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Detecting the Onset of Spring in the Midwest and Northeast United States: An Integrated Approach

Detecting the Onset of Spring in the Midwest and Northeast United States: An Integrated Approach. Jonathan M. Hanes Ph.D. Student Department of Geography University of Wisconsin-Milwaukee. Introduction. Phenology

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Detecting the Onset of Spring in the Midwest and Northeast United States: An Integrated Approach

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  1. Detecting the Onset of Spring in the Midwest and Northeast United States: An Integrated Approach Jonathan M. Hanes Ph.D. Student Department of Geography University of Wisconsin-Milwaukee

  2. Introduction • Phenology • Plant & animal life cycle events triggered by environmental changes (i.e. temperature) • Example: Onset of spring in deciduous vegetation • Primary methods of assessing plant phenology • Native species observations • Simulated phenology based on cloned species • Satellite imagery

  3. Native Species Phenology • Advantages • Adapted to the local environment • Genetic variability can be evaluated • Represent a precise signal of a certain location • Disadvantages • Lack of geographically distributed observations • Geographical variations in response • Limit comparisons between different locations

  4. Simulated Phenology • Advantages • Large geographical coverage • Require simple input data (i.e. temperature observations) • Standardized response to the environment • Disadvantages • Model insufficiencies • Based on small number of species • Simulates a limited set of events

  5. Satellite Imagery • Advantages • Large geographical coverage • Integrated ecosystem-scale response • Disadvantages • Temporal resolution • Cloud cover & sensor error • Short period of record • Limited measurements • Start of season (SOS), end of season (EOS), growing season length

  6. Integrated Approach to Phenology • Combines native species phenology, simulated phenology, and satellite SOS measurements • Collaborator: Prof. Mark D. Schwartz • Study Areas (2000-2006) • UW-Milwaukee Field Station • Harvard Forest, MA • Park Falls, WI

  7. Satellite Data • Satellite-derived SOS measurements at all sites • Fisher’s Method (Fisher et al. 2006) • MODIS • Delayed Moving Average (Reed et al. 1994) • MODIS NDVI & EVI • Seasonal Midpoint (White et al. 1997,1999,2002) • MODIS NDVI & EVI • Boston Method (Zhang et al. 2003) • MODIS EVI

  8. Surface Data • Bud-burst dates of native species • 27 native species at UWM Field Station • 33 native species at Harvard Forest • Spring Index (SI) first bloom dates at all sites • Schwartz & Marotz 1986, 1988

  9. Research Questions • How are SOS measures related to each other? • Which SOS measure is most similar to SI first bloom? • How does native species bud-burst relate to SI first bloom & SOS measures? • How can all phenological measures be compared?

  10. SOS Comparisons • Correlations differ at the 3 sites • Correlations are strong • Fisher’s method is similar to other SOS measures at all sites • No other consistent similarities

  11. SOS Comparison

  12. SOS Comparison

  13. SOS Comparison

  14. SOS-SI First Bloom Comparison • Variable correlations

  15. SI First Bloom-Native Species Comparison • High correlation between average bud-burst of 4 native species & SI first bloom • Sugar maple (Acer saccharum) • Hawthorne (Crataegus sp.) • White ash (Fraxinus americana) • Witch hazel (Hamamelis virginia) • r=.842 at UWM Field Station • r=.824 at Harvard Forest

  16. SOS-Native Species Comparison • Variable correlations with average bud-burst of 4 native species

  17. Comparison of All Measures • Use hierarchical clustering • Organizes native species into groups based on bud-burst • Examine which clusters represent the signal captured by satellite sensors • Compare average bud-burst of each cluster with SOS and SI first bloom

  18. Clustering Approach

  19. 4 Clusters of Native Species UWM Field Station Harvard Forest

  20. 3 Clusters of Native Species UWM Field Station Harvard Forest

  21. Comparison of all Measures • “Phenological footprints” • Standardizes native species bud-burst and SOS to SI first bloom • Uses simulated phenology to connect SOS & native species • Useful for comparing different locations

  22. Conclusions • Correlation between SOS measures vary at each site • Possible combination of issues • Different locations • Potential errors from clouds, atmosphere, & sensors • SOS is similar to native species & SI first bloom • Similarity varies by location • Fisher’s SOS method is consistently similar • Uses a logistic growth model • Unique method of measuring vegetation (GVF)

  23. Conclusions • Clustering of native species phenology • Reveals site-specific differences in phenological response • Correlated with SI first bloom and SOS • Phenological footprint • Comparison of phenology at multiple sites • Can be used with different phenological measures

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