Vegetation structure mapping
with airborne and ground-based
laser scanning to advance forest
fire research
Suzanne Marselis1,2,3
June 11th, 2014
Prof. Dr. Albert van Dijk1,2
Dr. Marta Yebra1,2
Tom Jovanovic2
Dr. Harry Seijmonsbergen3
1: Australian National University 2: CSIRO 3: University of Amsterdam

Acknowledgements
• Bushfire and Natural Hazards CRC
• ACT Parks and Conservation Service
• Earth Observation and Informatics Transformational Capability
Platform (CSIRO)
• Terrestrial Ecosystem Research Network (TERN)

Content
• Introduction
• Aim of research
• Airborne LiDAR
• Limitation of Airborne LiDAR
• Ground-based LiDAR opportunities
• Summary
• Recommendations for forest fire research

Bunyip State Forest, Victoria, 7 February 2009
© AAP 2009

Introduction
• Need for monitoring
• Two important aspects
• Fuel flammability
• Fuel load
• Problem: Field fuel assessments can be
• Time consuming
• Costly
• Slightly subjective
• Solution: Remote sensing?
Phil Zylstra & Marta Yebra, January & April 2014

Aim of my research
• Study the potential of using remote sensing data to map forest
structural characteristics that describe the fuel load.

Project Vesta Fuel assessment
Forest
Surface
Near-
surface
Elevated
Canopy
Continuity of litter: LiDAR
Available fuel: LiDAR
Amount of decomposition
Continuity of fuel
Proportion of dead material
Percentage cover
Amount of fuel (t/ha)
Continuity of fuel
Amount of fuel (t/ha)
Fraction of dead material
Type of bark based on tree species
Canopy cover
Canopy height
Assigning hazard
scores
Information needed
for fuel hazard scores
Division in layers
SF.FHS
SF.depth.mm
EF.FHS
NSF.ht.cm
NSF.FHS
EF.ht.cm
BK.FHS
Canopy.PC
Canopy.ht.m

Remote sensing
• Any data collected from a distance
• Active and Passive remote sensing
• Optical - Hyperspectral
• Light Detection and Ranging (LiDAR)
Aranxta Cabello-Leblich, June 2014
Hyperspectral data
for Black Mountain,
collected March 2014

Light Detection and Ranging (LiDAR)
• Airborne LiDAR
• Point cloud
Airborne LiDAR data (Source: Blair et al. 1999)
Full-waveform LiDAR signal Source: Wagner et al. 2008
p1
p2
p3

LiDAR
LiDAR point cloud for 1 isolated tree
LiDAR point cloud for Black Mountain Nature Reserve
• Point cloud
• x,y,z value

LiDAR – 2 datasets
• Research areas
• Black Mountain Nature Reserve
• Mulligans Flat Nature Reserve
• Point cloud: height classification
• Ground
• Understory (z < 0.3 meter, noise?)
• Midstory (0.3 < z < 2 meter)
• Canopy (z > 2 meter)
Black Mountain
Mulligans Flat

Tree dimensions
• Isolated trees on Mulligans Flat

Tree dimensions
R² = 0.8889
0
5
10
15
20
25
30
0 10 20 30 40
LiDARcalculatedtopheight(meter)
Field measured top height (meter)
Canopy Top Height
Individual trees
R² = 0.7034
0
2
4
6
8
10
12
0 2 4 6 8 10 12 14
LiDARcalculatedbaseheight(meter)
Field measured base height (meter)
Canopy Base Height
Individual trees
Source: Wagner et al. 2008

Spatial Maps – Black Mountain
• Canopy height
• Canopy base height
• Canopy cover

> 20
> 20

Canopy cover
Canopy cover, Black Mountain

How about understory and midstory?
Adam Leavesley, March 2014

Source: Wagner et al. 2008

Limitations
• It seems to work …
• But can we actually ground-truth this?
• Required:
• High resolution, reliable understory information
• Is this possible?
YES!

