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Global evaluation of four AVHRR–NDVI datasets: Intercomparison and assessment against Landsat imagery. Hylke E. Beck a, *, Tim R. McVicar b , Albert I.J.M. van Dijk b , Jaap Schellekens c , Richard A.M. de Jeu a , L. Adrian Bruijnzeel a a VU University Amsterdam, The Netherlands

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  1. Global evaluation of four AVHRR–NDVI datasets: Intercomparison and assessment against Landsat imagery Hylke E. Becka,*, Tim R. McVicarb, Albert I.J.M. van Dijkb, Jaap Schellekensc, Richard A.M. de Jeua, L. Adrian Bruijnzeela a VU University Amsterdam, The Netherlands b CSIRO Land and Water, Australia c Deltares, The Netherlands * E-mail: h.e.beck@vu.nl Corresponding publication: Beck, H.E., et al., Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery, Remote Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.012

  2. Outline • Introduction • AVHRR-NDVI dataset intercomparison • AVHRR-NDVI dataset validation • Conclusion/Summary

  3. (1) Introduction • NOAA’s AVHRR sensors operating since 1981 • Confused as to which global AVHRR-NDVI dataset to use:PAL, GIMMS, LTDR, FASIR, GVI, PAL-II, or …? • All are based on the AVHRR Global Area Coverage archive • Significant differences between these datasets! • Which one do I use? • Validation studies limited (e.g., regional, small sample size) • Idea: validate using forest cover change data? • Better idea: use FAO global database of Landsat samples

  4. (1) Introduction • Four global AVHRR-NDVI datasets: • PAL (8 km, 10 days) • GIMMS (8 km, 15 days) • LTDR V3 (8 km, 10 days) • FASIR (12 km, 10 days) • GIMMS the most popular • LTDR still in development

  5. (2) AVHRR-NDVI dataset intercomparison AVHRR-NDVI dataset intercomparison: • 1982-1999 • Annual means of ‘growing season’ months • Global assessment at 0.5° resolution • Where do the datasets agree/disagree • Median, variance, trend, and correlation (here only trend is discussed)

  6. (2) AVHRR-NDVI dataset intercomparison GIMMS dataset distinctly different patterns! Large differences in Congo and Sahel! Trends in desert areas!

  7. (2) AVHRR-NDVI dataset intercomparison • AVHRR-NDVI dataset intercomparison: • Average for 0.5° latitude bands • LTDR V3 overestimates variance 40°S-30°S • GIMMS has lowest trends • GIMMS higher trends in tundra • Positive trends over almost whole latitudinal range for all datasets

  8. (2) AVHRR-NDVI dataset intercomparison • AVHRR-NDVI dataset intercomparison: • Kruskal-Wallis test used for hypothesis of equal trends • Blue: similar trends • Red: different trends • Inconsistent trends in Africa and Europe • Highly consistent trends in Australia

  9. (2) AVHRR-NDVI dataset intercomparison • AVHRR-NDVI dataset intercomparison: • The most popular dataset (GIMMS) is also the most different • Greening almost the whole latitudinal range and in most regions for all datasets • Most greening in Europe • More favorable conditions globally for vegetation growth

  10. (3) AVHRR-NDVI dataset validation AVHRR-NDVI dataset validation: • FAO Landsat database of 20 x 20 km2 samples • 11,764 Landsat-5 samples covering all major land-cover types • Landsat suitable for validation • NDVI from bands 3 and 4 • Absolute-values comparison (see paper) • Temporal-change comparison • MODIS-NDVI for verification Every dot represents one or more Landsat samples!

  11. (3) AVHRR-NDVI dataset validation • AVHRR-NDVI dataset validation: • Landsat sample pairs • x-axis: AVHRR- or MODIS-NDVI change • y-axis: Landsat-NDVI change • Root Mean Square Difference (RMSD) indicates performance • GIMMS second best • MODIS best

  12. (3) AVHRR-NDVI dataset validation • AVHRR-NDVI dataset validation: • Higher RMSD in dense canopy land-cover types • NDVI saturation and non-linearity • Tropical forests: water vapor and clouds • Boreal forests: large SZA’s

  13. (3) AVHRR-NDVI dataset validation • AVHRR-NDVI dataset validation: • LTDR V3 most accurate in terms of absolute values (see paper) • GIMMS probably (!) most accurate in terms of temporal change • MODIS more accurate than all AVHRR-NDVI datasets, confirms method • Interesting: simple average of the AVHRR-NDVI datasets is better than GIMMS, information is lost by maximum-value compositing?

  14. (4) Conclusion/Summary • Significant differences in trends for almost half of the total land surface • Dataset choice has large implications • PAL and LTDR V3 lack calibration • GIMMS (the most popular dataset) is the most different • GIMMS probably has the best calibration • However, LTDR dataset still in development; may surpass GIMMS

  15. Thank you! Questions? Also check our publication, ask for a hardcopy: Beck, H.E., et al., Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery, Remote Sensing of Environment (2011), doi:10.1016/j.rse.2011.05.012

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