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Remote sensing with drones: The challenges of obtaining truly quantitative data (beyond pretty pictures)
The document discusses the use of unmanned aircraft systems (UAS) for quantitative remote sensing in environmental and agricultural applications, emphasizing their advantages over traditional satellite imagery, including higher spatial and temporal resolution. It highlights various applications such as precision agriculture, crop monitoring, and the importance of data calibration and metadata standards for successful UAS deployment. The future of UAS remote sensing presents exciting possibilities, necessitating continued research and demonstrator studies to overcome current challenges.
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Remote sensing with drones: The challenges of obtaining truly quantitative data (beyond pretty pictures)
- 1. Dr Darren Turner- TerraLuma research group Surveying and Spatial Sciences School of Land & Food Remote sensing with drones: The challenges of obtaining truly quantitative data (beyond pretty pictures)
- 2. TerraLuma • We developUnmanned Aircraft Systems (UAS) and image processing techniques for environmental, agricultural, and high-precision aerial mapping applications • Our focus is on quantitative remote sensing of vegetation with the use of sophisticated UAS sensors to better understand the structure, distribution, and functioning of vegetation, and to bridge the observational scale gap between field samples and satellite observations.
- 3. Problems with satellite& airborne remote sensing • Spatial resolution not high enough (2 m – 30 m pixel size) • Not frequent enough + can’t get imagery when needed • High Res commercial imagery $$$$$$ • Mainly suitable for large areas • Only one specific sensor • Unmanned Aircraft Systems (UAS also known as UAVs or drones) fill this niche
- 4. •Unique aspects ofUAS for mapping/monitoring: 1.Spatial resolution: Highest spatial resolution imagery available (1 cm) 2.Temporal resolution: Fly on-demand at critical stages 3.Spectral resolution: Integration of multiple sensors imaging outside the visible range (both passive and active)
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- 10. Barley – UASRGB image True colour composite of Uni Farm Barley Crop – 30/11/2010
- 11. Barley – UASnear-infrared image False colour composite (Green, Red, Infrared) of Uni Farm Barley Crop – 30/11/2010
- 12. Normalised Difference VegetationIndex (NDVI) NDVI of Uni Farm Barley Crop – 30/11/2010
- 13. Image courtesy ofK. Price. https://www.agronomy.org/story/2013/nov/tue/improved-crop-monitoring-takes-flight NDVI ≠ yield NDVI ≠ health NDVI ≠ stress Meaning of NDVI?
- 14. University of MelbourneDookie Experiment Sites UAV-borne Wheat Growth Monitoring: Nitrogen impact Source: Richard Eckard, UniMelb
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- 16. Thermal imaging Thermal Imageof Uni Farm Barley Crop – 30/11/2010
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- 19. Houston’s Farm Paddock ofLettuce 2 cm/pixel resolution mosaic Flying height 80 m Total area 2 ha
- 20. Example of diseasedlettuce
- 21. HyperUAS Lucieer, A.,Malenovsky, Z., Veness, T., Wallace, L. (2014). HyperUAS – Imaging spectroscopy from a multi-rotor unmanned aircraft system. Journal of Field Robotics, 31(4): 571-590. doi:10.1002/rob.21508 TM_width Green Yellow Withering Vegetation Wavelength (nm) Reflectance -0.05 0.05 0.15 0.25 0.35 0.45 0.55 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 41 32 5 7
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- 27. Crop Surface Modelof Poppy Farm
- 28. Crop Height vsCapsule Volume Crop height cm 20-40 40-60 60-80 80-100 Bare ground
- 29. UAS for poppycrops Low Medium High Very High Bare ground Yield Map
- 31. Hyperspectral snapshot sensor(IMEC) Source: IMEC and PhotonFocus
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- 37. Conclusions • UAS arean effective tool to capture high-resolution imagery • UAS imagery can be used for wide range of applications • Research required to overcome hurdles and generate “demonstrator studies” • Calibration is critical • The future of UAS remote sensing is exciting!
- 38. The following slidesare courtesy of Professor Stuart Phinn (UQ) They were presented at the UAS4RS conference 24th-25th May 2017
- 39. Defining: Data: • Flight planningfiles • Position, timing and attitudinal files • Associated field data collection files (radiometric and positional) • Raw image data files and flight logs • Processed data files at a range of levels • Ancillary and QA/QC files from processing
- 40. Defining: Meta-data • Content? • Flightplanning details • Flight log • Position, attitude, sensor operation settings • Standard spatial meta-data descriptors • Discipline and software specific ? • Licencing requirements • Metadata Standard File Formats and Entry Tools?
- 41. Defining: Protocols and Standards •Flight planning • Image acquisition • Geometric Correction • Radiometric Correction • Output Spatial Data Set Generation • Error assessment and QA/QC
- 42. • Purposes ofdata publishing: • Requirement of funding or institution • Security, and return from investment • Enable discovery and re-use • Stages of data publishing: • Collection • Verification and Quality Assurance + Checking • Storage format • Metadata selection and generation • Select and assign licence • Assign unique and citable identifier • Select suitable Publisher-Portal • Publish…. Defining: Publishing
- 43. • Why shouldI publish data? http://www.tern.org.au/Data-publishing-pg26249.html
- 44. How? Existing UAS EOResearch Infrastructure to Build on? • Individuals
- 45. How? Existing UAS EOResearch Infrastructure to Build on? • Research Centres?
- 46. How: Existing UAS EOResearch Infrastructure to Build on? • On-line publishers
- 47. How: Existing UAS EOResearch Infrastructure to Build on? • Various “open-” sites e.g. www.dronecode.org opendronemap.org https://github.com
- 48. How: Existing UAS EOResearch Infrastructure to Build on? • Fowlers Gap Site http://fowlersgap.uas4rs.org.au/data-support/
- 49. How: Existing UAS EOResearch Infrastructure to Build on? • TERN AusCover http://portal.auscover.org.au
- 50. How: Existing UAS EOResearch Infrastructure to Build on? • TERN AusCover
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