (using the pitfree algorithm) Pixel Values - Raster containing a numeric value for the distance between the ground and the top of trees Forestry Application - Helpful for determining the distribution of canopy coverage Entropy (20m) Description - Shannon entropy quantifies the diversity and evenness of an elevation distribution of LiDAR points from 2m above the ground Pixel Values - Entropy results range from 0 to 1. Random data has a Shannon entropy value of 1 Forestry Application - Useful for describing and quantifying species diversity in biological systems. Canopy Cover > 2m (20m) Description - Canopy cover at a height greater than 2 metres Pixel Values - Ratio from the sum of first returns > 2 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 2 metres LAIe (20m) Description - A measurement of the gap fraction through the probability of beam penetration of sunlight through the vegetation. Pixel Values - Ratio of one-sided green leaf area per unit ground surface area Forestry Application - Important growth index for the status of crop populations Mean Height (20m) Description - Mean height of first returns above 2m from "ground" (last return data) Pixel Values - Mean height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the mean height of all objects (trees) that are at least 2m tall Mean Standard Deviation (20m) Description - Standard Deviation of height distributions above 2m Pixel Values - Standard deviation height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the standard deviation for the height of all objects (trees) that are at least 2m tall Skewness (20m) Description - A measure of the distribution's symmetry from 2m above the ground Pixel Values - A normal distribution would produce skewness results of zero. Negative values indicate that data is skewed to the left, and positive values indicate that data is skewed to the right. Forestry Application - Skewness is often used with kurtosis to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 5m (20m) Description - Canopy cover at a height greater than 5 metres Pixel Values - Ratio from the sum of first returns > 5 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 5 metres Kurtosis (20m) Description - The size of the tails of a distribution (likelihood that the distribution will produce outliers) from 2m above the ground Pixel Values - A normal distribution would produce kurtosis results of 3. Distributions with kurtosis less than 3 are platykurtic (fewer and less extreme outliers) and distributions with kurtosis greater than 3 are laptokurtic (produce more outliers) Forestry Application - Kurtosis is often used with skewness to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 15m (20m) Description - Canopy cover at a height greater than 15 metres Pixel Values - Ratio from the sum of first returns > 15 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 15 metres Rumple (20m) Description - Crown Surface Roughness from 2m above the ground Pixel Values - A ratio of canopy outer surface area to ground surface area Forestry Application - Higher rumple values indicate more vertical and horizontal heterogeneity Anchor 4 Vector Products

INFOLETTRES Notre infolettre est un excellent moyen de se tenir au courant de toutes les actualités de Silva21 ! Ils incluent des nouvelles concernant les dates importantes et les événements à venir, les nouvelles recherches et données, les opportunités de carrière suggestions de lectures. Pour vous inscrire à notre infolettre, remplissez le formulaire en bas de cette page . September 2023 Français English AGM Recap (July 2023) Français English March 2023 Français English December 2022 Français English September 2022 Français English July 2022 Français English April 2022 Français English February 2022 Français English November 2021 Français English August 2021 Français English June 2021 Français English Avez-vous quelque chose que vous aimeriez que nous ajoutions dans notre prochaine newsletter? Remplissez simplement les détails de ce formulaire avec toutes les informations et Amy (notre coordinatrice scientifique) fera le suivi si nécessaire ! Contribuez à notre infolettre Prénom Nom de famille Adresse courriel Vos nouvelles à partager Merci pour votre message. Soumettre

< Retour Regeneration after catastophic disturbance OB 1a Hub: Quesnel, BC; Malcolm Knapp, BC Year: 2021-2024 Sarah Smith-Tripp, PhD While silviculturists can generally ensure regeneration success following harvest, stand re-establishment following catastrophic disturbances such as fire is much more variable. BC has experienced two of the largest ever recorded fire years in terms of area burned over the past three years. As a result, many hundreds of thousands of hectares need re-establishing, not all under active forest management. This project will utilise synoptic broad scale remote sensing approaches with the objective to provide a landscape assessment of the poorly regenerated areas. Sarah Smith-Tripp (PhD) will develop approaches by first assessing the rates of recovery of spectral indices from imagery and monitor broad scale re-establishment, and then examine how establishment success varies across the landscape, using these satellite indicators and field data, in response to species, topography, climate and disturbance type. Outcome (OB.1a): New techniques to assess re-establishment after severe disturbance over large areas, and areas prioritised for remediation measures. Sarah Smith-Tripp, PhD at University of British Columbia Main Partner: Future of Forestry Think Tank (British Columbia) Professor Nicholas Coops Collaborator Dominik Roeser Smith-Tripp, S., Coops, N.C., Mulverhill, C., White, J.C., Axelson, J. 2024. Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests. ISPRS Journal of Photogrammetry and Remote Sensing, 208:121-135. https://doi.org/10.1016/j.isprsjprs.2024.01.008. Smith-Tripp, S., Coops, N., White, J. 2022. Combining forest structure measurements with satellite spectral observations for forest recovery monitoring in burned environments of British Columbia, Canada. Forest Disturbances and Ecosystem Dynamics in a Changing World, International symposium, Berchtesgaden National Park, Germany. https://sarahsmithtripp.github.io/publications/

Quesnel Research Forest Quesnel is a 20,454 square kilometres dry inland forest hub site located in the middle of British Columbia, south of Prince George. To gain access to more raw data, please contact Jodi Axelson with the Government of British Columbia and Erin Robinson with the City of Quesnel. [Click on any image to magnify] Site Details Climate LiDAR Products Vector Products Forest Monitoring Anchor 1 Site Details Best Available Pixel (2020) Quesnel Hub Site Boundary Quesnel Digital Elevation Model (DEM) at 30 m resolution Best Available Pixel (BAP) composites use Landsat scenes to develop cloud-free, surface reflectance pixel-based image composites capable of large-area production. When incorporated in a time series, they generate land cover, land cover change, and forest structural attributes information products in a dynamic, transparent, systematic, repeatable, and spatially exhaustive manner. This figure displays the 2020 BAP composite within the Haliburton hub site in Ontario. The acquisition of all pixels for this BAP composite were within 30 days of the first of August, 2020. Quesnel Digital Elevation Model (DEM) at 250 m resolution Anchor 2 Climate Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) Climate data for historical (1981-2010) and future (2050 and 2090) projections Anchor 3 LiDAR Derived Products Digital Elevation Model (1m) Description - An interpolation of last returns classified as 'ground' points using TIN. Pixel Values - Elevation at a 1 metre resolution. Forestry Application - The Digital Elevation Model is important for topographical information, including slope, aspect, and radiation Slope (1m) Description - Steepness or the degree of incline of a surface based on the DEM model Pixel Values - Raster containing numeric values representing degrees of incline Forestry Application - Slope influences tree stability, harvesting solutions, productivity of harvesting and collection means, architecture of the road networks, road characteristics, and solutions related to the reclamation of degraded forested land. Aspect (1m) Description - Orientation of slope, measured clockwise in radians based on the DEM model Pixel Values - Raster containing a numeric value representing the radians of orientation Forestry Application - Land oriented in northerly are typically wetter and cooler compared to land oriented in southerly Radiation (1m) Description - A technique to visualize a shaded relief, illuminating it with a hypothetical light Pixel Values - Raster containing a numeric value representing the solar-radiation aspect index. Values range from 0 (land oriented in a northern direction resulting in less solar radiation exposure) and 1 (land oriented with southern slopes) Forestry Application - Land oriented in northerly (values closer to zero) are typically wetter and cooler compared to land oriented in southerly (values closer to one) 20th Height Percentile (20m) Description - Height at which 20% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 20% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy 95th Height Percentile (20m) Description - Height at which 95% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 95% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Canopy Height Model (1m) Description - Based on an interpolation of the height of the top of trees (using the pitfree algorithm) Pixel Values - Raster containing a numeric value for the distance between the ground and the top of trees Forestry Application - Helpful for determining the distribution of canopy coverage Mean Height (20m) Description - Mean height of first returns above 2m from "ground" (last return data) Pixel Values - Mean height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the mean height of all objects (trees) that are at least 2m tall Mean Standard Deviation (20m) Description - Standard Deviation of height distributions above 2m Pixel Values - Standard deviation height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the standard deviation for the height of all objects (trees) that are at least 2m tall Entropy (20m) Description - Shannon entropy quantifies the diversity and evenness of an elevation distribution of LiDAR points from 2m above the ground Pixel Values - Entropy results range from 0 to 1. Random data has a Shannon entropy value of 1 Forestry Application - Useful for describing and quantifying species diversity in biological systems. Skewness (20m) Description - A measure of the distribution's symmetry from 2m above the ground Pixel Values - A normal distribution would produce skewness results of zero. Negative