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Five Years of Land Surface Phenology in an Arctic Landscape
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Five Years of Land Surface Phenology in an Arctic Landscape

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  • 1. Five Years of Land Cover Reflectance in a Large Scale Hydrological Manipulation in an Arctic Tundra Landscape 1Santonu Goswami 2John A. Gamon 1Craig E. Tweedie 1Environmental Science and Engineering University of Texas at El Paso 2Dept. of Earth and Atmospheric Sciences University of Alberta - .
  • 2. Importance of Surface Hydrology (Soil Moisture) in ecosystem structure and function • Many observed and modeled change responses of arctic terrestrial ecosystems are related to surface hydrology: – Surface energy budgets (Chapin et al. 2005, Euskircken et al. 2007) – Land-atmosphere carbon exchange (Merbold et. al 2009, Wolf et al. 2008) – Geomorphic processes (Lawrence and Slater 2005, McNamara and Kane 2009) – Provision of ecosystem goods and services (Milennium Ecosystem Assessment) – Plant phenology and response to warming (Arft et al. 1999, Walker et al. 2006)
  • 3. Plants as an indicator of change • Detecting biotic responses to a changing environment is essential for understanding the consequences of global change. • Plants can work as effective indicators of • changing conditions and, depending on the nature of the change, • respond by increasing or decreasing amounts of green-leaf biomass, chlorophyll, and water content. • important effects on ecosystem processes such as NPP and nutrient cycling. • Plant phenology is detectable using remote sensing technologies and therefore scaleable over space and time (e.g. NDVI).
  • 4. Study Site - Biocomplexity Experiment, Barrow, Alaska
  • 5. Experimental Setup: Biocomplexity Experiment Tramlines (300m long) • Large-scale Hydrological Manipulation experiment to study the effect of Controlled varying Soil Moisture on ecosystem Carbon Drained Balance in the Arctic Flooded Dikes 100km Barrow (71º19'N 156º37'W) Atqasuk Coastal Plain Eddy covariance towers Brooks Range
  • 6. The Tram System
  • 7. The Tram System
  • 8. The Tram System
  • 9. The Tram System Near surface remote sensing technology • Measurements are made at every meter along each of the three 300 meter tramlines (precise and repeatable) • Collects hyperspectral data in the vis-nir region and digital images of vegetation • More than 200,000 data files collected in 5 years • Has the ability to carry various sensors packages • Can be used to study change in phenology, sun angle effect on vegetation etc • Standardized sampling design is part of a larger effort (SpecNet) to compare ecosystem responses using a combination of spectral reflectance and flux measurements Tramline system in the biocomplexity experiment is the largest ground based spectral data monitoring system that we are aware of internationally
  • 10. Measurement of Reflectance • Reflectance = ITarget / IDownwelling = Radiance/Irradiance • Reflectance correction = (RTarget / RDownwelling) X (RDownwelling/ RPanel) • NDVI (Normalized Difference Vegetation Index) calculation = (R800-R680)/ (R800+R680) • Also measures water table depth, thaw depth.
  • 11. Questions being asked in this study • How does land-surface phenology (i.e. surface reflectance properties) change inter-annually? • Is there any detectable effect of experimental treatment (flooding and draining) on surface reflectance?
