Application of multi-source UAV data to assess revegetation efforts on waste rock

Transcription

1 Application of multi-source UAV data to assess revegetation efforts on waste rock Tim Whiteside, Renée Bartolo & James Boyden Environmental Research Institute of the Supervising Scientist

2 Outline of this talk Background to the Trial Landform at Ranger Project aim Data capture and analysis Some preliminary results Concluding comments & What s next 5 Jan 12

3 Location Northern Australia Mining lease established before declaration of Kakadu National Park Surrounded by Kakadu National Park Tropical monsoonal climate with distinct wet & dry seasons ~1550 mm annual rainfall 3

4 Ranger Uranium Mine Scheduled for closure in 2026 Final landform will be mostly waste rock Established a trial landform to understand waste rock behaviour Information about: Material transport Hydrology Vegetation Helps inform closure criteria, calibrate models etc.

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6 Construction Construction Built in layers Waste rock & lateritic material Mixed using an excavator Levelled with large bulldozer Estimated cost of $10 million Mixing

7 Trial Landform (TLF) Plots 1&2 Waste rock only Plots 3&4 Waste rock & laterite mix

8 Vegetation Tubestock planted on EP1 & EP4 - March 2009 Direct seeding occurred on EP2 & EP3 -July 2009 Some infill planting - tubestock on EP1 & EP4 Jan 2010 Direct seeding had poor germination, infill planted with tubestock Jan 2011 Relied in natural recruitment since 10 Dec 09 Need images of the vegetation Perhaps before and after 6 May 12

9 TLF vegetation now On site for 8-9 years Tree stem diameters > 5 cm No understorey yet on plots 1&2 Due to substrate plots 3&4 has understorey (mostly weeds) and is managed

10 Primary aim of this project To assess ongoing viability of an established revegetation effort on waste rock Resilience fire, seasonality, substrate Species composition Using UAS data major benefit is data frequency and full site coverage

11 Skycam UAV - Swampfox Bungee launch Parachute recovery

12 3DRobotics X8+

13 Gryphon Dynamics X8 1400

14 Data captures to date - TLF Year Date Fixed Multi RedEdge HS LiDAR RGB May 28 July 14 October 16 November January 26 April 18 May Fixed wing flights at 100 m alt Multi rotor MS and HS flights at 70 m Lidar at 30 m

15 Data processing RGB and multispectral data processed using Pix4D Mapper MS radiometric calibration to reference panel Up to 8 GCPs used DGPS recorded All RGB and MS data co-registered to May 2016 data

16 Multispectral time series

17 Controlled burn May 2016

18 Multispectral time series

19 Multispectral time series

20 Multispectral time series

21 Multispectral time series

22 Multispectral time series

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25 Object-based analysis of cover pre- and post-burn

26 Object-based approach Multiresolution segmentation Random Forest classifier Accuracy assessment RGB data used for segmention Based on mainly on multispectral data Point based from mosaic

27 Random Forest parameters Features used

28 Accuracies Trees vs non-trees 92% accuracy Weeds vs non-weeds 85% accuracy 200 stratified random points per class

29 Accuracies Trees vs non-trees 91% accuracy Weeds vs non-weeds 95% 200 stratified random points per class

30 Building up a field reference dataset

31 Conclusion So far: UAV data enables high temporal analysis of rehabilitation sites at the plant level. Enables the monitoring of seasonal effects (eg phenology, water stress), plant fate and fire effects for the whole of a site.

32 Strengths and challenges Strengths Coverage for whole of site ~ 30 min Cost effective multitemporal data set Challenges Remote locality Terrain Erectophile leaves Sparse canopies Wind and thin stems

33 Further work Continued data collection including hyperspectral and LiDAR data Using the multi-temporal UAV data to describe: Further analysis of structural, spectral and textural information to discriminate tree and ground cover Plant object fate analysis as site matures further Surface behaviour including particle movement

34 Thank you. Any questions?