Use of remote sensing in research and industry:

an international perspective Michael Watt

Contributors - Erik Næsset – Norway, Matti Maltamo – Finland, Håkan Olsson

–Sweden, Mike Wulder – Canada, Ron McRoberts – USA, Terje Gobakken –

Norway, Russell Turner – Australia

.

Presentation Overview

• Overview of LiDAR and satellite imagery

• What is remote sensing being used for?

• Cost-benefit analyses

• Future applications

Light detection and ranging (LiDAR)

Active remote sensing technology

LiDAR emits ~ 200,000

pulses per second

Records up to 4 returns per pulse

Each return is converted to 3D point

1. Direct measurements Canopy height

2. Derived Canopy structure/density

3. Topographic measures

LiDAR - Trends

• Systems progressively improved from

dual return to multi-return with increased

laser strength and pulse densities.

• Lower point densities being flown

• Operationally used for inventory

purposes worldwide – wall to wall and

strip sampling

• Value recognised for terrain

characterisation

• Research focus moving towards

evaluating wood properties, LAI,

waveform LiDAR, single tree

delineation and modelling processes.

Mapping Applications

Source: Mark Forward. Nelson Forests Ltd.

Digital Terrain Models

• One of the most widely

used applications of LiDAR

• Can be used for :

Harvest planning

Road planning

Machine slope classing

ID hazards, e.g. escarpments

Erosion mapping

Source: Mark Forward. Nelson Forests Ltd.

Inventory

• Link LiDAR metrics to stand

dimensions

• Used to develop spatial maps

LiDAR mean height (m)

0 10 20 30 40

Tota

l ste

m v

olu

me (

m3 h

a-1

)

0

200

400

600

800

1000

1200

LiDAR 95th height percentile

0 10 20 30 40 50

Me

an t

op

he

ight

(m)

0

10

20

30

40

50

Source: Mark Forward. Nelson Forests Ltd.

Single tree delineation

Where are Lasers used Experimentally or Commercially in Forest Inventory

EUROPE AMERICA ASIA OCEANIA AFRICA

Finland Canada Japan Australia South Africa

Norway USA Nepal

New Zealand

Sweden Chile China

Austria Brazil

The Baltic States Uruguay

Bulgaria

Denmark

Germany

Poland

Portugal

Spain

Switzerland

UK

Italy

Extent of Use

• Operational stand based inventories have been

conducted in Norway, Finland and Sweden since

2002–2004.

• 2 Million ha inventoried in Finland

Stand management system developed 2005-10

LiDAR is about 1 pulse/m2.

• Surveying companies acquire LiDAR data and deliver

outputs describing forest inventory products.

• Stand metrics derived from LiDAR (0.5 – 1.0 pulse /m2)

for all forestry land in Sweden (23 Million ha)

Timeline to Implementation – Nordic Countries

Extent of Use – Australia

• Queensland, flood risk, coastal forests

• NSW, used operationally in

ca. 300,000 ha forest

Almost 1 M ha LiDAR coverage

• South Australia, ForestrySA

operationalised 2007

• Tasmania, FT undertook operational

trial 2007 by ‘double planning’ coupes

Operationalised 2009

Currently 700,000 ha LiDAR coverage

14/52

2010 National

Lidar Transects 25,000 km

Lidar metrics gridded to

25 x 25 m cells

> 20 Million lidar-plots

Source: Wulder, M.A.,

Canadian Forest Service

Volume of Forest Stands Costs & Error (precision): 2001

050

100150200250300350400450500

10 12 14 16 18 20 22

Co

sts

(U

SD

pe

r h

a)

Error (%)

Method 3

Method 1 Method 2

Laser

Method 1: Relascope plots

Method 2: Photogrammetry

Method 3: 15 sample plots per stand

Cost / Benefit Analysis

Early work demonstrated that LiDAR-assisted inventories were cost-effective

when compared to alternative methods

Forest inventory approaches – Case Study

(based on airborne laser scanning)

Source: Gobakken et al., 2012

Study area and datasets

• Field measurements

2407 trees measured

23 sample plots

• Airborne laser scanning

Optech ALTM 3100 EA

Source: Gobakken et al., 2012

Diameter distribution

18

Conclusions

• The value of the inventory information depends of what

the data are going to be used for.

• Analysis showed the prediction of diameter distributions

from LiDAR (ABA-DD) was the most cost effective

inventory method

• Further studies are needed to verify the results.

