A review based on current literature

Applications and prospects of

terrestrial LiDAR and drones for an

improved forest inventory

Erich Seifert

Stefan Seifert

Anton Kunneke

David M Drew

Jan van Aardt

Thomas Seifert

Precision Forestry Symposium 2017 — Stellenbosch — 28th February

Bibliometrics

Publications over time

20162015201420132012201120102009200820072006200520042003200220012000199919981997199619951994

0

10

20

30

40

50

60

70

80

Drones Terrestrial LiDAR

Year

Nu

mb

er

of

pap

ers

Data source: Scopus search for

( "terrestrial laser scanning" OR "terrestrial LiDAR" OR "t-LiDAR" ) AND ( "forest" OR "forestry" )

( "UAV" OR "drone" OR "umanned aerial") AND ( ( forest OR tree ) AND measurement OR forestry )

Precision Forestry Symposium 2017 — Stellenbosch — 28th February

Bibliometrics

Publications per country

United States

China

Spain

Australia

Germany

Canada

Brazil

Finland

France

South Korea

0 10 20 30 40 50 60 70 80

Number of publmcatmons

United States

China

Germany

Finland

Canada

Australia

United Kingdom

France

Netherlands

Austria

0 10 20 30 40 50 60 70 80

Number of publmcatmons

Terrestrial LiDAR Drones

Data source: Scopus

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 4

Terrestrial LiDAR

Technology

Source: Stefan Seifert Source: Zoller+Fröhlich GmbH

Time-of-flight (pulse-based) Phase-shift

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 5

Terrestrial LiDAR

Technology

Source: Dassot et al. 2016

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 6

Terrestrial LiDAR

Technology

Source: Anton Kunneke

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 7

Terrestrial LiDAR

Development of hardware

2002 2004 2006 2008 2010 2012 2014 20160

20

40

60

80

100

120

Year

Prm

ce

[1

,00

0 €

]

2002 2004 2006 2008 2010 2012 2014 20160.0

5.0

10.0

15.0

20.0

25.0

Year

Ac

cu

rac

y a

t 5

0m

[m

m]

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 8

Terrestrial LiDAR

Development of hardware

2002 2004 2006 2008 2010 2012 2014 20160

200

400

600

800

1000

1200

Year

Sp

ee

d [

1,0

00

po

mnts

/s]

2002 2004 2006 2008 2010 2012 2014 20160

5

10

15

20

25

30

35

Year

We

mgh

t [k

g]

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 9

Drones

Technology

Fixed-wing Multicopter

Source: senseFly SA Source: DJI

Source: multikopter.ccSource: QuestUAV

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 10

Drones

Technology

Fixed-wing Multicopter

Take-off and

landing area

Large

e.g. 20 m × 15 m

Very small

vertical take-off/ landing

Cruise speed High

40 – 90 km/h

Low – Medium

15 – 50 km/h

Maximum flight time High

45 – 60 minutes

Low

15 – 20 minutes

Maximum coverage

(single flight)

High

~ 12 km²

Low

~ 0.3 km²

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 11

Drones

Flight planning

Source: SPH Engineering

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 12

Drones

Multiple View Geometry

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 13

Drones

Multiple View Geometry

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 14

Forest inventory

Traditional inventory

Measured values

DBH

Height

Number of trees

Species

Position

Secondary target variables

Taper and volume

Biomass

Log assortments

f (DBH, h)

⏟

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 15

Forest inventory

Additional information from Terrestrial LiDAR

Source: Dassot et al. 2016

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 16

Forest inventory

Additional information from Terrestrial LiDAR

Source: Dassot et al. 2016

Bark texture analysis

● Tree species recognition● Detection of external wood defects

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 17

Forest inventory

Additional information from Terrestrial LiDAR

Bark texture analysis

Source: Othmani et al. 2013

hornoeam oan spruce oeech pine

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 18

Forest inventory

Additional information from Terrestrial LiDAR

Source: Dassot et al. 2016

Bark texture analysis

● Tree species recognition● Detection of external wood defects

Geometrical fitting

● Diameters (DBH, stem and branch profiling)● Wood volumes / Stand value● Stem density / Basal area● Tree height

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 19

Forest inventory

Additional information from Terrestrial LiDAR

Geometrical fitting

Source: Raumonen et al. 2011Source: Hackenberg et al. 2014

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 20

Forest inventory

Additional information from Terrestrial LiDAR

Source: Dassot et al. 2016

Bark texture analysis

● Tree species recognition● Detection of external wood defects

Geometrical fitting

● Diameters (DBH, stem and branch profiling)● Wood volumes / Stand value● Stem density / Basal area● Tree height

Plot cartography

● Digital Terrain Model● Stem detection and location

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 21

Forestry

Usage of drones

Individual trees / stems

Multicopter, 45° angle, MVG reconstruction,

Comparison with TLS

Source: Fritz et al. 2013

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 22

Forestry

Usage of drones

Tree height

Fixed-wing drone

Reconstruction with MVG

Comparison with TLS data

Source: Lisein et al. 2013

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 23

Forestry

Usage of drones

Spatial gaps in forests

Fixed-wing drone

10 ha area

Source: Getzin et al. 2014

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 24

Drone point clouds

Airborne LiDAR vs. Digital Aerial Photogrammetry

Source: Wallace et al. 2016

Airborne LiDAR Photogrammetric reconstruction

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 25

Conclusion

● Terrestrial LiDAR can provide detailed below-canopy information,

but it requires intensive field work, so it cannot be applied to large areas

● Drones can provide improved above-canopy and forest structure

information at a fraction of the cost of terrestrial LiDAR. However, it does

not reach the same level of accuracy and detail.

● Both technologies have considerably matured in the past decade and have

reached a technological level that warrants their use for forest inventory

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 26

Outlook

● Drones fly in the forest

● SLAM:

Simultaneous Localization

and Mapping

● Swarm intelligence

● Machine learning

● …

Source: Astec

Precision Forestry Symposium 2017 — Stellenbosch — 28th February 27

Acknowledgements

Stefan Seifert

Scientes Mondium

Anton Kunneke

Stellenbosch University

David M. Drew

Stellenbosch University

Jan van Aardt

RIT

Thomas Seifert

Scientes Mondium,

Stellenbosch University

Co-authors

Funding