Alex Held_Achievements of AusCover - TERN's remote sensing data facility


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Alex Held_Achievements of AusCover - TERN's remote sensing data facility

  1. 1. AusCover The National Satellite Image-basedBiophysical Data and Ground Validation Facility
  2. 2. AusCover FacilityProduction and delivery of nationally consistent long‐time series ofsatellite‐based biophysical map products and next generation remotesensing research data that is validated for Australian conditions.
  3. 3. Capabilities to be DeliveredRemote Sensing Data Delivery Backbone • Solving data formats, interoperability, data-policy, • Physical storage, efficient delivery to end-users etc. Data Production Network (via Central Console and 7 Regional “Nodes”) • Nationally-consistent, standard biophysical data products, • Metadata and technical support documents • Specialised space-borne, airborne & in-situ research-grade data Ground Validation Program and Instrumentation • Set national standards & field validation protocols, • TERN SuperSites and Environmental transects etc. • International Benchmarking (eg CEOS WGCV LPV)
  4. 4. The AusCover Team & Network Securing Data and Collaborations via Links to International Agencies and Networks Darwin Node Stefan Maier (CDU) Brisbane Node Kasper Johansen UQ) Peter Scarth (DSITIA Perth Node Merv Lynch (Curtin) Sydney Node Alfredo Huete (UTS) Adelaide Node Megan Lewis (Adel. U.) Canberra Node Alex Held (CSIRO)7 Regional Nodes Melbourne Node Medhavy Thankappan (GA) Ian Grant (BoM)11 Institutions Simon Jones (RMIT)28+ Researchers andStudents..over 500 TBytes ofground, airborne and satellite data
  5. 5. Progress• Extensive field campaigns and airborne data acquisitions in 2012. Six out of eight AusCover Test-sites covered• Drafting first field validation manual “Green Book”.• Consolidation of continental-scale datasets, associated metadata, and portal across AusCover data system.• Held several agency briefings across “TERN Themes” (Carbon, Fire, Biodiversity..)• Collaboration with Google Earth Outreach (“Earth Engine and Smartphone apps) and IIASA (GEOwiki)• Initiated establishment of demonstration “forest sensing and phenocam network”• Field validation program continuing for key data products (e.g. aboveground biomass and GEOSS Ecosystems)& many others..
  6. 6. Field Team Activities Selected photos source © Charles Tambiah and members of the AusCover team• TERN Supersites and AusCover field calibration sites• New TERN Field Instrumentation & testing• New data acquisition for key in-situ measurements (e.g. leaf chemistry, canopy structure, reflectance, leaf-area index, etc.)• Validation and calibration of airborne and satellite images
  7. 7. AusCover Good Practice Guidelines, Contents• Review of Validation Campaigns• Representativeness and Sampling Design• Sensor Calibration – Vicarious calibration – Atmospheric correction – Radiometric – Optical sensors – Best practice guidelines for calibration and validation of SAR data and derived biophysical products• Validation of AusCover Biophysical RS products – LAI, Fpar – Fractional Cover• Validation of key variables derived from biophysical RS products – Phenology – Leaf spectroscopy – Foliar chemistry – Persistent green vegetation index – Biomass• AusCover specifications and quality assurance steps for hyperspectral and LIDAR data• Good Practice Field Data Management and Delivery• National examples of spectral dataset exchange• National examples of CalVal• Appendices with protocols for field spectroscopy, FPC, LAI, etc.
  8. 8. Satellite and Airborne-Derived Data Products for use in Ecosystem Science and NRMNational/Regional Scale Time-series • Green Land-Cover (NDVI-EVI) • Fractional Cover (green vegetation, non-photosynthetic vegetation, bare ground) • Fraction Absorbed PAR (Photosynthetically Active Radiation) • Burnt Area • Forest cover • Land Surface Temperature • Broad Ecosystem MapsState-based (to be extended nationally) • Fraction Projected Cover (FPC)In-Situ Validation and TERN SuperSite Information • Leaf-Area Index • Vegetation 3D Structure & biomass • Spectral Reflectance (nadir-corrected)Science Data • Airborne + Spaceborne hyperspectral • Lidar • SAR
  9. 9. Data AccessVia (inter connected servers)
  10. 10. Early Data Use Examples and Opportunities
  11. 11. Linking Climate and Land-Cover DynamicsGridded Climate Data:
  12. 12. Dynamics of Fractional Cover Validation (a CSIRO, ABARES C4C, and TERN AusCover Project)• “Fractional Cover Product” – Used for land condition and trends, soil erosion – Vegetation indices based unmixing of monitoring photosynthetic vegetation, non- photosynthetic – Available from vegetation and bare soil – Guerschman et al, 2009 (RSE) – Based on MODIS reflectance (MCD43A4) – Australia, 8-day, 500 meter, 2000-2012 – Semi-operational, updated weekly
  13. 13. Adoption by South Australia of remotely sensed soil exposure data to improve soil erosion risk monitoringGreen vegetation / non-photosynthetic vegetation/ soil from a dry to a wet time, as measured bythe Relative Spectral Mixture Analysis index(originally by Greg Okin, developed by AusCoverfacilitated collaboration between Greg Okin andAusCover SA)
  14. 14. Tracking of Land-Cover Dynamics and Drying WARRA Enhanced Vegetation Index0. 0 2/17/96 2/17/97 2/17/98 2/17/99 2/17/00 2/17/01 2/17/02 2/17/03 2/17/04 2/17/05 2/17/06 2/17/07 Alice Springs mean_evi0.60.40.2 0 3/4/96 3/4/97 3/4/98 3/4/99 3/4/00 3/4/01 3/4/02 3/4/03 3/4/04 3/4/05 3/4/06 3/4/07
  15. 15. Comparing Land-Cover dynamics with known land- management practices across Australian Wildlife Conservancy Sanctuary sites NVIS classes (protected and non-protected) MODIS satellite data (protected and non-protected) Hummock Grassland protected and non- protected MODIS EVI Decadal MODIS time series 2000 2006 2011
  16. 16. Using International Data to Monitor Land-cover and productivity: e.g. Southern Hemisphere Crop NDVI Anomaly, September 15th, 2012 Australia: Wheat Brazil/Argentina: Wheat Crop NDVI Anomaly South Africa: Wheat Non Cropland -0.4 0 0.4 Worse than normal Better than normal normal Current season crop development (2012) Median season development (2000-2011)
  17. 17. Sensing Australia’s Landscapes
  18. 18. New Ecosystem Science with High-Resolution Data and Ground Sensors
  19. 19. Text…• Text...
