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Modeling of Ecological Processes: Applications in Arizona and Chile Wim van Leeuwen w/ contributions from ARSC School of Natural Resources and the Environment School Sc oo o of Geography Geog ap y and a d Development e e op e t Arizona Remote Sensing Center University of Arizona, Tucson, USA Overview • Phenology? gy • Remote Sensing? • Is I land l d surface f phenology h l a proxy for climate? • Sky Island Research • Chile • The Future What is phenology? Phenology is a proxy for climate (IPCC, 2007) “Phenology gy – the timing g of seasonal activities of animals and plants – is perhaps the simplest process in which to track changes in the ecology off species in response to climate change.” “Observed Observed phenological events include leaf unfolding, flowering, fruit ripening, leaf colouring, leaf fall of plants, bird migration, chorusing of amphibians, and appearance/emergence of butterflies.” Phenology? • Biological g life cycle y * Abiotic * Biotic • Flower phenology • Butterfly phenology • Green-up phenology Flower phenology - Cherry blossom Washington DC has traditionally had a Cherry Blossom Festival during the first t two weeks k in i A April, il culminating l i ti iin mid id April with a parade. Over the past few decades, the cherryy trees have been blooming earlier and earlier, so that peak bloom is now at the beginning, rather than during the celebration at the end of the festival…… http://www.windows.ucar.edu/citizen_science/bud burst/phenology_important.php Phenology - Monarch butterflies face plight of flight in hot Texas (Washington Post Oct 7,7 2011) http://www.learner.or g/jnorth/tm/monarch/ MigrationPathwayM exicoCalvert.html NDVI green-up Phenology NDVI green-up Phenology Edwards Plateau 6500 Mean NDVI 6000 NDVI * 1 10000 5500 5000 4500 4000 3500 3000 Date MODIS NDVI 16-day Composite Image Refle ectance 1 BGR NIR 0.8 SWIR Portion of Electromagnetic Spectrum 0.6 Normalized Difference Vegetation Index 0 0.4 0.2 0 300 900 1500 2100 Wavelength (nm) 2700 Green Vegetation Dry Grass Soil NDVI = (NIR - RED) (NIR + RED NDVI MODIS - TERRA Jan, 2004 MODIS NDVI 16-day Composite Image Normalized Difference Vegetation Index NDVI = (NIR - RED) (NIR + RED MODIS – TERRA/AQUA NDVI How does phenology impact the world? Phenology is an essential component of the biosphere Adapted from Bonan (2002) Ecol. Climatology Climate…. Cli t and phenology… Fig. 1. (A) Geographic distribution of potential climatic constraint to plant growth derived from longterm climate statistics Temperature Vapor Pressure Deficit Solar Radiation R. R. Nemani et al., Science 300, 1560 -1563 (2003) Published by AAAS Phenology is a proxy for climate (IPCC, 2007) “Phenology gy – the timing g of seasonal activities of animals and plants – is perhaps the simplest process in which to track changes in the ecology off species in response to climate change.” “Observed Observed phenological events include leaf unfolding, flowering, fruit ripening, leaf colouring, leaf fall of plants, bird migration, chorusing of amphibians, and appearance/emergence of butterflies.” Vegetation Trajectories © Kamel Didan,VIP lab Colorado Rocky Mountains Sierra Mariquita Sierra Elenita MODIS NDVI MODIS NDVI May 25, 2010 Sierra Madres Sonoran Desert Ponderosa Pine Shrubland Cottonwood,Pinyon, Juniper, Oak ATMOSPHERIC COMPOSITION ANTHROPOGENIC ACTIVITES CLIMATE VARIABILITY & CHANGE Key to: BIOSPHERE MODELS MANAGEMENT TOURISM HEALTH LAND-USE/LANDLANDUSE/LANDCOVER CHANGE and DISTURBANCE © K. Didan,VIP-lab WATER and CARBON CYCLE Background ac g ou d o of tthe e Area ea Biogeography (Lomolino et al., 2010) Goals and Objectives Develop an assessment of changes