19

Satellite imagery

Satellite imagery - Trends

Typical Resource Applications

• Resource updates

• Growth variation mapping

• Repeat monitoring

• Image costs have dropped

• Data is available and frequent

• Resolution is appropriate for planning

• Fits into existing GIS systems

• Easy to implement

Landsat 30 m

High resolution 5 m

Three Levels: that interlink together to cover the rotation

1. Strategic Planning 2. Operational Planning 3. Precision Forestry (LiDAR) UAV?

5 m imagery Targeted coverage based on 1

Information Linkages

Increased Cost

Decreased update frequency

Approximate Data Costs vs. Detail

RapidEye – 5 m resolution

Wide area, daily imaging, data cost low. Ideal

for frequent monitoring & targeting use of

other options i.e. Level 2

Digital Globe – or aerial Imaging

Smaller area, 3-5 day imaging.

Ideal for mapping updates

LiDAR & CIR photography

Level 1: Strategic Planning

Level 2: Operational Planning

Level 3: Precision Forestry

1 cent / ha

33 cents/ ha tasked

16-19 cents/ ha archived

• Strategic use of different systems that result in cost savings through better information.

• Cost of the option. The decision maker should not pay for information that is more expensive

than the expected improvement in the value of the decision.

Data cost (USD)

~$ 4-10 /ha

Level

UAV?

Applications Satellite Imagery

Complements other technologies by identify areas where further

information is required.

• Plantation establishment success – i.e. has the crop established

• Variation in growth across plantations

• Monitoring of change caused by – i.e. fire, theft, termites, wind

damage or foliage disorders

• Evaluate progress of planned silvicultural operations and

harvesting

• Provides a cost-effective way of updating plantation mapping

Level 1: Strategic Planning – providing a cost effective overview

Overview

Data cost low at 1 cent/ha

• Highlights areas that

require further

attention. Due to fire,

disease,

encroachment or

plantation variation or

failure

• Enables long term

repeat monitoring

once GIS boundaries

are up-to-date

Disease Crop variation Fire

Failed areas

Examples: NZ, Aust. Brazil and China

Source: Watt P.J Indufor 2013

Detection of Harvesting and Thinning

The following slide transition shows harvesting and thinning operations as detected The first image is Jan 2012 and the second May 2012. From the images harvest progression is able to be monitored and estimated over this period.

P. radiata Australia

Source: Watt P.J Indufor 2012

Stand variation routine applied to

2 m DigitalGlobe imagery.

What does it provide?

• Semi-automated mapping of

plantation gaps and areas of

poor performance.

• Allows for improved

stratification

• Improves stocked area

estimates which lead to

improved woodflow / yield

projections.

Mapping Variation

Rubber Cambodia

Source: Watt P.J Indufor 2013

UAV – 90 day Monitoring Plantation Establishment

• Individual Tree identification

• Identifying weed areas

S. America Eucalypt plantation

Source: Watt P.J Indufor 2014

Using Sensefly UAV fitted with camera (5-7 cm) pixels. Routine used to firstly map canopies and then plot tree locations.

New Zealand Industry Where are we at? • Strong link between research and industry uptake

• LiDAR • First trialled MfE 2005, Ernslaw 2006

• Used operationally 2007 MfE for carbon

• 2012 onwards for inventory, Blakely Pacific, Nelson Forests, KT

• Road and harvest planning

• LiDAR-assisted inventories

• Satellite Imagery Mostly only trialled in NZ

• Mapping variability

• Forest changes – GIS quality checks, harvesting, large-scale

events

• Needle cast detection (proof of concept)

• Multispectral LiDAR

Colour LiDAR

Vertical profiles of indices

True LAI vs plant LAI

• Terrestrial Laser Scanning

Technology behind airborne LiDAR in terms of

integration

Portable instruments, hyperspectral, dual wavelength

Integration with aerial LiDAR – 3D forests

Future research directions

• Aerially derived Digital Surface Model

Require initial DTM from LiDAR

• Less expensive if aerial imagery for other purposes can

be used

Future research directions

• Unmanned Aerial Vehicles (UAV)

Sensor improvements, flight times increased and detection

routines improved

• Wood properties from LiDAR

• Linking information from harvesters to remotely sensed

data

Future research directions

Acknowledgements

• Pete Watt – New Zealand

• Erik Næsset – Norway

• Matti Maltamo – Finland

• Håkan Olsson – Sweden

• Mike Wulder – Canada

• Ron McRoberts – USA

• Terje Gobakken – Norway

• Russell Turner – Australia

http://www.scionresearch.com/gcff

Michael Watt

Research Leader – Forest Operations and Monitoring, Scion

michael.watt@scionresearch.com