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Ha Nguyen Phenology 2018 presentation on Melbourne pollen trends

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University of Technology Sydney Ha Nguyen's Phenology 2018 Conference presentation on the timing and trends of grass pollens in Melbourne, Australia.

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Ha Nguyen Phenology 2018 presentation on Melbourne pollen trends

  1. 1. Linking land use land cover changes in pastures to timing and trends of grass pollens in Melbourne, Victoria Ha Thanh Nguyen University of Technology Sydney 1
  2. 2. Motivations • Exotic grass species produced large quantities of pollens and are responsible for the allergic respiratory diseases in Australia. • In Melbourne, Victoria, the annual amount of grass pollens has been decreasing with accelerating date of maximum daily pollen count. • Hypothesis: land use change affects the location and strength of the pollen sources for Melbourne. 2
  3. 3. ´ Generate pollen seasonality metrics from Parkville, Melbourne, Victoria for 2016: start/ end dates, absolute peak and secondary peaks ´ Generate wind trajectories up to 72 hours prior to dates of absolute and secondary peaks in pollen records (HYSPLIT) ´ Classify points along such trajectories by land cover (Google Earth): pasture/ non pastures ´ Assess the percentage area that peak in EVI within 3 x 3 km2 surrounding each point (MODIS Subset) ´ Extract time series of Enhanced Vegetation Index from Landsat and analyze for past changes. Methodology: combine wind back trajectory with satellite remote sensing of phenology 3
  4. 4. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model ´ uses observed/ calculated 3-dimensional meteorological fields (>= 0.5deg) to estimate the most likely central path over geographical areas that provided air to a receptor at a given time. ´ starts a trajectory from a single location and height every 3 hours backward in time and then sum the frequency that the trajectory passed over a grid cell and then normalize by either the total number of trajectories or endpoints. 4
  5. 5. Sample HYSPLIT output • Points with higher trajectory frequencies are expected to contribute more to pollen concentration within the time window (24/ 72 hours) i.e. “potential point sources”. • Wind trajectories are different on different days, suggesting that the potential point sources for daily counts changed with the day/ episodes. • Most of the potential point sources are close to the trap, so we limited our analysis to one Landsat scene that encompassed the Parkville pollen trap. 5
  6. 6. Vegetation at HYSPLIT point sources • What is the dominant land cover within an 3 x 3km2 area surrounding each HYSPLIT point source? (Google Earth) • What is the vegetation phenology within each 3 x 3km2 area? (MODIS Subset) • To contribute to the pollen count of a certain date, an area must have flowered , and hence must have peaked in greenness prior to that date. 6
  7. 7. Vegetation phenology 7 16-days 250m MODIS data Green-up Brown-down
  8. 8. § For each HYSPLIT point source, percentage of the 3 x 3km2 area that has peaked in EVI prior to a date was assessed. § A point with higher area percentage that has peaked in EVI prior to a date is expected to contribute more to the pollen count of that date. 8 Secondary peak Absolute peak
  9. 9. 9 0.20 - 1
  10. 10. ´ Using wind back trajectories, Google Earth and MODIS, we have identified potential point sources that (i) an air parcel most likely passed through prior to reaching the Melbourne pollen trap and (ii) have peaked in EVI prior to a high pollen episode. ´ Points with 0%, 50% and 100% peaking EVI in their surroundings were selected and analyzed for past land cover changes using BFAST (Breaks For Additive Season and Trend). Our goal: link land use land cover changes to timings and trends in grass pollens 10
  11. 11. BFAST algorithm ´ Decompose data into trend, seasonality, and residuals. ´ Define breakpoints by directional or magnitude change between new data and historical data. ´ Make use of the entire time series. Only requires the tweaking of one parameter h, which is related to the duration and frequency of the process of interest and data availability. 11 Raw data Seasonality Trend Residuals Slope_2Slope_1 One Landsat pixel: 30 x 30m
  12. 12. Slope_2 = slope of the most recent trend 100% peaking before 14-10-2016 50% peaking before 14-10-2016 12
  13. 13. 0% peaking before 07-11-201650% peaking before 07-11-2016 13 Slope_2 = slope of the most recent trend
  14. 14. grassland to urban areas14
  15. 15. grassland to other types of vegetation15
  16. 16. grassland stays grassland16
  17. 17. Validation and Future works • Fine-tuning parameters of BFAST • Grass observations from the Living Atlas of Australia. • Insight from agronomists and farmers of changes in pastures. 17 Subtropical species - Temperate species - [Source : Medek et al., 2016]
  18. 18. Conclusions ´ In this study, we analyzed land use land cover changes at locations that (i) an air parcel most likely has passed through prior to reaching the Melbourne pollen trap and (ii) have peaked in EVI (and hence flowered) prior to a high pollen episode. ´ The potential sources of grass pollens are different for different episodes of high pollen counts throughout the pollen monitoring period of 2016. These potential sources also differed in terms of their vegetation phenology. ´ At these potential point sources, the most popular land use change trajectories are urbanization, conversion of pasture to other types of vegetation and decrease in pasture greenness. 18
  19. 19. 19 Acknowledgement

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