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Remote Sensing Phenology

Remote Sensing Phenology Bradley Reed Principal Scientist USGS National Center for Earth Resources Observation and Science Sioux Falls, SD. Remote Sensing Phenology. Potential to provide wall-to-wall phenology estimates Potential to provide information on medium-term trends in phenology

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Remote Sensing Phenology

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  1. Remote Sensing PhenologyBradley ReedPrincipal ScientistUSGS National Center forEarth Resources Observation and Science Sioux Falls, SD

  2. Remote Sensing Phenology • Potential to provide wall-to-wall phenology estimates • Potential to provide information on medium-term trends in phenology • Potential to provide improved phenology estimates to global models (climate, ecosystem, etc.)

  3. Remote Sensing Phenology Background • High spatial resolution sensors • Landsat • 1972-present, 16-day repeat cycle, 30- 25-m resolution • SPOT • 1986-present, 10m Pan, 20m multispectral • IKONOS • 1999-present, high resolution; 1m Pan, 4m multispectral, local coverage

  4. Sensors for Phenological Studies • Hi-resolution sensors history • Large Area Crop Inventory Experiment (LACIE) • AgRISTARS • temporal profiles • crop calendars • Limited by temporal resolution of sensors

  5. Remote Sensing Phenology Background • High temporal resolution sensors • AVHRR • 1981-present; (8-km) global coverage • 1989-present; (1-km) conterminous US • SPOT Vegetation • 1998-present; 1-km resolution • Envisat MERIS • 2002; 300m resolution • MODIS • 2000-present; 250m, 500m, 1-km resolution

  6. Vegetation Indices for R.S. Phenology • Normalized Difference Vegetation Index (Deering and others 1976) • NDVI = (NIR-Red)/(NIR+Red) • Soil Adjusted Vegetation Index (Huete 1988) • SAVI = (1+L)(ρnir-ρred)/(ρnir+ρred+L) • ρ – reflectances • L – adjustment factor for red and NIR extinction through canopy

  7. Vegetation Indices • NDVI • Time-tested • Saturates at high values • Coupled to red band reflectance, photosynthetic capacity (fPAR, fractional green cover) • SAVI (EVI) • Coupled to infrared band reflectance; structural canopy parameters (LAI, biomass) • More stable, higher dynamic range at high end, but less dynamic range at low end

  8. Time-series VI measurements

  9. Atmospheric and Sensor noise • Cloud contamination • throughout composite period • sub-pixel clouds • Illumination angle and viewing geometry • Atmospheric aerosols • Water vapor, haze, other contaminants • Sometimes unreliable calibration • All of the above usually reduce NDVI values

  10. Example contaminated pixel Time periods affected by Atmospheric contamination

  11. Noise reduction methods • Maximum value compositing • reduces noise, but still affected by persistent clouds/haze • BISE (Viovy and others 1992) • FASIR (Sellers and others 1994) • Weighted least-squares regression (Swets and others 1998) • Other temporal smoothers • polynomial; FFT; compound mean/median

  12. Example smoothed NDVI pixel Critical to retain temporal nuances

  13. Identifying start of season (SOS) • Key to seasonal characterization • other seasonal metrics depend on SOS • looking for a trend shift toward high values • Methods for identifying SOS • Thresholds • Inflection points • Curve derivation

  14. SOS – Threshold Method • Pre-defined threshold (Lloyd 1990) • e.g., NDVI = 0.099 • Half-maximum (White and others 1997) • mid-point between minimum and maximum NDVI • 10% amplitude (Jönsson and Eklundh 2002)

  15. SOS – Inflection Point Method • Inflection point • Badhwar (1984) • Time derivative transition • Moulin and others (1997) • Maximum curvature • Zhang and others (2001)

  16. SOS – Curve Derived Method • Delayed Moving Average • Reed and others (1994) • Time of Largest Increase • Kaduk and Heimann (1996)

  17. SOS – Comparisons

  18. What is SOS Measuring? • DMA – first sustained flush of greenness? • Half-max – primary leaf expansion? • Greatest Increase – early season growth peak (perceived spring)? • Inflection pt. – environmental conditions preceding first flush? …what biophysical phenomena should be represented? Application specific.

  19. Satellite SOS vs. GPP estimates (USDA-Agriflux towers) Mandan, ND Days offset n = 13 x = 2.23 std = 8.21 +5 -10 1999 2000 Woodward, OK = Satellite SOS -5 -6 +1 1999 2000 2001

  20. Additional metrics can be derived from the annual VI cycle Seasonal integrated NDVI

  21. 1989 1991 1990 1992 1993 1995 1996 1994 1999 2000 1998 1997 Annual summaries of the metrics can be created to assess interannual trends 2001

  22. Regions with significant trends in annual greenness: Is the seasonally integrated greenness from 1989 – 2003 increasing or decreasing? Seasonal Integrated Greenness

  23. Regions with Significant Trends in Annual Greenness: Is the slope (b) of best-fit line significantly different from 0? b = 2.12 standard error (s) = 0.46 t-test: t = b/s = 4.60 t-distribution at 0.05 level of significance and df = 11 = 2.201, 4.60 > 2.201, therefore is significant

  24. SOST 1989-2003 Earlier SOS Later SOS

  25. EOST 1989-2003 Earlier EOS Later EOS

  26. Trends in Duration of growing season 1989-2003 Shorter Duration Longer Duration

  27. Trends in Total NDVI 1989-2003 Decreasing Greenness Increasing Greenness

  28. Analysis of Trends Driving Forces Fire recovery Land use change Land use practice Biological succession Short and long-term climate change Yellowstone National Park

  29. Issues in Satellite Phenology – non-vegetation related environmental conditions ■ Snow ■ Soil moisture ■ Plant litter ■ Atmospheric perturbations

  30. Issues in Satellite Phenology – “unusual” pixels Bimodal growing seasons Poorly defined seasons Evergreen systems with differing seasonality

  31. Issues in Satellite Phenology- heterogeneity Satellite sensors integrate the constituents of each pixel, therefore we may not be estimating the phenology of any single plant type, but rather the “sum” of the pixel constituents 1km 32m resolution 1km 1km 8m resolution 16m resolution

  32. Issues in Satellite Phenology- field verification • Traditional field measures are plant/plant type specific • Pixel heterogeneity makes scaling up from plant specific data difficult • Remote sensing optimized approach needs to be considered

  33. Conclusions • Remote Sensing can generate consistent, objective estimates of phenology • start, end, peak, duration of growing season • Variety of approaches are available which may be measuring fundamentally different phenomena • stages of plant growth, environmental conditions preceding growing season, etc.

  34. Conclusions Improvements/understanding of estimates are needed • What factors are influencing VI signals and resulting phenology estimates? • Field validation is difficult, but critical • Users beware • Carefully consider which approach is most appropriate for particular applications

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