  20. 20. Use of Terrestrial Scanning Lidar to Characterise 3D Structure of Canopies• Evaluating Ground-based lidar technologies designed specifically for forest and vegetation assessment• The CSIRO-patented Echidna® has key differences from scanning rangefinders – Digitizes the full return waveform – Has variable beam divergence – Uses full hemispherical scanning and beyond – Linear response and calibration AGU 2012 22
  21. 21. Visualisation of Echidna Forest Measurements in CaliforniaGap probability to range (dark = 1, light = 0, green indicates partial return Plate Carré (equal-angle, “Andrieu”) projection AGU 2012 23
  22. 22. Canopy Lidar Data Robson Creek - QLD
  23. 23. 25
  24. 24. Demonstration of In-Situ Sensor Network at Whroo Flux Tower Site - Victoria• Ten-node wireless sensor network measuring Photosynthetically Active Radiation (PAR), temperature and humidity• Five minute sample frequency
  25. 25. Following through the Fire TERN Theme…. Dr Geoff Garrett (photo at left) speaks at the opening of the TERN–Queensland Government briefing on fire risk, which was attended (photo at right) by Fergus Adrian (left) and Bernard Trembath (right) from the Queensland Department of Fire and Emergency Services
  26. 26. Sample: TERN/AusCover Fire ProductsProduct Name Availability ValidationBurned Area – MODIS (CDU 250m), Australia Partial 2Fire Frequency – MODIS (CDU 250m), Australia 2013/Q4Fire Frequency – AVHRR (1km), Australia Available 1Fire Severity – MODIS (CDU 250m), Australia 2013/Q4Sub-pixel Fire Patchiness – MODIS (CDU 250m), Australia 2013/Q4TOA Reflectance Red/NIR – MODIS (for manual burned area Partial 3mapping), North and Central AustraliaThermal Anomalies (Fire Hotspots) – MODIS (LPDAAC Available 2MOD14A2 1km), Australia – not near real-timeBurned Area – MODIS (LPDAAC MCD45A1 500m), Australia Available 1Burned Area – MODIS (University of Maryland MCD64A1 Available 1500m), Australia 1 Product accuracy is assessed from a small (typically < 30) set of locations and time periods by comparison with in-situ or other suitable reference data. 2 Product accuracy is estimated over a significant set of locations and time periods by comparison with reference in situ or other suitable reference data. 3 Uncertainties are characterized in a statistically robust way over multiple locations and time periods representing the complete range of environmental conditions.
  27. 27. Grassland curing (Bureau of Meteorology)• Bushfire CRC curing project maps (A, B, C, D)• National maps and state cut-outs
  28. 28. Experimental Live Fuel Moisture Content Product (WA Landgate)July 2009 November 2009
  29. 29. GHG Emissions due to Fire
  30. 30. Sample: Fire Research Enabled through TERNOliveira et al. (in preparation). Characterization of fire in tropical savannas of northern Australia usingsatellite measurements of fire radiative power.Edwards et al. (accepted). Remote sensing of fire severity in north Australian tropical savannas.Price et al 2013. Role of weather and fuel in stopping fire spread in tropical savannas.Maier et al. 2013. Sensitivity of the MODIS fire detection algorithm (MOD14) in the savanna region of theNorthern Territory, Australia.Maier & Russell-Smith 2012. Measuring and Monitoring of Contemporary Fire Regimes in Australia usingSatellite Remote Sensing.Maier et al. 2011. Characterising Bush Fires in Australia’s Top End using MODIS Active Fire Observations.
  31. 31. AusCover Facility Coordination OfficeContact DetailsAlex Held – Facility DirectorRowena Smith – Facility CoordinatorMatt Paget – Data and Systems CoordinatorCSIRO Marine and Atmospheric ResearchGPO Box 3023Canberra, ACT 2601AustraliaTel.: 61-2-6246 5630Fax: 61-2-6246 5988Email: / /