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Remote Sensing for Phenotyping Authors: David Pont, Mike Watt, Jonathan Dash, Rod Brownlie

Remote Sensing for Phenotyping - WordPress.com

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Page 1: Remote Sensing for Phenotyping - WordPress.com

Remote Sensing for Phenotyping Authors:

David Pont, Mike Watt, Jonathan Dash, Rod Brownlie

Page 2: Remote Sensing for Phenotyping - WordPress.com

2.1a Development of an integrated

remote sensing platform

Tools to shift the resolution of forest assessment from the

stand to the sub-stand level – “Precision forestry”

Building on the success of the FFR programme

Consolidate expertise in LiDAR and image analysis

Evaluate and integrate new technologies

Page 3: Remote Sensing for Phenotyping - WordPress.com

What is phenotyping?

Genotype

The genetic blueprint carried in DNA

Phenotype

The interaction between an organism’s

genes and its environment.

Phenotyping

The assessment of plant traits such as

growth, architecture, and wood

properties.

Page 4: Remote Sensing for Phenotyping - WordPress.com

Integrated remote sensing platform

A range of technologies and

techniques that will allow us to

phenotype trees remotely.

Recent technological advances

have created a wide range of

powerful data sources

Page 5: Remote Sensing for Phenotyping - WordPress.com

Technologies

Aerial LiDAR

Terrestrial LiDAR

Leaf Area Index

Multi- and hyper- spectral imagery

Page 6: Remote Sensing for Phenotyping - WordPress.com

Platforms

Aircraft

Satellite

Terrestrial

UAV

Page 7: Remote Sensing for Phenotyping - WordPress.com

Satellite - Detection of Growth Irregularities

Variation (plus

and minus) from

expected

productivity can

be quantified

and mapped

Page 8: Remote Sensing for Phenotyping - WordPress.com

Aerial LiDAR

Laser scans a swath and records

returns from the ground or trees

250,000 pulses per second

Multiple returns per pulse

Every return is recorded as a point

in 3D

Page 9: Remote Sensing for Phenotyping - WordPress.com

Aerial LiDAR

Point cloud used to estimate and map

key forest measures

Stocking

Top Height

Basal Area

AGE 9 STAND

Page 10: Remote Sensing for Phenotyping - WordPress.com

Aerial LiDAR – Tree Level Analysis

2D and 3D crown metrics

Direct measurement of

tree metrics in the upper

canopy

Models linking crown

metrics to variables of

interest to phenotyping

• Height

• Diameter

• Stiffness

Page 11: Remote Sensing for Phenotyping - WordPress.com

Aerial LiDAR – Tree Level Analysis

Models linking crown

metrics to variables of

interest to phenotyping

• Height

• Diameter

• Stiffness

Page 12: Remote Sensing for Phenotyping - WordPress.com

TLS scanners - FARO

• Trimble TX5 3D scanner

(Faro Focus 3D 120)

• 360° hemispherical scan

with colour images

• 244K points per second

• 2mm accuracy

• Weight 5kg

• Cost $80K ?

• 15 minutes per scan, 3

scans per plot

Page 13: Remote Sensing for Phenotyping - WordPress.com

Point cloud - FARO

Page 14: Remote Sensing for Phenotyping - WordPress.com

TLS scanners – ZEB1

• ZEB1 3D scanner

• 360° hemispherical scan

• 43K points per second

• 30mm accuracy

• Weight <1kg

• Cost $27K

• 5-10 minutes per plot

Page 15: Remote Sensing for Phenotyping - WordPress.com

Point cloud – ZEB1

Page 16: Remote Sensing for Phenotyping - WordPress.com

TLS – estimation of diameter

Page 17: Remote Sensing for Phenotyping - WordPress.com

Phenotyping platform development

Combine data from multiple

sources and use to estimate

key metrics

Height;

Tree position;

DBH;

Pruned height;

Stem form;

Branchiness;

Wood properties;

Foliar health.

Objective measures

Page 18: Remote Sensing for Phenotyping - WordPress.com

One year in, work completed

Phenotyping cluster group formed

Experimental framework developed

Acquired satellite and aerial terrestrial

LiDAR data sets

Conventional phenotyping, terrestrial

LiDAR scans in genetics trials and

inventory plots

LAI field work completed

Analyses commenced

Page 19: Remote Sensing for Phenotyping - WordPress.com

Looking forward…

Develop and test models for tree phenotyping

Develop maps of productivity and variation at forest,

stand and tree levels.

Combine with existing data sets

soils, climate, stand records, trial data

to create a phenotyping platform to unravel the

interactions of genetics, site and silviculture

Combining whole-of-forest knowledge with new

genetic analyses and technologies will provide the

step-change in gain delivery for forestry

Page 20: Remote Sensing for Phenotyping - WordPress.com

www.research.nzfoa.org.nz

www.scionresearch/gcff

David Pont

Research Leader

[email protected]

20/10/2014