1. Validation of Landsat Time-Series of
Persistent Green-Vegetation
Fraction for Australia
Presentation by: Kasper Johansen1,4, Tony Gill2,4, Rebecca
Trevithick3, John Armston3,4, Peter Scarth3,4, Neil Flood4, Stuart
Phinn1,4
1The University of Queensland (k.johansen@uq.edu.au)
2 NSW Office of Environment and Heritage, Department of Premier and Cabinet
3 Queensland Department of Science, Information Technology, Innovation and the Arts
4 Joint Remote Sensing Research Program

2. Outline
• Introduction: AusCover Activities and Products
• National Persistent Green Vegetation Fraction
• Objectives
• Methods
• Results
• Validation
• Main Use of Product
• Conclusions and Potential Future Work

3. AusCover Activities and Products

4. Field Campaigns

5. Airborne Campaigns

6. AusCover field and airborne campaigns
Field-based Measurements Airborne Measurements Satellite Based Measurements
Time-Series Measurements

7. AusCover Products
• The vertically-projected fraction of long-term, persistent green
vegetation (nominally woody vegetation) cover
• Common essential variable for ecological and ecosystem models
of vegetation structure and dynamics

8. National Landsat-based Persistent
Green Vegetation Fraction
Objective: to produce a calibrated and validated Landsat based
Persistent Green Vegetation (PGV) Fraction map based on a 2000
to 2010 time-series of the whole of Australia
• Fully automated model
• Downloaded >4000 Landsat images from USGS Earth Explorer
• Selection process: cloud cover, driest time of year, sun
elevation, anniversary dates, TM and ETM+ SLC-on
• Processing stream also produces time-series fractional cover and
water masks

9. Persistent Green Vegetation Fraction -
Methods
Calibrated Normalised Masks Modelling/
radiance time reflectance calibration
series
Modelling/ Fractional cover Masked green Persistent
calibration cover green-veg
fraction
• Pre-processing of data to BRDF/topographically corrected reflectance.
• Masking (cloud, cloud shadow, snow, topographic shadow, high incidence
angle, water)
• Unmixing algorithm and field data to create fractional cover images
(green, non-green, bare)
• Time-series algorithm, statistics and field data to classify persistent-green
vegetation and its fractional cover
• LiDAR data used to validate persistent-green vegetation fraction

10. Persistent Green Vegetation Fraction –
Pre-Processing
• At-sensor
radiance

11. Persistent Green Vegetation Fraction –
Pre-Processing
• Standardised
reflectance
• Topographic
correction
• BRDF
correction

12. Persistent Green Vegetation Fraction -
Masks
• Cloud and cloud shadow mask based on
published algorithm (Fmask): Zhu, Z. and
Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in
Landsat imagery. Remote Sensing of Environment 118 (2012) 83-94.
• Water mask based on discriminant
analysis: Danaher, C. Collett, L (2006). Development, optimisation
and multi-temporal application of a simple Landsat based water index, 13th
ARSPC, Canberra.
• Topographic shadow mask
• High incidence angle mask (> 80 degrees)
• Not perfect, so robust statistical methods
required to account for outliers in
time-series e.g. due to misclassified
cloud

13. Persistent Green Vegetation Fraction –
Fractional Cover Time-Series
• Fractional cover uses a constrained
unmixing model with endmembers
derived from field sampling.
• Creates an image with the percentage
Bare, Green and non-green fractions
• Field data from 800 sites collected
using consistent, nationally agreed
protocol
• Overall RMSE of 11%
Green
Green Non-green Bare ground
Bare Non-green

14. Persistent Green Vegetation Fraction -
Classification and Prediction
• Training data obtained from a range of sources
• Approximately 5100 sites of which 3800 are persistent green
• Decision tree classifier based on robust regression statistics used to
classify each pixel as persistent or non-persistent green vegetation
• Robust regression statistics used to predict the persistent green fraction
SOURCE DESCRIPTION
QLD DSITIA Fractional-cover field sites
ABARES Fractional-cover field sites
NSW OEH Image-interpretation (SPOT-
5/Google Earth) of woody/not-
woody vegetation cover
NT Bushfires DBH field sites
NT NRETAS Fractional-cover field sites
ACRIS Locations of low-foliage scrub
Persistent green WA Woody-vegetation sampling
Not persistent green sites
QLD Biomass field sites
Herbarium

