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

An Integrated Approach

Jonathan M. HanesPh.D. Student

Department of GeographyUniversity of Wisconsin-Milwaukee

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

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

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

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

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

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

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

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?

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

SOS Comparison

SOS Comparison

SOS Comparison

SOS-SI First Bloom Comparison

• Variable correlations

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

SOS-Native Species Comparison

• Variable correlations with average bud-burst of 4 native species

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

Clustering Approach

4 Clusters of Native SpeciesUWM Field Station

Harvard Forest

3 Clusters of Native SpeciesUWM Field Station

Harvard Forest

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

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)

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