Using Remote Sensing to Monitor Plant Phenology Response to Rain Events in the Santa Catalina Mountains Katheryn Landau Arizona Remote Sensing Center Mentors: Willem J.D. van Leeuwen, Theresa Crimmins, and Michael Crimmins Arizona Space Grant Consortium Symposium University of Arizona 17th April 2010 Desert Scrubland - Riparian Scrub – Oak-Pine Forest – Pine Woodland Abstract It is important to monitor phenological changes to better understand plant response to climate change as the IPCC has stated that climate change has a discernible impact on plant populations. This study assesses the use of satellite data to monitor changes in the onset of greenness and total plant productivity for dry and wet years for a sky island in the Southwest. Satellite based normalized difference vegetation index (NDVI) was collected for a 10-year period (2000-2009) across a 5-mile, 4,158 ft elevation gradient in the Santa Catalina Mountains near Tucson, Arizona, USA. Results show a strong relationship between greater rainfall, earlier onset of greenness, and total productivity. Satellite data can be used to identify shifts in the timing of plant activity and allow for greater understanding of the long-term impact predicted decreased precipitation will have on plant phenology. Photo Acknowledgement: David Bertelson

Why Study Vegetation Phenology? Phenology – study of plant life cycle events Climate change has been shown to have a discernible impact on plant populations Studies have related ground observations to climate - Crimmins et al 2008, 2009 Remote sensing allows for regional observation of plant response Climate Change – IPCC reports Crimmins et al – want to see if remote sensing can be integrated with ground observations to see the same trends/impacts Satellites - data can be used for to observe identify shifts in the timing of plant activity and allow for greater understanding of the long-term impact predicted decreased precipitation will have on plant phenology.

Study Objectives Explore the relationship between precipitation and vegetation response Derive phenological metrics from satellite time series Normalized Difference Vegetation Index (NDVI) data for a range of vegetation communities Assess applicability of satellite data to monitor changes in the start of season (SOS) and total plant productivity for dry (2002) and wet (2006) years NDVI – signature of plant productivity

Study Area Mesic Semi-Arid Pine Forest Oak-Pine Woodland Oak Woodland Mile 5 Desert Scrub Mile 4 Scrub Grassland Mile 3 Mile 2 5 mile trail in the Santa Catalina Mountains Sky Island Mountain islands of forests isolated by intervening valleys of grassland or desert Several biotic communities are represented along the route to Mt. Kimball Desert scrub at the base of the mountain range to pine forest at the peak. 4,158 ft elevation change 3100 ft to 7,258 ft Semi-arid to mesic ecoregions Bi-modal growing season – spring and summer Riparian Scrub Coronado National Forest Tucson Semi-Arid Mile 1 Photo Acknowledgement: David Bertelson

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Dry Wet Precipitation – Seasonally averaged precipitation

Methods and Analysis Process satellite derived NDVI and PRISM rainfall time series data for 2000-2009 Use noise reduction and curve fitting software to extract pheno-metrics: TIMESAT - Jönsson & Eklundh, 2004 Correlations between precipitation and pheno-metrics - SOS and productivity 10 year time series Advanced Very High Resolution Radiometer (AVHRR) Parameter-elevation Regressions on Independent Slopes Model (PRISM)

LI Start t, NDVI LI – Large Integral Time series Savitzky–Golay filter Been shown to best fit the bi-modal growing seasons seen in the Southwest Works well with quasi-periodic time series data SOS – Productivity (S Integral) – area between under the curve and the baseline

Start of Season Linear Regression R2 – coefficient of determination 0<R<1, 0 – cannot be predicted without error, 1 – can be predicted without error Examples of upper and lower elevations Mile 1 Vegetation – Desert Scrub Mostly annual growths Creosote Less year-to-year variability in the spring SOS No statistically significant relationship between rain events and when SOS occurs 5 - Pine forest, oak-pine woodlands Mostly evergreens Very slight relationship between rain events and the SOS, stronger with the spring growing season

Productivity Productivity – total photosynthetic activity Desert Scrub Greater productivity occurring in the summer season, corresponds with overall higher precipitation Not a statistically significant precipitation-productivity relationship at lower miles Scrub Grassland Stronger relationship in spring season

Study Findings High elevation more productive than lower elevation Earlier SOS at lower elevation Vegetation productivity has positive correlation with rain events Precipitation does not clearly determine vegetation SOS or productivity Satellite data can be used to monitor vegetation pheno-metric changes High elevation more productive that lower elevation Mile 3 showed overall greater productivity for Spring & Summer seasons Earlier SOS at lower elevation Spring & Summer SOS started earlier at lower elevation (mile 1) than at higher elevation (mile 5) Vegetation productivity has positive correlation with rain events Wetter years (2006) have overall greater productivity in Dryer years (2002) have less overall productivity Summer is the wetter season in the Santa Catalina Mountains Summer consistently shows greater productivity Precipitation does not clearly determine vegetation SOS or productivity Precipitation is not the only factor in determining when a season will start or how much photosynthetic activity will occur Satellite data can be used to monitor vegetation pheno-metric changes Although cannot see the exact trends, satellites are still able to give regional trends Able to see the extent of annual change Using time series satellite data will still allow to see major shifts in green up activity and the extent of plant growth

Recommendations Analyze longer time series Semi-arid environment has high annual precipitation variability Incorporate more environmental variables Temperature Soil Topography Analyze longer time series More years will give more data, increasing likelihood of observing trends Semi-arid environment has high annual precipitation variability Incorporate more environmental variables

Thank You! Photo Acknowledgement: David Bertelson

Normalized Difference Vegetation Index The generic normalized difference vegetation index (NDVI): has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative surface, and of assessing the length of the growing season and dry-down periods (Huete and Liu, 1994).