15. Classification and Prediction
1 • Persistent green areas show
0.9
0.8
low variation in green fraction
green fraction
0.7 over time, and a minimum
0.6
0.5 above a threshold.
0.4
0.3 • Robust regression fit to time-
0.2
0.1
series of green fraction for use
0
0 1000 2000 3000 4000
in the classification of
day persistent and non-persistent
green vegetation.
max max
min
min Not PGV
mask mask
Variation in time-series Minimum fraction in time-series Persistent green fraction

16. Persistent Green Vegetation Fraction
max
min
Non-PGV
mask

17. Persistent Green Vegetation Fraction
max
min
Non-PGV
mask

18. Persistent Green Vegetation Fraction
max
min
Non-PGV
mask

19. Persistent Green Vegetation Fraction
max
min
Non-PGV
mask

20. Persistent Green Vegetation Fraction
max
min
Non-PGV
mask
http://tern-auscover.science.uq.edu.au/thredds/catalog/
auscover/persistentgreen/persistentGreen/catalog.html

21. Persistent Green Vegetation Fraction -
Validation
• Accuracy statistics for persistent/non-persistent green vegetation
classification
• Persistent green vegetation fraction estimates compared to
field-observed woody foliage cover measurements (SLATS star
transects)
Non-persistent Persistent
Non- 878 440
persistent
Persistent 457 3366
Overall accuracy 0.826
Non-persistent producer’s accuracy 0.658
r2: 0.859
Non-persistent user’s accuracy 0.666 Slope: 0.928
Persistent producer’s accuracy 0.884 Intercept: 0.005
Persistent user’s accuracy 0.880

22. Persistent Green Vegetation Fraction –
Airborne LiDAR Validation
• Collation of Riegl LMS-Q560 and Riegl LMS-Q680i waveform LiDAR datasets
captured within the temporal extent of the product (2000-2010)
• Woody Foliage Projective Cover estimates from field calibration of LiDAR Pgap
• Comparison with Landsat persistent green extent and cover fractions

23. Main Uses of PGV Map
Main use would be for:
• Determining (1) Wooded Extent; (2) Forest Extent; (3) Forest
Density/Forest Crown Cover/Foliage Cover; (4) Rangeland Extent
• Correcting fractional cover to ground cover
• Evaluate the effectiveness of management activities
More experimental use:
• Carbon Applications – Basal Area
• Support land-cover/land use/biodiversity/carbon mapping
• Greenness trends in regions
• Mapping water bodies across the landscape
• Mapping vegetation connectivity across the landscape

24. Main Uses of PVG Map

25. Future Work & Conclusions
Future Work
• Additional USGS imagery back to 1986 will allow a longer time-series
to be used, improving accuracy
• Use of all images in the time-series will allow better discrimination of
the persistent green fraction and may enable detection of woody
thickening.
Conclusions
• Produced nationally consistent calibrated and validated map of persistent
green vegetation fraction at Landsat scale
• Data and metadata are freely accessible through the TERN Data Discovery
Portal
• Working with state and federal government agencies and researchers
associated with AusCover and TERN enabled this work

26. Acknowledgements
AGENCY PEOPLE
ABARES Jasmine Rickards
NT Bushfires Andrew Edwards
NT NRETAS Nick Cuff
ACRIS / CSIRO Gary Bastin
WA DEC Graeme Behn
Airborne Research Australia Jorg Hacker
Monash Jason Beringer
CDU Stefan Maier
QLD Herbarium
NSW Office of Environment and Heritage Tim Danaher

27. Validation of Landsat Time-Series of
Persistent Green-Vegetation
Fraction for Australia
Presentation by: Kasper Johansen1,4, Tony Gill2,4, Rebecca
Trevithick3, John Armston3,4, Peter Scarth3,4, Neil Flood4, Stuart
Phinn1,4
1The University of Queensland (k.johansen@uq.edu.au)
2 NSW Office of Environment and Heritage, Department of Premier and Cabinet
3 Queensland Department of Science, Information Technology, Innovation and the Arts
4 Joint Remote Sensing Research Program