Assessing the three dimensional vegetation structure is important in fire management. Manually mapping forest structural characteristics is time consuming and hence expensive and automated methods should prove beneficial. In this research I investigated the use of airborne light detection and ranging (LiDAR) for mapping vegetation height and canopy cover and to derive information on the understory. Airborne LiDAR data provided good quality information on both vegetation height and canopy cover, but understory information was more uncertain. The use of automated hand-held LiDAR data collection to obtain information on the understory and to complement the airborne LiDAR data was investigated and looks to have strong potential.
Marselis 2014 Vegetation Structure mapping with LiDAR for forest fire research
1. 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
2. Acknowledgements
• Bushfire and Natural Hazards CRC
• ACT Parks and Conservation Service
• Earth Observation and Informatics Transformational Capability
Platform (CSIRO)
• Terrestrial Ecosystem Research Network (TERN)
3. Content
• Introduction
• Aim of research
• Airborne LiDAR
• Limitation of Airborne LiDAR
• Ground-based LiDAR opportunities
• Summary
• Recommendations for forest fire research
5. 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
6. Aim of my research
• Study the potential of using remote sensing data to map forest
structural characteristics that describe the fuel load.
7. 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
8. 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
9. 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
10. LiDAR
LiDAR point cloud for 1 isolated tree
LiDAR point cloud for Black Mountain Nature Reserve
• Point cloud
• x,y,z value
11. 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
22. Limitations
• It seems to work …
• But can we actually ground-truth this?
• Required:
• High resolution, reliable understory information
• Is this possible?
YES!
23. Ground-based LiDAR - Zebedee
Tom Jovanovic (CSIRO) preparing the Zebedee for data collection
30. 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
31. 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
32. 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
33. What else can we do with Zebedee data?
• Interpolate tree heights, 1x1 meter resolution
Airborne LiDAR Zebedee LiDAR
36. 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
37. Calculate DBH – Slice at 1.3 – 1.35 meter
Slice
Raw slice
Selection of the stems
38.
39. Automating this processing?
• Need for good classification
Height classification Understory, midstory & canopy Understory, midstory, canopy & stem
Application in different area
41. 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
42. 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
43. 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!
Albert: What picture of field assessment doe you suggest? / have ?
False color – R: infra-red G: green B: blue
I agree … the colours on the scale bare are not so well visible… hmm.. You want me to change that colorbar? I think I can do that. But I am not sure I I can get the exact same one back.
Botanical gardens and powerline clearing
south facing gullies -> less evapotranspiration, so better growth.
and also botanical gardens very clear, inc. rain froest gully
and also botanical gardens very clear, inc. rain froest gully
Albert: a picture of what?
Commission (red) and omission (yellow) errors. What is what again? – it also hurts my eyes but I can’t really help it.
This is the pearson correlation coefficient. Is that wrong?