Opportunities for Research at PRF Building on “State of the Art” Inventories

Murray Woods – Ontario Ministry of Natural Resources & Forestry

GMV

Dbhq

PRF Timeline of Remote Sensing Missions & Products

LiDAR (~0.5 pts/m2) & RGB imagery

LiDAR Flight lines (~3 -10 pts/m2) & Multispectral imagery

MNRF eFRi Imagery - Multispectral/Panchromatic - Image Point clouds

Riegl LiDAR (~15 pts/m2)

Multispectral LiDAR - Titan AWARE MNRF Forest Resource Inventory Delivery SkyforestTM LiDAR comparision

LiDAR Inventory - Image ITC DTM Derivatives

Earlier Remote Sensing work - PNFI studies

2006

2007

2009

2012

2016 2017

2015

2006

2007

2009

2012

2016 2017

2015

• 2006-2016 eFRI production

•Imagery - Leica ADS40/80

•Supporting calibration plots

•Increased number of descriptors

•Ecosite delineation vs. WG

Ontario’s eFRI Program

G048TtD n

Used with permission of Doug Pitt

Airborne LiDAR – Light Detection And Ranging

“Active” remote sensing technology; transmit & receive ~35,000- 500,000 pulses of NIR laser light per second

GPS + INS provides the exact X-Y-Z position of each return

Each pulse can produce multiple returns

Discrete System

Graphic: D. Pitt

Area of Interest

Measuring Forest Structure with LiDAR

Petawawa Research Forest LiDAR Derived Digital Terrain Model

2m

Advanced

Forest

Resource

Inventory

Technologies

Doug Pitt

CWFC Scientist

Emeritus

Margaret Penner Forest Analysis Ltd.

Petawawa Research Forest Predictor Surfaces

20m

20m

LiDAR Predictions 101- Statistical Measures of Point Clouds

Plots

Gridded Landbase

• Height (D-C,Top, Lorey) • DBHq • Volume (GTV, GMV) • Basal area • VBAR (Poles, Sawlogs) • Biomass/Carbon • Density

LiDAR predictive Models for:

• Mean Tree GMV • Size Class Distributions • Canopy Height • Crown Closure • Vertical Structure

Pre

dic

ted

Observed

LiDAR Derived Inventory

All Data

No species information known or needed!

PRF Inventory Rasters

Grt Lakes St. Lawrence Validation

Mh18 Be17 Pw13 PT7 By4 Bw4 Pl3 Bd3 Sw2

12

DBHq (Avg Diameter)

Percentage Absolute

Cruise LiDAR Merch Ba -5.1%

Summary STAND COUNT MEAN

BA 28.9 463 232 30.37 1.5 m2 ha

m2 ha

LiDAR Merch Dq -5.4%

Density 664 STAND COUNT MEAN

Dq 23.6 463 232 24.89 1.3 cm

cm

LiDAR GMV 4.2%

STAND COUNT MEAN

GMV 215.32 463 232 206.3 -9.0 m3 ha

m3 ha

LiDAR GTV -5.2%

STAND COUNT MEAN

GTV 277.42 463 232 291.79 14.4 m3 ha

m3 ha

Difference14.3 ha – 59 plots

PRF Validation of Stand Level LiDAR predictions - 17 stands

Intensive 50m grid sampling on 17 stands

Carbon Sequestration Mapping

Innovative Remote Sensing Technologies

Image Pixel Matching or

Semi Global Matching (SGM) or

Image Point Clouds (IPC)

Image Point Cloud DSM

• Similar to LiDAR but not the same – only one “return”

• Lower cost acquisition – intensive computer processing

• challenge for DTM creation in forested situations

Image Point Clouds (Pixel Correlation – Semi Global Matching)

SGM

Hearst Forest Plot Example

Image Point Cloud Inventory Opportunities – WITH detailed DTM

Average stem characteristics

0

10

20

30

40

50

60

70

0

20

40

60

80

100

Top Height D-C Height QM-DBH AGMV

BA GMV SLV Density

m2/ha m3/ha x 10

m3/ha x 10

stems/ha x 100

m m

cm

m3/100

Stand characteristics

Observed

LiDAR-predicted

SGM-predicted

A comparison of airborne LiDAR and digital

photogrammetric point clouds for the area-based

estimation of forest inventory attributes in boreal

Ontario

Doug G. Pitt, Murray Woods, and Margaret Penner

Canadian Journal of Remote Sensing, 40:214-232, 2014

Image Point Clouds SGM

LiDAR

Petawawa Research Forest Wet Area Mapping

Collaboration with Paul Arp, UNB - Forest Watershed Research Centre www.watershed.for.unb.ca

• Raster of depth to water table

Petawawa Individual Tree Classification - Imagery

François Gougeon & Don Leckie - CFS Victoria

Dense LiDAR – CHM 0.5m - Work with SEGMA – Benoît St.Onge UQAM

Future Inventory Opportunities - LiDAR

New opportunities with higher densities Towards individual tree inventories

Future Inventory Opportunities - LiDAR

SEGMA (Benoit St-Onge, UQAM)

LiDAR Canopy Height Model Segmentation

Pr Pw He Sw Sb

LiDAR Segmentation and Species Prediction - 2015

Mh Be Bw By

N %

Correct Total Correct

Calibration 519 701 74%

Validation 160 212 75%

N %

Correct Total Correct

Calibration 773 812 95%

Validation 229 237 97%

Predicting Hardwood from Conifer Predicting 10 Conifers x Origin (14 species)

24

Example of New Brunswick results

Credit: Dr Benoît St-Onge – UQAM (AWARE)

Hardwood vs. Conifer Species Prediction

LiDAR Intensity (Single)

Multispectral LiDAR

New Multispectral LiDAR Technology – Optech Titan

RGB Image

AWARE - Benoit St-Onge, UQAM

Thank You & Looking forward to seeing you at PRF