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 Potential to provide improved phenology estimates to global models (climate, ecosystem, etc.)
Remote Sensing Phenology Background High spatial resolution sensors Landsat 1972-present, 16-day repeat cycle, 3-25-m resolution SPOT 1986-present, 1m Pan, 2m multispectral IKONOS 1999-present, high resolution; 1m Pan, 4m multispectral, local coverage
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
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 22; 3m resolution MODIS 2-present; 25m, 5m, 1-km resolution
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)(ρ nir -ρ red )/(ρ nir +ρ red +L) ρ reflectances L adjustment factor for red and NIR extinction through canopy
NDVI Vegetation Indices 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
Time-series VI measurements
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
Example contaminated pixel Time periods affected by Atmospheric contamination
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
Example smoothed NDVI pixel Critical to retain temporal nuances
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
SOS Threshold Method Pre-defined threshold (Lloyd 199) e.g., NDVI =.99 Half-maximum (White and others 1997) mid-point between minimum and maximum NDVI 1% amplitude (Jönsson and Eklundh 22) NDVI.6.4.2 Time Pixel value Half-max Threshold 1%
SOS Inflection Point Method Inflection point Badhwar (1984).6 Pixel value Inflection pt. Time derivative transition Moulin and others (1997) NDVI.4.2 Time Maximum curvature Zhang and others (21)
SOS Curve Derived Method Delayed Moving Average Reed and others (1994).6 Pixel value DMA Largest Increase Time of Largest Increase NDVI.4.2 Kaduk and Heimann (1996) Time
SOS Comparisons.6 Pixel value DMA Largest Increase Inflection Point Threshold Half maximum NDVI.4.2 Time
Average SOS deciduous forest Average SOS evergreen forest Average SOS mixed forest Day of year 15 1 5 1997 1998 1999 Year Inf l. Pt. Gr. Inc. DMA Day of year 15 1 5 1997 1998 1999 Year Inf l. Pt. Gr. Inc. DMA Day of year 1 5 1997 1998 1999 Year Inf l. Pt. Gr. Inc. DMA Average SOS shrubland Average SOS grasslands Average SOS pasture Day of year 15 1 5 1997 1998 1999 Inf l. Pt. Gr. Inc. DMA Day of year 15 1 5 1997 1998 1999 Inf l. Pt. Gr. Inc. DMA Day of year 1 8 6 4 2 1997 1998 1999 Infl. Pt. Gr. Inc. DMA Year Year Year Average SOS row crops Average SOS small grains Average SOS All land cover types Day of year 15 1 5 1997 1998 1999 Infl. Pt. Gr. Inc. DMA Day of year 15 1 5 1997 1998 1999 Infl. Pt. Gr. Inc. DMA Day of year 15 1 5 1997 1998 1999 Infl. Pt. Gr. Inc. DMA Year Year Year
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.
5 1 15 2 25 3 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 31 32 33 34 35 36 -.2 -.1.1.2.3.4.5.6.7.8 GPP avpg NDVI 2 4 6 8 1 12 14 16 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 31 32 33 34 35 36 -.1.1.2.3.4.5.6.7.8 GPP avpg NDVI 5 1 15 2 25 3 35 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 31 32 33 34 35 36.1.2.3.4.5.6.7 GPP avpg NDVI 5 1 15 2 25 3 35 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 31 32 33 34 35 36.1.2.3.4.5.6.7 GPP avpg NDVI 5 1 15 2 25 3 35 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 31 32 33 34 35 36.1.2.3.4.5.6 GPP avpg NDVI Woodward, OK Mandan, ND 1999 2 21 Days offset n = 13 x = 2.23 std = 8.21-1 +5-5 -6 +1 1999 2 = Satellite SOS Satellite SOS vs. GPP estimates (USDA-Agriflux towers)
Additional metrics can be derived from the annual VI cycle Seasonal integrated NDVI
1989 199 1991 1992 1993 1994 1995 1996 1997 21 1998 1999 2 Annual summaries of the metrics can be created to assess interannual trends
Regions with significant trends in annual greenness: Is the seasonally integrated greenness from 1989 23 increasing or decreasing? 7 6 Seasonal Greenness 5 4 3 2 1 1989 1991 1993 1995 1997 1999 21 Seasonal Integrated Greenness
Regions with Significant Trends in Annual Greenness: Is the slope (b) of best-fit line significantly different from? Seasonal Greenness 7 6 5 4 3 2 1 1989 1991 1993 1995 1997 1999 21 b = 2.12 standard error (s) =.46 t-test: t = b/s = 4.6 t-distribution at.5 level of significance and df = 11 = 2.21, 4.6 > 2.21, therefore is significant
Earlier SOS Later SOS SOST 1989-23
Earlier EOS Later EOS EOST 1989-23
Trends in Duration of growing season 1989-23 Shorter Duration Longer Duration
Decreasing Greenness Increasing Greenness Trends in Total NDVI 1989-23
Analysis of Trends Driving Forces Fire recovery Land use change Land use practice Biological succession Short and long-term climate change Yellowstone National Park
Issues in Satellite Phenology non-vegetation related environmental conditions Snow Soil moisture Plant litter Atmospheric perturbations
Issues in Satellite Phenology unusual pixels NDVI.25.2.15.1.5 Shrubland NDVI.6.5.4.3.2.1 Double-crop 1 12 23 34 45 56 67 78 89 Year 1 Year 2 Year 3 Year 41 Poorly defined seasons 1 11 21 31 41 51 61 71 81 91 Year 1 Year 2 Year 3 Year 4 Bimodal growing seasons 11 Evergreen Evergreen.8.8.6.6 NDVI.4.2 NDVI.4.2 1 11 21 31 41 51 61 71 81 91 Year 1 Year 2 Year 3 Year 4 11 1 11 21 31 41 51 61 71 81 91 Year 1 Year 2 Year 3 Year 4 11 Evergreen systems with differing seasonality
Issues in Satellite Phenology- heterogeneity 1km 32m resolution 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 1km 16m resolution 8m resolution
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
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.
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