in landscape scale phenology vegetation along elevation gradients mountain sky islands in the drylands of the Southwest US and Northern Mexico. Examine the variability in climate and vegetation egetation green green-up p relationships seasonally and interannually, along the elevation and latitudinal gradients. Precipitation (PRISM) Elevation NDVI Average summer Precipitation 2007 Pinaleño Catalina Rincon Picacho Sierra El Pinito mm (x100) Average winter A i t Precipitation 2007 mm (x100) Sierra San Louis NDVI NDVI phenology 7000 Fire 1 2 6000 NDVI (x10000) 5000 4000 3000 2000 1000 0 Drought Penaleno S t C t li Mountains Santa Catalina M t i Sierra El Pinito El Picacho 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 23 Observation/Year Photo credits: Dave Bertelsen 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 NDVI Elevation ((m)) '2005-15 Aspect changes... NDV a a119 a18 a17 b16 c6 b115 d15 c4 d114 e15 d13 f3 e112 f14 g13 h12 0 h1i9 h11 i12 0 j9 k i19 1 l9 k1l8 l10 m0 8 n m7 o79 n o68 n o55 p5 o4 p4 o1 p1 o o23 NDVI and E Elevati MODIS NDVI and Elevation for pixels along Finger Rock trail pixels 8000 Elevation 3200m 2100m - Forest Woodland Snow‐dips 1100m - Desert Drough 7000 Phenological asynchronies (start, peak and end of growing season) t 700m NDVI (x10000) 6000 5000 4000 3000 2000 Bimodal Unimodal 1000 2000 - 1 2001 - 1 2002 - 1 2003 - 1 2004 - 1 2005 - 1 Year - period 8000 NDVI (x10000) 7000 y = 2.7359x ‐ 180.2 R2 = 0.8524 Pine‐Oak Forest & Woodland 6000 5000 4000 3000 2000 Desert Desert 1000 Cacti & Scrub 800 1000 1200 1400 1600 1800 2000 2200 2400 Sierra Mariquita Sierra Elenita MODIS NDVI May 25, 2010 Elevation (m) Methods and Data Analysis: • NDVI : 2/26/2002- 03/11/2002 R Remotely t l sensed d data d t (MODIS) NDVI time series 2000 -2010 250 m spatial resolution 23 23-period-per-year period per year (16-day (16 day composite) NDVI : 6/26/2007- 07/11/2007 • Climate data p ((PRISM)) at 4 km Precipitation Average summer precipitation (JJSO) Average winter precipitation (DJFM) Temperature (min and max) Average A summer ttemperature t (JJSO) Average winter temperature (DJFM) • Topographical data Elevation Aspect Slope Elevation in meter Methods and Data Analysis 1 1. Pheno-metrics Pheno metrics from NDVI time time-series series from 2000 - 2010 TIMESAT analysis software – – – 2. Start of growing season (SOS) from 2000 to 2010 Length of season (LS) Small integral (SI) Statistical Analysis Spatio-temporal trend analysis of phenological parameters Coefficient of variation of phenological parameters Multiple Linear Regression • Phenological response varibles: SOS, SI, LS • Cli t d Climate data: t Precip, P i Temp T • Topographical data : Elevation, Slope, and Aspect Evaluate interannual trends and spatial variation in vegetation dynamics across sky islands Evaluate seasonal and interannaul varibility across sky islands Evaluate temporal variation in vegetation dynamic with the relation to climate and topographical data TIMESAT: Phenological metrics (t, NDVI) l. deriv r. deriv Peak (t) Amplit. Sm Integr Length End (t) Lg Int. Start (base,t) 2000 2001 2002 2003 2004 2005 TIMESAT: pheno-metrics Large Int. NDVI (Productivity) Small Int. NDVI S Senescence INPUT: stack of time-series time series images Greenup Amplitude Peak Base Length End Mid. Start OUTPUT: 11 “layers” of pheno metrics pheno-metrics Start of growing season 2007 Small integral 2007 Phenological metrics The Pinaleño Mountains southeastern Arizona 260 240 220 Linear ((SOS2000)) Linear (SOS2004) Linear (SOS2008) Linear ((SOS2001)) Linear (SOS2005) Linear (SOS2009) Linear ((SOS2002)) Linear (SOS2006) Linear (SOS2010) Linear ((SOS2003)) Linear (SOS2007) 200 Start of