Newfoundland Fibre Inventory Project Maximizing the value of forests through enhanced inventory of fibre attributes

Newfoundland Fibre Inventory Project Maximizing the value of forests through enhanced inventory of fibre attributes

Background CBPPL Atlantic Innovation Fund (AIF) Project maintain and increase the competitiveness of CBPPL and the province s forest industry by making better use of wood and fibre resources. Enhanced Fibre Inventory Project maximize the value of Newfoundland s fibre resources through the development of an enhanced inventory of fibre attributes in the forest.

CBPPL AIF Project Objectives 1. Determine wood fibre properties in standing trees and on chip streams feeding CBPP 2. Establish databases of wood and fibre properties that can be incorporated into the provinces digital forest inventory (GIS) framework 3. Develop models relating key wood fibre properties to the mill s processing parameters and end-product properties.

CBPPL AIF Project Activities Project has several distinct activities on-going which can be grouped into two major categories: 1. Forest related 2. Mill related

Forest Related Activities 1. Core Sample Collection 1930 cores 2. Core Sample Analysis 3. Field Probe Testing 4. Correlation of Data to Forest Conditions 5. Integration into Provincial Forest Inventory

Mill Related Activities 1. On-line NIR Chip Sensor 2. Process / Product Modeling 3. Mill Trials 4. Fibre and Process Optimization Strategies

Conclusion By the end of the project we should have: forest inventory GIS maps showing the fibre characteristics important to mill processing and paper quality. Also important for lumber industry. a mill yard that has wood sorted based on fibre properties, not simply species improved mill efficiency based on our ability to manage the fibre characteristics of the wood feeding the mill the ability to better match end-product quality to customer demand by better controlling the inputs and the processing.

Enhanced Fibre Inventory System Spatially-explicit information on the attributes contained in Newfoundland s fibre basket Attributes need to be related to product potential and value GIS layers that can support: better harvest planning segregation of raw materials according to end-use and for maximum value

1. Terrestrial LiDAR 4. Tree level models 2. High resolution imagery and airborne LiDAR 5. Plot / stand level models 3. Medium resolution imagery Approach Land area: > 11 million ha Productive forest area: > 3 million ha 6. Enhanced plot / stand level models ENHANCED FIBRE INVENTORY SYSTEM

1. Terrestrial LiDAR 4. Tree level models 2. High resolution imagery and airborne LiDAR 5. Plot / stand level models 3. Medium resolution imagery 6. Enhanced plot / stand level models Core sampled 193 plots Crown measured 114 plots TLiDAR sampled 106 plots >400 trees Test sites ENHANCED FIBRE INVENTORY SYSTEM

1. Terrestrial LiDAR 4. Tree level models 2. High resolution imagery and airborne LiDAR 5. Plot / stand level models 3. Medium resolution imagery 6. Enhanced plot / stand level models ENHANCED FIBRE INVENTORY SYSTEM Existing Elevation Annual mean precipitation. inventory temp. Working Height Age Value class class Celsius mm Group Balsam 7 High: 5.7 1750 836 Fir Black 6 Spruce 5 Low: 890-0.2-5 4 3 2 1

Data management Geodatabase A central data repository for spatial data storage and management

Inventory Tools for measuring and mapping forest structural attributes related to value e.g. species, height, diameter, basal area, branchiness, gapiness, leaf area index, etc.

Correlations Models Maps for predicting and mapping intrinsic fibre attributes from measured structural and environmental variables e.g. fibre length, width, wood density, microfibril angle, etc.

Fibre attributes Wood density Modulus of elasticity Fibre wall thickness Microfibril angle Fibre length Coarseness Ratio of radial to tangential fibre diameter *Attribute Database (from CBPPL AIF Project) 7 attributes 193 plots 10 trees per plot ~1930 cores individual ring measurements

Airborne LiDAR & high res. imagery Stephenville Test Site WorldView-2 - October 2010

Research Elements 1. Terrestrial LiDAR 4. Tree level models 2. High resolution imagery and airborne LiDAR 5. Plot / stand level models 3. Medium resolution imagery 6. Enhanced plot / stand level models Enhanced fibre inventory system

1. Terrestrial LiDAR (J.F. Coté) Architectural modeling for detailed characterization of tree and plot structure (e.g. crown dimensions, branchiness, gapiness, leaf area, etc.)

2. High Resolution Imagery & Airborne LiDAR Methods and models for withinstand estimation and mapping of forest structure attributes Species classification Stand delineation Structural attributes by species within a stand E.g. - # of stems - average crown area - average height - height standard deviation - leaf area index -biomass -.

3. Medium Resolution Imagery Methods and models for spatialization of withinstand estimates of forest structure Medium resolution imagery Predicted structural attributes

4. Tree Level Models (A. Groot) Modeling fibre attributes from tree and plot structure Black Spruce Balsam Fir Structural Variables Fibre Attributes * tree species diameter at breast height tree height height to live crown crown dimensions branchiness gapiness leaf area etc. wood density radial fibre diameter tangential fibre diameter fibre length microfibril angle Coarseness MOE wall thickness specific surface area *measured from core samples by Silviscan and related to product performance

5. Plot/Stand Level Models (E. Lessard) Modeling and mapping fibre attributes using existing forest inventory and environmental layers (site, stand and tree characteristics) Predicted fibre attribute

6. Enhanced Plot/Stand Level Models (D. Blanchette) Modeling and mapping fibre attributes using forest structure attributes derived from airborne LiDAR and/or optical imagery

Enhanced Fibre Inventory System Supporting value chain optimization by adding detailed forest structure and related fibre attributes to the forest management inventory Inventory Planning / Processing Products

Benefits Use information about fibre attributes to allocate wood for best end use Feed harvest planning systems with more precise data Control variability in fibre attributes going into the mill Better meet customer demands for products with specific quality characteristics (e.g. strength) Create opportunities to expand or develop new products

Competition / Alternative Existing system Volume based attributes represent amount of wood e.g. species, height, crown density, age, merchantable volume Based on photo interpretation Represent average stand conditions Enhanced system Value based attributes linked to product quality e.g. wood density, fibre length, microfibril angle, etc. Based on semiautomated processing Represents withinstand variability and distributions

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