  • 12. Transition Throughout the Season avg corrected reflectance south 070613 1 H2O Band reflectance 0.5 1st Week of June 0 350 450 550 650 750 850 950 1050 wavelength avg corrected reflectance south 070618 0.2 Chl Feature reflectance 0.1 3rd Week of June 0 350 550 750 950 wavelength 0.3 Corrected reflected south 080806 Chl Feature Reflectance 0.2 0.1 1st Week of August 0 350 550 750 950 Wavelength
  • 13. Inter-annual variability of NDVI in the treatment areas for pre-treatment years (2005, 2006, 2007) and treatment years (2008 and 2009) Peak growing time Peak growing time 0.70 0.70 0.60 0.60 0.50 0.50 0.40 NDVI 0.40 NDVI 0.30 Flooding 0.30 treatment effect Snow fall 0.20 0.20 0.10 Heavy flooding event 0.10 0.00 0.00 Snow-melt Snow-melt -0.10 -0.10 -0.20 -0.20 150 170 190 210 230 250 150 170 190 210 230 250 Day of Year Day of Year NDVI North 2005 NDVI North 2006 NDVI North 2007 NDVI Central 2005 NDVI Central 2006 NDVI Central 2007 NDVI North 2008 NDVI North 2009 NDVI Central 2008 NDVI Central 2009 FLOODED DRAINED
  • 14. 25 20 Water Table Depth (cm) 15 10 5 Flooding treatment Flooded 0 -5 2009 -10 2008 -15 -20 2007 160 170 180 190 200 210 220 230 0 Day of Year Comparison of inter- Thaw Depth (cm) Increased thaw in 2009 -15 annual variability of -30 NDVI to water table -45 160 170 180 190 200 210 220 230 depth and thaw depth 0.70 Day of Year in the flooded section 0.60 Heavy flooding 0.50 event for 2007 (pre- 0.40 0.30 0.20 treatment) and 2008 NDVI 0.10 Flooding treatment in 2008 0.00 Snow-melt and 2009 (treatment -0.10 -0.20 160 170 180 190 200 210 220 230 years) Day of Year
  • 15. 30 Water Table Depth (cm) 20 10 Drained 0 2009 -10 2008 -20 2007 160 170 180 190 200 210 220 230 0 -15 Comparison of inter- Thaw Depth (cm) -30 annual variability of -45 160 170 180 190 200 210 220 230 NDVI to water table depth and thaw depth 0.70 0.60 in the drained section 0.50 0.40 0.30 for 2007 (pre- 0.20 treatment) and 2008 NDVI 0.10 Snowfall 0.00 -0.10 Snow-melt and 2009 (treatment -0.20 160 170 180 190 200 210 220 230 years) Day of Year
  • 16. 30 Water Table Depth (cm) 20 10 Control 0 2009 -10 2008 -20 2007 160 170 180 190 200 210 220 230 0 Dry year 2007 Thaw Depth (cm) -15 Comparison of inter- -30 annual variability of -45 160 170 180 190 200 210 220 230 NDVI to water table 0.70 depth and thaw depth 0.60 0.50 in the control section for 0.40 0.30 2007 (pre-treatment) NDVI 0.20 0.10 0.00 Snowfall and 2008 and 2009 -0.10 Snow-melt -0.20 (treatment years) 160 170 180 190 200 210 220 230 Day of Year
  • 17. 0.50 0.43 0.40 0.40 0.33 0.30 NDVI 0.20 0.10 0.00 11% surface 28% surface 83% surface water water water Standing surface water has a significant effect on reflectance spectra. This has profound effect on the NDVI values.
  • 18. The spectral range (400-1000nm) offers the ability to detect several things, including water. Using a spectral index (NDSWI) capable of capturing multi-dimensional surface hydrological dynamics can improve the sampling capabilities of this system NDSWI (Normalized Difference Surface Low Water Index) = (R490-R1000) (R490+R1000) High Quickbird 2002 Quickbird 2008 Goswami et. al – submitted, JGR Biogeosciences
  • 19. Summary and Conclusion • Changes in vegetation cover during the growing season was clearly detectable using surface reflectance. • Seasonal patterns of the NDVI values showed some differences with flooding. The flooded section showed bigger differences than the drained section. • The seasonal patterns in NDVI was least different for the control section among the years. • NDVI values were compromised by varying water cover (among other things), so simultaneously tracking NDVI and NDSWI might be a better approach in this landscape. • Alternatively, other methods like Spectral Mixture Analysis can probably help here.
  • 20. Acknowledgements • National Science Foundation (ASSP-0421588). • Barrow Arctic Science Consortium. • CH2M Hill Polar Field Services • Ukpeagvik Inupiat Corporation – landholder • Spectral Network. • UTEP Cybershare Center of Excellence. • Sergio Vargas, Perry Houser, Christian Andresen, Adrian Aguirre, Sandra Villarreal, Ryan Cody, David Lin. • Systems Ecology Laboratory, University of Texas at El Paso.