season (DO OY) 180 160 140 120 100 80 60 40 20 0 1500 1700 1900 2100 2300 2500 2700 Elevation (m) 2900 3100 3300 3500 Elevation Startt of season (DOY) The Sierra El Pinito is a large Sky Island mountain range located in southeast of Nogales, Sonora 260 Linear (SOS2000) 240 Linear (SOS2004) ( ) 220 Linear (SOS2008) 200 180 160 140 120 100 80 60 40 20 0 1400 1500 1600 Linear (SOS2001) Linear ((SOS2005)) Linear (SOS2009) Linear (SOS2002) Linear ((SOS2006)) Linear (SOS2010) Linear (SOS2003) Linear ((SOS2007)) Elevation 1700 1800 1900 El ti (m) Elevation ( ) 2000 2100 2200 2300 260 240 220 Linear (SOS2000) Linear (SOS2004) Linear (SOS2008) Linear (SOS2001) Linear (SOS2005) Linear (SOS2009) Linear (SOS2002) Linear (SOS2006) Linear (SOS2010) 200 The Pinaleño Mountains 180 Start of se eason (DOY) Linear (SOS2003) Linear (SOS2007) 160 140 120 100 80 60 40 20 Star rt of season (DOY) 0 1500 1700 260 Linear (SOS2000) 240 Linear (SOS2004) 220 Linear (SOS2008) 200 180 160 140 120 100 80 60 40 20 0 1400 1500 1600 1900 2100 Linear (SOS2001) Linear (SOS2005) Linear (SOS2009) 2300 Linear (SOS2002) Linear (SOS2006) Linear (SOS2010) 2500 2700 Elevation (m) Linear (SOS2003) Linear (SOS2007) Sierra El Pinito. 1700 1800 1900 Elevation (m) 2000 2100 2200 2300 2900 3100 3300 3500 Annual SOS trends 80000 70000 60000 Linear (SI2000) Linear (SI2001) Linear (SI2002) Linear (SI2003) Linear (SI2004) Linear (SI2005) Linear (SI2006) Linear (SI2007) Linear (SI2008) Linear (SI2009) Linear (SI2010) Sm mall Integral Pi l ñ Mountains Pinaleño M t i 50000 40000 30000 20000 10000 0 1500 60000 Small IIntegral 50000 2000 Linear (SI2000) Linear (SI2003) Linear (SI2006) Linear (SI2009) Linear (SI2001) Linear (SI2004) Linear (SI2007) Linear (SI2010) 2500 Elevation( m) Linear (SI2002) Linear (SI2005) Linear (SI2008) 40000 30000 20000 10000 0 1400 Sierra El Pinito 1600 1800 2000 Elevation (m) 2200 3000 3500 Inter-annual variability of productivity changes as a function of elevation Coefficient of variation (COV) values for start of season (2000-2010) Elevation Coefficient of variation for the start of growing season was variable among the sky islands, highest for the northern sky islands vs southern This suggested that the landscape of the northern sky islands is responding strongly to interannual changes in climate and/or disturbance The observed high COV values for all of the sky Islands are located in Higher elevation The desert areas shows low and high variability in the season start dates likely related to distribution of winter/spring/summer precipitation COV of SOS Coefficient of variation of Small I t Integral l Elevation Distribution of coefficient of variation i ti (COV) values l for f small ll integral • The COV of vegetation g p production in the northern sky islands are higher comparing to these in the south. • This suggested that the landscape of the northern sky islands is responding strongly to interannual changes in climate and/or disturbance COV of Small Integral Elevation COV of length of season Coefficient of variation (COV) for length of growing season Intercept SOS Spatio-temporal trends. 1. P value shows significance and spatial p variability y for all sky islands Slope P Value Intercept Spatio-temporal trends for Small integral Slope P Value Intercept Spatio-temporal Spatio temporal regression for l length th off growing season Slope P Value Pinaleno Mountains MLR: SOS response to Climate and Topographical Data Madrean Pinyon-Juniper Woodland communities in the Pinaleno mountains (2003) DEM PinyonJuniper Woodland A Aspect t Sl Slope P Precw x x x P Precs T Tmaxw T Tmaxs x T i Tmins T i Tminw T i Tminw R2 x x 0.428 Pinaleno Mountains MLR: Small Integral in response to