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Ph.D. Dissertation Defense Warming-enhanced Plant Growth in the North Since 1980s: A Greener Greenhouse? Liming Zhou Department of Geography, Boston University Dissertation Committee Ranga B. Myneni Robert K. Kaufmann Yuri Knyazikhin Nathan Phillips Compton J. Tucker 1 of 43

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Summary of Presentation 1.Motivation 2.Data 3.Quality of Satellite Data 4.Changes in Northern Vegetation Activity 5.Spatial Pattern of Changes in Vegetation 6.Drivers for Changes 7.Contributions of Research 8.Future Directions 2 of 43

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Has Vegetation Responded to Climate Change? Pronounced warming in northern high latitudes Earlier disappearance of snow in spring Increased precipitation in northern high latitudes Increased concentration of atmospheric CO 2 Changes in Climate Increased productivity through: - enhanced photosynthesis - enhanced nutrient availability Changes in Vegetation 3 of 43

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i. greatest warming in winter and spring ii. continental difference: overall warming in Eurasia and smaller warming or cooling trends in North America coolingwarming Monthly land surface climate data (1981-1999) 1. NOAA precipitation: 2.5 x2.5 2. GISS temperature: 2 x2 Data 4 of 43

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Satellite NDVI data at 8 km resolution 1. GIMMS 15-day composite NDVI (07/81-12/99) 2. Pathfinder AVHRR Land 10-day composite NDVI (07/81- 09/94) Solar zenith angle (SZA) from GIMMS and Pathfinder data Monthly stratospheric aerosol optical depth (AOD) reported as zonal means A land cover map at 8 km resolution 5 of 43

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Factors that may contaminate long-term satellite measures: 1. calibration uncertainties (satellite drift and changeover) 2. atmospheric and bidirectional effects (aerosol, vapor, etc) 3. soil background effects Methods that help to reduce some non-vegetation effects 1. Maximum NDVI compositing 2. Spatial and temporal aggregations 3. Empirical methods Changes in SZAChanges in AOD El ChichonMt. Pinatubo 6 of 43

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Are AVHRR Satellite Measures of NDVI Contaminated by Satellite Drift and Changeover? Kaufmann, R. K., Zhou, L., Knyazikhin, Y., Shabanov, N.V, Myneni, R.B., and Tucker, C.J., Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens. 38: 2584-2597, 2000. 7 of 43

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Theoretical Analysis The effect of changes in SZA on NDVI can be examined from radiative transfer equation in vegetation media. NDVI = f (SZA, ) Sensitivity experiments Result: NDVI is minimally sensitive to SZA changes and this sensitivity decreases as leaf area increases. 8 of 43

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Empirical Analysis A statistically meaningful relation between NDVI and SZA? 1. Ordinary least squares (OLS) 2. Cointegration analysis (VECM) i. spurious regression results? i. a cointegrating relation? ii. the statistical ordering of this relation? 9 of 43

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Land cover type A statistically meaningful relation? OLSCointegration analysis causal order Evergreen needleleaf forests no Evergreen broadleaf forests yesno Deciduous needleleaf forests no Deciduous broadleaf forests no Mixed forests no Woodlands no Wooded grasslands/shrubs yes SZA NDVI Closed bushlands/shrublands yes SZA NDVI Open shrublands yes SZA NDVI Grasses yes SZA NDVI Relationship between NDVI and SZA 10 of 43

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Conclusions Theoretical and empirical analyses indicate that NDVI is minimally sensitive to SZA changes and this sensitivity decreases as leaf area increases. Using OLS can generate spurious regressions because of the nonstationary properties of time series. The AVHRR NDVI do not cointegrate with satellite drift and changeover for dense vegetation types. 11 of 43

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Has Northern Hemisphere Vegetation Changed? Zhou, L., Tucker, C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V, and Myneni, R.B., Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. 106, 20069-20083, 2001. 12 of 43

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Study Pixels Vegetated pixels (defined by NDVI) between April to October 1. Minimize the SZA effect 2. Reduce the soil background contribution (snow, barren and sparsely vegetated areas) 3. Use data from the same pixels in the entire analysis. Map of vegetated pixels 13 of 43

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Changes in Vegetation Activity Changes in vegetation photosynthetic activity can be characterized by 1. changes in growing season 2. changes in NDVI magnitude Increases in NDVI magnitudeIncreases in growing season JanDecJul Aug Increase NDVI JanDecJul Aug earlier spring delayed fall NDVI 14 of 43

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Increases in Growing Season (Increased by 18 Days) (Increased by 12 Days) 11.9 days/18 yrs (p

Define the persistence index (PI) of NDVI increase: PI = PI(1) + PI(2) + PI(3) + PI(4) + PI(5) +PI(6), 0 PI 6 PI (i) = 1 if Trend i > 80% Trend i-1 0 otherwise Year PI (1) = 1 if Trend 1 > 80% Trend 0 0 otherwise 82 84 87 89 91 93 95 97 99 P(1), PI(2), PI(3), P(4), PI(5), PI(6) Trend 0 (82-87) Trend 1 (82-89) Trend 2 (82-91) Trend 3 (82-93) Trend 4 (82-95) Trend 5 (82-97) Trend 6 (82-99) 20 of 43

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Persistence index of NDVI 21 of 43

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Consistency between NDVI and Temperature R=0.79 (p