Validation of Landsat Time-Series ofPersistent Green-VegetationFraction for AustraliaPresentation by: Kasper Johansen1,4, ...
Outline• Introduction: AusCover Activities and Products• National Persistent Green Vegetation Fraction   • Objectives   • ...
AusCover Activities and Products
Field Campaigns
Airborne Campaigns
AusCover field and airborne campaignsField-based Measurements   Airborne Measurements   Satellite Based Measurements      ...
AusCover Products• The vertically-projected fraction of long-term, persistent green  vegetation (nominally woody vegetatio...
National Landsat-based Persistent        Green Vegetation FractionObjective: to produce a calibrated and validated Landsat...
Persistent Green Vegetation Fraction -                  Methods   Calibrated         Normalised          Masks            ...
Persistent Green Vegetation Fraction –              Pre-Processing• At-sensor  radiance
Persistent Green Vegetation Fraction –               Pre-Processing• Standardised  reflectance• Topographic  correction• B...
Persistent Green Vegetation Fraction -               Masks               • Cloud and cloud shadow mask based on           ...
Persistent Green Vegetation Fraction –       Fractional Cover Time-Series• Fractional cover uses a constrained  unmixing m...
Persistent Green Vegetation Fraction -        Classification and Prediction• Training data obtained from a range of source...
Classification and Prediction                   1                                                                  • Persi...
Persistent Green Vegetation Fraction                                       max                                     min    ...
Persistent Green Vegetation Fraction                                       max                                     min    ...
Persistent Green Vegetation Fraction                                       max                                     min    ...
Persistent Green Vegetation Fraction                                       max                                     min    ...
Persistent Green Vegetation Fraction   max   minNon-PGV  mask          http://tern-auscover.science.uq.edu.au/thredds/cata...
Persistent Green Vegetation Fraction -                  Validation• Accuracy statistics for persistent/non-persistent gree...
Persistent Green Vegetation Fraction –          Airborne LiDAR Validation• Collation of Riegl LMS-Q560 and Riegl LMS-Q680i...
Main Uses of PGV MapMain use would be for:   • Determining (1) Wooded Extent; (2) Forest Extent; (3) Forest     Density/Fo...
Main Uses of PVG Map
Future Work & ConclusionsFuture Work• Additional USGS imagery back to 1986 will allow a longer time-series  to be used, im...
AcknowledgementsAGENCY                                   PEOPLEABARES                                   Jasmine RickardsNT...
Validation of Landsat Time-Series ofPersistent Green-VegetationFraction for AustraliaPresentation by: Kasper Johansen1,4, ...
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Kasper Johansen_Validation of Landsat-based time-series of Persisten Green-vegetation fraction for Australia

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Transcript of "Kasper Johansen_Validation of Landsat-based time-series of Persisten Green-vegetation fraction for Australia"

  1. 1. Validation of Landsat Time-Series ofPersistent Green-VegetationFraction for AustraliaPresentation by: Kasper Johansen1,4, Tony Gill2,4, RebeccaTrevithick3, John Armston3,4, Peter Scarth3,4, Neil Flood4, StuartPhinn1,41The University of Queensland (k.johansen@uq.edu.au)2 NSW Office of Environment and Heritage, Department of Premier and Cabinet3 Queensland Department of Science, Information Technology, Innovation and the Arts4 Joint Remote Sensing Research Program
  2. 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. 3. AusCover Activities and Products
  4. 4. Field Campaigns
  5. 5. Airborne Campaigns
  6. 6. AusCover field and airborne campaignsField-based Measurements Airborne Measurements Satellite Based Measurements Time-Series Measurements
  7. 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. 8. National Landsat-based Persistent Green Vegetation FractionObjective: 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. 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. 10. Persistent Green Vegetation Fraction – Pre-Processing• At-sensor radiance
  11. 11. Persistent Green Vegetation Fraction – Pre-Processing• Standardised reflectance• Topographic correction• BRDF correction
  12. 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. 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. 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. 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 minmin Not PGVmask mask Variation in time-series Minimum fraction in time-series Persistent green fraction
  16. 16. Persistent Green Vegetation Fraction max min Non-PGV mask
  17. 17. Persistent Green Vegetation Fraction max min Non-PGV mask
  18. 18. Persistent Green Vegetation Fraction max min Non-PGV mask
  19. 19. Persistent Green Vegetation Fraction max min Non-PGV mask
  20. 20. Persistent Green Vegetation Fraction max minNon-PGV mask http://tern-auscover.science.uq.edu.au/thredds/catalog/ auscover/persistentgreen/persistentGreen/catalog.html
  21. 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. 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. 23. Main Uses of PGV MapMain 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 activitiesMore 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. 24. Main Uses of PVG Map
  25. 25. Future Work & ConclusionsFuture 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. 26. AcknowledgementsAGENCY PEOPLEABARES Jasmine RickardsNT Bushfires Andrew EdwardsNT NRETAS Nick CuffACRIS / CSIRO Gary BastinWA DEC Graeme BehnAirborne Research Australia Jorg HackerMonash Jason BeringerCDU Stefan MaierQLD HerbariumNSW Office of Environment and Heritage Tim Danaher
  27. 27. Validation of Landsat Time-Series ofPersistent Green-VegetationFraction for AustraliaPresentation by: Kasper Johansen1,4, Tony Gill2,4, RebeccaTrevithick3, John Armston3,4, Peter Scarth3,4, Neil Flood4, StuartPhinn1,41The University of Queensland (k.johansen@uq.edu.au)2 NSW Office of Environment and Heritage, Department of Premier and Cabinet3 Queensland Department of Science, Information Technology, Innovation and the Arts4 Joint Remote Sensing Research Program

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