Climate and Topographical Data Precw= Winter Precipitation Precs= Summer Precipitation Tmaxs= Max Summer Temperature Tmaxw= Max Winter Temperature Tmins= Min Summer Temperature Tminw= Min Winter Temperature Elevation Aspect SI2000 x x SI2001 x x SI2002 x SI2003 x SI2004 SI2005 Slope x Precs Precw Tmaxs Tmaxw Tmins Tminw R2 x x x x x 0.245 x x x x x x 0.287 x x x x x x 0.263 x x x x x x x 0.478 x x x x x x x x x x x x x x x x x x x SI2006 x SI2007 x x SI2008 x x x x x x x SI2009 x x x x x x x SI2010 x x x x x x 0 552 0.552 x 0.400 x 0.051 x x x 0.364 x 0.186 x 0.533 0.505 Findings to date • Satellite time series data can be used to derive sky island vegetation phenological information to monitor and assess vegetation response to climate variability i bilit and d change h and d disturbance di t b events t such h as wildfires • The spatio-temporal phenological characterization utilizing MODIS time series data show distinctive vegetation response patterns and trajectories • Phenology of sky islands and elevation clines respond to topography and interannual variability in precipitation i it ti and d temperature t t van Leeuwen et al., 2010. Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data. Remote Sens., 2, 388-415. Davison et al., 2011. Remotely sensed vegetation phenology and productivity along a climatic gradient: on the value of incorporating the dimension of woody plant cover. Global Ecology and Biogeography.; Online July 10, 2010. http://dx.doi.org/10.1111/j.1466-8238.2010.00571.x MODIS NDVI Transect (North South) DOY 241 DOY 33 Winter DOY 241 Summer DOY 33 MODIS NDVI TRANSECTS (WEST EAST) Summer DOY 33 Winter DOY 241 (WEST-EAST) croplands http://daac.ornl.gov/glb_viz_2/16Oct2011_18:33:47 _840455417L-37.448497L71.841869S9L9_MOD13Q1/index.html Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). 2010. MODIS subsetted land products, Collection 5. Available on-line [ http://daac ornl gov/MODIS/modi http://daac.ornl.gov/MODIS/modi s.html] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A. Accessed 10-16-2011. MODIS NDVI interannual time series - Shrubland http://daac.ornl.gov/glb_viz_2/06Oct2011_ http://daac ornl gov/glb viz 2/06Oct2011 20:31:49_841728164L31.353636941500987L71.015625S25L25_MOD13Q1/index.html NDVI interannual ti time series i Forest http://daac.ornl.gov/glb_viz_2 /16Oct2011_18:51:41_35704 1069L-37.448497L71.521944S9L9 MOD13Q1/i 71.521944S9L9_MOD13Q1/i ndex.html Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). 2010. MODIS subsetted land products, Collection 5. Available on-line [ http://daac.ornl.gov/MODIS/modis.htm l] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A. Accessed 10-162011. NDVI interannual time series -Forest Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). 2010. MODIS subsetted land products, Collection 5. Available on on-line line [ http://daac.ornl.gov/MODIS/modis.htm l] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A. Accessed 10-162011 http://daac.ornl.go v/glb_viz_2/16Oct 2011 18:51:41 35 2011_18:51:41_35 7041069L37 448497L37.448497L 71.521944S9L9_ MOD13Q1/index.h tml MODIS NDVI interannual time series - AG http://daac.ornl.gov/glb_viz_2/06Oct2011_2 04 0:45:55_913454647L-38.343697L9134 464 L 38 34369 L 72.382719S25L25_MOD13Q1/index.html The Future • F Further h characterize h i pheno-metrics h i for f different diff sky islands and associated vegetation types • Examine impact of disturbance and climate change and variability • Sky island RS phenology model development • Are Sky y Islands g good barometers for climate change? • IPCC - Land surface phenology: a proxy for climate change and variability? • The phenology of Chile? • Longer time series Muchas Gracias! [email protected]