values indicate that data is skewed to the left, and positive values indicate that data is skewed to the right. Forestry Application - Skewness is often used with kurtosis to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Kurtosis (20m) Description - The size of the tails of a distribution (likelihood that the distribution will produce outliers) from 2m above the ground Pixel Values - A normal distribution would produce kurtosis results of 3. Distributions with kurtosis less than 3 are platykurtic (fewer and less extreme outliers) and distributions with kurtosis greater than 3 are laptokurtic (produce more outliers) Forestry Application - Kurtosis is often used with skewness to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 2m (20m) Description - Canopy cover at a height greater than 2 metres Pixel Values - Ratio from the sum of first returns > 2 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 2 metres Canopy Cover > 5m (20m) Description - Canopy cover at a height greater than 5 metres Pixel Values - Ratio from the sum of first returns > 5 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 5 metres Canopy Cover > 15m (20m) Description - Canopy cover at a height greater than 15 metres Pixel Values - Ratio from the sum of first returns > 15 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 15 metres Rumple = LAIe (20m) Description - A measurement of the gap fraction through the probability of beam penetration of sunlight through the vegetation. Pixel Values - Ratio of one-sided green leaf area per unit ground surface area Forestry Application - Important growth index for the status of crop populations Rumple (20m) Description - Crown Surface Roughness from 2m above the ground Pixel Values - A ratio of canopy outer surface area to ground surface area Forestry Application - Higher rumple values indicate more vertical and horizontal heterogeneity Anchor 4 Vector Products

Reconnaissances territoriale Silva21 reconnaît les recherches en cours menées sur les territoires traditionnels, ancestraux et non cédés des communautés autochtones qui ont été leur foyer pendant d'innombrables générations. Nous reconnaissons les diverses cultures, langues et histoires autochtones qui ont façonné et continuent d'enrichir ces terres. Nous rendons hommage aux aînés, passés et présents, et exprimons notre gratitude aux communautés autochtones qui ont préservé les forêts et les terres sur lesquelles nous vivons, travaillons et jouons. Nous reconnaissons que les effets de la colonisation, passés et actuels, ont eu de profondes répercussions sur les peuples autochtones et leur relation avec la terre. Nous nous engageons à apprendre des communautés des Premières Nations et à travailler en partenariat avec elles pour promouvoir les principes de réconciliation, de respect et de collaboration dans le domaine de la foresterie. Alors nous invitons tous la communauté de Silva21 à réfléchir au rôle vital que jouent les connaissances, les perspectives et les voix autochtones dans l'élaboration de notre compréhension de l'écosystème forestier et de la gestion durable des forêts. Joignez-vous à nous pour vous engager dans des efforts continus visant à établir des relations significatives avec les communautés autochtones, à reconnaître leurs droits et leur souveraineté et à intégrer les connaissances et les perspectives autochtones dans nos recherches et nos pratiques dans un avenir de réconciliation, d'équité et de durabilité. Information par institutions University of British Columbia UBC Vancouver is located on the traditional, ancestral, and unceded territory of the Musqueam people. The land it is situated on has always been a place of learning for the Musqueam, who for millennia have passed on their culture, history, and traditions from one generation to the next on this site. University of Toronto The University of Toronto is located on the traditional territory of the Wendat, the Anishnaabeg, Haudenosaunee, Métis, and the Mississaugas of the New Credit First Nation. University of Alberta The University of Alberta, its buildings, labs, and research stations are primarily located on the traditional territory of Cree, Blackfoot, Métis, Nakota Sioux, Iroquois, Dene, and Ojibway/Saulteaux/Anishinaabe nations; lands that are now known as part of Treaties 6, 7, and 8 and homeland of the Métis. The University of Alberta respects the sovereignty, lands, histories, languages, knowledge systems, and cultures of First Nations, Métis and Inuit nations. Université Laval Dans un esprit d’amitié et de solidarité, l’Université Laval rend hommage aux Premiers Peuples de ces lieux. Étant à la croisée du Niowentsïo du peuple Huron-Wendat, du Ndakina du peuple Wabanaki, du Nitassinan du peuple Innu, du Nitaskinan du peuple Atikmekw et du Wolastokuk Malécite, nous honorons nos relations les uns avec les autres. University of New Brunswick UNB stands on the unsurrendered and unceded traditional Wolastoqey land. The lands of Wabanaki people are recognized in a series of Peace and Friendship Treaties to establish an ongoing relationship of peace, friendship and mutual respect between equal nations. En savoir plus sur la terre où vous vivez, travaillez et jouez Utilisez la carte interactive à www.native-land.ca pour en savoir plus sur les territoires, les langues et les traités des Premières Nations qui existent là où vous vivez, travaillez et jouez et partout dans le monde.