Remote Sensing Applications for Strategic and Operational ... · Applications for Strategic and Operational Forestry Grant McCartney, MSA Forest Information Systems Coordinator Chief

Rayonier Advanced Materials Confidential and Proprietary Information

Remote Sensing Applications for Strategic and Operational Forestry

Grant McCartney, MSA

Forest Information Systems Coordinator

Chief Forester's Group - Ontario


OUTLINE

▪ Rayonier Advanced Materials

▪ Tembec LiDAR Legacy

▪ Optical Remote Sensing

▪ LiDAR Remote Sensing

▪ Operational Tools

▪ Research & Development

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RAYONIER ADVANCED MATERIALS


RAYONIER ADVANCED MATERIALS

▪ Purchased Tembec in 2017

▪ Headquarters in Jacksonville, Florida

▪ Manufacturing operations in the United Sates of America, Canada and France

▪ Forestry Operations in Ontario & Quebec, Canada.

▪ Leading global supplier of high-purity, cellulose specialties, lumber, paper pulp, paper and paperboard products.

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Cellulose Specialties

▪ High purity cellulose specialties is a broad product spectrum that includes:

– Acetates used in plastics, films, textiles and filter products;

– Ethers used in paint, industrial coatings, food and pharmaceutical products;

– Specialty products used in tires, food products and automotive filters;

– Fluff pulp used in baby diapers and other personal hygiene products and food grade absorbent pads

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Rayonier Advanced Materials Confidential and Proprietary Information6

Optical Remote Sensing


Ontario’s enhanced Forest Inventory (eFRI)

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Soft Copy Stereo Viewing

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Digital Aerial PhotogrammetryRegeneration Trials

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UAV LiDAR

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Tembec LiDAR Legacy


Tembec LiDAR Legacy

▪ ‘Wall to Wall’ LiDAR Acquisitions:

– Romeo Malette 2004

– Hearst Forest 2007

▪ Operational LiDAR Acquisitions:

– Gordon Cosens Forest

– Martel Forest

▪ Used for the production of enhanced Forest Inventory (EFI)

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Operational LiDAR

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Operational Implementation of a LiDAR Inventory in Boreal Ontario

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Forestry Chronicle 87: 512-528


Romeo Malette Forest Metric Predictions

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Source: Woods et al. (2011)


LiDAR Benefits

FPInnovations Advantage 14(1)

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Improved Planning with EFI

Cost Centres

Inventory Acquisition and Processing -$0.10/m3

Forest Operations $1.38/m3

Mill Operations $0.30/m3

Total Savings: $1.58/m3

x 500,000 m3/year: $800,000/year

Payback : 1.3 years

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LiDAR – Topography, Hydrology, Height


LiDAR Topography

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LiDAR Hydrology

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Provincial Hydrology LiDAR Hydrology


LiDAR Height

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Forest Resources Inventory LiDAR Height Data


Roads Planning – Sweet Lake

▪ Roads planning using eFRI imagery

– 45 km of operational roads

▪ Roads planning using LiDAR

– 27 km of operational road

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LiDAR Forest Metrics


LiDAR Volume – Clear Cut

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• Harvested Area –

187 hectares

• Planned Forest

Inventory Volume –

32,000m³

• LiDAR derived

Volume – 43,000m³

• Actual Scaled


• LiDAR derived

volume is within 4%

of actual delivered

volume

20m x 20m Cells for GMV


LiDAR Volume - Careful Logging

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• Harvested Area –

342 hectares

• Planned Forest

Inventory Volume –

37,000m³

• LiDAR derived


• Actual Scaled


• LiDAR derived

volume was about

12% over actual

delivered volume

20m x 20m Cells for GMV

Small Birch

White Pine


LiDAR Quadratic Mean DBH

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20m x 20m Cells for Diameter

• Very easy to

identify small

wood (bypass)

• Classify Sawlog,

Pulpwood &

Oversize within

harvest block

• OSB / Veneer sort

for HWD species

• Minimize roads

built to lower

productive sites

(big cost savings)

• Design blocks to

be mill specific


Other LiDAR Metrics

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Basal Area

Above Ground Bio-Mass

Stand Density

Gross Total VolumeMore LiDAR datasets, Mean Tree

Volume, Sawlog Volume, Fuel Variables, etc.

Operational Tools


LIM Geomatics

▪ Advanced Forest Resource Inventory Decision Support – AFRIDS

▪ Op Tracker

▪ Water crossing tool

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Op Tracker

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Op Tracker™ - Mobile Op Tracker™ - Web App


Business Benefits

▪ Operators utilize liDAR predicted forest inventory metrics and elevation models to better plan their harvest activities.

▪ Improved operator experience and productivity that leverage lidar information products.

▪ Simplified data management as instead chasing operators to download GPX files from various recreational GPS units, Op Tracker™ track data are synced.

▪ Tracking information is now used to provide a first cut of depletion mapping prior to flying photography with focus shifted on QC instead of digitization.

▪ Web app provides staff with up-to-date cut progress to inform planning and silviculture activities and analytics for insight in to business operations.