Ground-based LiDAR - Zebedee
Tom Jovanovic (CSIRO) preparing the Zebedee for data collection

Data collection in Mulligans Flat

Result: in 15 minutes a floating point cloud

Data collection in Mulligans Flat
• 3 field sites

Zebedee data ‘floating in space’
Airborne LiDAR data, plot Tom Un-georeferenced Zebedee LiDAR data, plot Tom

Georeferencing Zebedee point cloud
Rotation

Matching two datasets
Airborne Ground-based Merged

Compare Zebedee with Airborne LiDAR
• Create same classification for Zebedee
• Ground
• Understory (z < 0.3 meter, noise?)
• Midstory (0.3 < z < 2 meter)
• Canopy (z > 2 meter)
Zebedee dataset, classified in three classes based on heights

Understory presence: z< 0.3 meter
PLOT 1
Airborne Airborne
0 1 Total
Zebedee 0 1336 97 1433
Zebedee 1 463 220 683
Total 1799 317 2116
PLOT 2
Airborne Airborne
0 1 Total
Zebedee 0 577 131 708
Zebedee 1 720 563 1283
Total 1297 694 1991
PLOT TOM
Airborne Airborne
0 1 Total
Zebedee 0 1028 202 1230
Zebedee 1 2354 640 2994
Total 3382 842 4224
Zebedee Airborne
Omission error Commission error

Midstory presence: 0.3 < z < 2 meter
PLOT 1
Airborne Airborne
0 1 Total
Zebedee 0 1524 2 1526
Zebedee 1 532 58 590
Total 2056 60 2116
PLOT 2
Airborne Airborne
0 1 Total
Zebedee 0 1179 3 1182
Zebedee 1 789 20 809
Total 1968 23 1991
PLOT TOM
Airborne Airborne
0 1 Total
Zebedee 0 1476 6 1482
Zebedee 1 2481 261 2742
Total 3957 267 4224
Zebedee Airborne
Omission error Commission error

What else can we do with Zebedee data?
• Interpolate tree heights, 1x1 meter resolution
Airborne LiDAR Zebedee LiDAR

Height difference
Airborne - Zebedee
= height difference
meter
meter
Reclassified
Height
Difference
(meter)

Height difference (meter)
Zebedee point density

Calculate canopy cover
Zebedee Airborne
Plot nr. R2 R2 –
restriction*
Plot 1 0.438 0.851
Plot 2 0.143 0.557
Plot Tom 0.368 0.649
- Fractional cover, 1x1 meter resolution
- Airborne: straightforward
- Zebedee: occupied grid cells within larger grid cell
*Only cells with more than 20 Zebedee points included in analyses

Calculate DBH – Slice at 1.3 – 1.35 meter
Slice
Raw slice
Selection of the stems

Automating this processing?
• Need for good classification
Height classification Understory, midstory & canopy Understory, midstory, canopy & stem
Application in different area

Calculating grass volumes
The total volume of grass: 33.12 m3
Area: 234 m2
Average volume: 0.14 m3/m2.

Summary of findings
Dataset Pro’s Con’s
Airborne
- Covers large areas
- Canopy height
- Canopy base height
- Canopy cover
- Applicability for
understory/midstory
evaluations
Zebedee
- Easy data collection
- Understory volume
- Shrub dimensions
- DBH calculations
- Processing times
- Algorithm availability
- Small-scale

Recommendations for fire research
• Depending on the needs it would be better to invest in either:
• Airborne LiDAR data collection to large areas
• Research on using Zebedee data and data sampling

Thank you
• For having me at ANU
• For all the assistance
• For the funds
• And for listening to my story – I hope you enjoyed
• I definitely did!

Questions?
Mourad Bandjee, 2014
Questions?

Clumping

Automating stem extraction
18 out of 29 stems = 62.07 %.

Automating stem extraction
clustering of 14/18 = 77.78 % of the stems

Automating stem extraction
R² = 0.9483
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
CalculatedDBH
Measured DBH
Correlation between Calculated & measured DBH

DBH frequency distributions

Calculating shrub & grass dimensions
Measure Field –
Measured
LiDAR -
Calculated
Error
(meter)
Grass1
NZ 0.45 0.82 -0.37
EW 0.55 0.72 -0.17
Height 0.45 0.4 0.05
Grass2
NZ 0.8 0.85 -0.05
EW 0.85 0.88 -0.03
Height 0.5 0.675 -0.18
Shrub1 NZ 1.2 1.1418 0.06
EW 1.44 1.199 0.24
Height 2.5 2.2596 0.24
Shrub
3
NZ 1.2 0.7868 0.41
EW 1.18 1.1169 0.06
Height 1.45 1.4114 0.04
Shrub
4
NZ 1.4 1.1158 0.28
EW 1.4 1.1744 0.23
Height 1.8 1.7294 0.07

Canopy cover & height error

Biomass estimations
• Tree recognition
• DBH calculations
• Height calculations
• Allometric equation: Calculate biomass.