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Research & Development


Enhanced Wetland Inventory

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Enhanced Wetland Inventory

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Road Construction Risk Hydrodynamics


Assessment of Wood Attributes for REmotesensing (AWARE)

▪ Model Transferability

▪ Forest Health

▪ Growth & Yield

▪ Hydrology & Surficial geology

▪ Individual Tree Inventory

▪ Species Identification

▪ Ecosystem Services

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Development Of A Forest Inventory Using Single Photon LiDAR & Assessing Decadal Forest Change

Grant McCartney – RYAM Forest ManagementDr. Nicholas Coops – UBC Forestry IRSSMartin Queinnec – UBC Forestry IRSS


Project Objectives

▪ This project has four main objectives: – (i) propose a structurally guided sampling design for locating and

collecting FRI calibration plots for model development for the 2018 SPL. [COMPLETE]

– (ii) Coordinate procurement and delivery of field data collection services using the draft Integrated Monitoring Framework (IMF) design specifications,

– (iii) develop an EFI and examine the utility of both Area Based Approach (ABA) and Individual Tree Detection (ITD) approaches using SPL data and

– (iv) assess growth and change using previous acquired 2005 LIDAR data.

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Structurally Guided Sampling for Locating Plots

▪ SPL metrics have been calculated over the entire RMF on a 20m x 20m grid (400 m2, corresponding to plot area with r=11.28 m)

▪ Height percentile metrics: p05, p10, p20, …, p90, p95, p99

▪ Average height of first returns / Average square height

▪ Cover: % first returns above 1.3 m, 5 m, 10 m and 15 m

▪ Structural Variability: standard deviation

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Principle Components Analysis

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▪ PCA was performed on 20 SPL metrics:

▪ FORESTED polygons only

– PCA1 -> 76% of variance



▪ 182 existing plots, incl. 20 IMF plots

94

%

88

%


Determine Candidate Cells For Sampling

▪ Between 30m and 200 m of roads (90% of existing plots are within 200 m of roads)

▪ Within 200 m of any existing plot accessible via 2x4 truck or 4x4 truck but > 200 m from roads

▪ Remove 100 m wide band around power lines

▪ Keep only cells located within FOR polygon (productive forest stands)

▪ 3,383,903 candidate cells for sampling

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Example where the road to access the IMF plot (square shaped) is

not in the database.


Methods to select new plots

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Minimum distance between samples having the same strata of

500 m

1) Select as much as possible samples located within 3x3

clusters (45/160 new plots)

2) If not enough clusters are available, consider neighbouring

strata in the 3x3 clusters (93/160 new plots)

3) Finally, isolated cells are considered for sampling only if

additional samples are required after steps 1 and 2 (22/160

new plots)

After random selection, inspection of orthoimages and point

cloud:

• Flag plots having similar PC1/PC2 values and located

too close to each other

• Flag plots located too close to a stand border, wetland,

river …

• Flag plots having very isolated trees

1

2 3

4

2

3

2 4

21

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4

3

2


Comparison of wall-to-wall and candidate cells

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Comparison: Existing plots vs. New plots

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257 plots: 97 existing G&Y, 20 IMF, 160 new


Plot by Species Group

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Working Group (WG)

# plots

% plots % RMF area

PO (Poplar) 110 42.5 15.2

Sb (Black spruce) 62 15.2 49.9

Pj (Jack pine) 41 11.9 11.2

BW (White birch) 36 11.8 14

SW (White spruce) 4 1.5 2.2

CE (Cedar) 2 0.8 3.7

LA (Eastern Larch) 2 0.8 1.9

BF (Balsam Fir) 1 0.4 1

Sx (Spruce mix) 1 0.4 0.5


Romeo Malette Forest – Plot Locations

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▪ ~630,000 ha

▪ Boreal Ecoregion 3E

Timmins


Final Product ▪ A shapefile /geodatabase with the 258 plots and their associated point clouds (side and top view)

attached

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Attribute Type Description

PlotID Integer Unique ID

PlotKey Integer ID corresponding to existing plots

X / Y Float XY coordinates of plots

IMF_Intens Text Is it an IMF plot ? ‘y’ / ‘n’

STEMMAP Text Stem mapping? ‘y’/’n’

PC1/PC2 Float PC1 and PC2 values

strata Integer Corresponding strata in the 10x5 matrix

p95 / cov / std

Float 95th elevation percentile, cover above 1.3 m, std of returns

Comments Text Additional comments concerning the plot location


Strata 45

Strata 52

Strata 44Strata 54

Strata 85

Strata 63


PLOT 198: FTGNAT, SB 45PJ 30LA 15BW 5PT 5, Age: 51 | Strata: 42, p95: 15.92 m , Cover: 91.6

StDev: 3.61

IMF, STEM MAP


Plot 44: FTGNAT, PJ 80SB15 BF 5, AGE 104 | Strata: 54, p95: 21.69 m , Cover:

81.4 StDev: 7.32


Plot 166: FTGNAT, PO 80PJ 10SB 10, AGE 94 | Strata: 102, p95: 29.26 m , Cover:

97.1 StDev: 5.71

Thank You

Remote Sensing Applications for Strategic and Operational ... · Applications for Strategic and Operational Forestry Grant McCartney, MSA Forest Information Systems Coordinator Chief

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