Advertisement

Main Global NDVI Datasets, Databases, and Software

  • Genesis T. Yengoh
  • David Dent
  • Lennart Olsson
  • Anna E. Tengberg
  • Compton J. TuckerIII
Chapter
Part of the SpringerBriefs in Environmental Science book series (BRIEFSENVIRONMENTAL)

Abstract

Coarse spatial resolution datasets are invaluable at the global scale, but they lack the thematic and spatial detail required for habitat assessments at the country level and for finer-resolution assessments such as vegetation species distribution or high-quality forest-change monitoring. Mapping, monitoring, and assessments at the national and subnational level are performed using moderate-resolution sensors such as Landsat, ASTER, SPOT HRV, and IRS with spatial resolutions from 15 to 60 m. Newer, high-resolution optical sensors (5 m or better) provide enough spatial and spectral detail to discriminate between individual trees and, in some cases, species, but high-resolution imagery is prohibitively costly (see Annex 7) for many national governments and research institutions (Strittholt and Steininger 2007).

Keywords

Tropical Rainfall Measuring Mission Solar Zenith Angle Global Precipitation Climatology Project Tropical Rainfall Measuring Mission Data Interim Reanalysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Beck P, Karlsen S, Skidmore A, Nielsen L, Høgda K (2005) The onset of the growing season in northwestern Europe, mapped using MODIS NDVI and calibrated using phenological ground observations. In: 31st International Symposium on remote Sensing on Environment–Global Monitoring for Sustainability and Security, pp 20–24Google Scholar
  2. Beck HE, McVicar TR, van Dijk AI, Schellekens J, de Jeu RA, Bruijnzeel LA (2011) Global evaluation of four AVHRR–NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens Environ 115(10):2547–2563CrossRefGoogle Scholar
  3. Cepicky J, Becchi L (2007) Geospatial processing via internet on remote servers-PyWPS. OSGeo J 1(5):39–42Google Scholar
  4. De Beurs K, Henebry G (2005) A statistical framework for the analysis of long image time series. Int J Remote Sens 26(8):1551–1573CrossRefGoogle Scholar
  5. Dee D, Uppala S, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer P (2011) The ERA‐interim reanalysis: configuration and performance of the data assimilation system. Q J Roy Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  6. Fensholt R, Proud SR (2012) Evaluation of Earth Observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ 119:131–147. doi:http://dx.doi.org/10.1016/j.rse.2011.12.015
  7. Gallo K, Ji L, Reed B, Eidenshink J, Dwyer J (2005) Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data. Remote Sens Environ 99(3):221–231CrossRefGoogle Scholar
  8. Gentemann CL, Wentz FJ, Mears CA, Smith DK (2004) In situ validation of Tropical Rainfall Measuring Mission microwave sea surface temperatures. J Geophys Res: Oceans (1978–2012) 109(C4):C04021CrossRefGoogle Scholar
  9. Green RM, Hay SI (2002) The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications. Remote Sens Environ 79(2):166–175CrossRefGoogle Scholar
  10. Higginbottom TP, Symeonakis E (2014) Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sens 6(10):9552–9575CrossRefGoogle Scholar
  11. Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7(11):1417–1434CrossRefGoogle Scholar
  12. Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: GPCP version 2.1. Geophys Res Lett 36(17):L17808CrossRefGoogle Scholar
  13. James M, Kalluri SN (1994) The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int J Remote Sens 15(17):3347–3363CrossRefGoogle Scholar
  14. Jensen J (2007) Remote sensing of the environment. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  15. Khorram S, Koch FH, van der Wiele CF, Nelson SA (2012) Remote sensing. Springer, New YorkCrossRefGoogle Scholar
  16. Mather P, Koch M (2011) Computer processing of remotely-sensed images: an introduction. Wiley, ChichesterCrossRefGoogle Scholar
  17. Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52(3):588–606CrossRefGoogle Scholar
  18. Pedelty J, Devadiga S, Masuoka E, Brown M, Pinzon J, Tucker C, Roy D, Ju J, Vermote E, Prince S (2007) Generating a long-term land data record from the AVHRR and MODIS instruments. In: Proceedings of the IEEE 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, pp 1021–1025Google Scholar
  19. Pinzon J, Tucker C (2014) A non-stationary 1981–2012 AVHRR NDVI3G time series. Remote Sens 6(8):6929–6960CrossRefGoogle Scholar
  20. Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim G-K (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Climate 24(14):3624–3648CrossRefGoogle Scholar
  21. Rudolf B, Beck C, Grieser J, Schneider U (2005) Global precipitation analysis products of the GPCC. Climate monitoring—Tornadoklimatologie–Aktuelle Ergebnisse des Klimamonitorings, Germany, pp 163–170Google Scholar
  22. Scheftic W, Zeng X, Broxton P, Brunke M (2014) Intercomparison of seven NDVI products over the United States and Mexico. Remote Sens 6(2):1057–1084CrossRefGoogle Scholar
  23. Schneider, U., et al. 2008. Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre (GPCC), DWD, Internet Publikation, 112Google Scholar
  24. Sellers P, Tucker C, Collatz G, Los S, Justice C, Dazlich D, Randall D (1994) A global 1 by 1 NDVI data set for climate studies. Part 2: the generation of global fields of terrestrial biophysical parameters from the NDVI. Int J Remote Sens 15(17):3519–3545CrossRefGoogle Scholar
  25. Sietse O (2010) ISLSCP II FASIR-adjusted NDVI, 1982–1998. ISLSCP Initiative II Collection Data set Available on-line [http://daac.ornl.gov/] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, TN, USA
  26. Steiniger S, Hunter AJS (2013) The 2012 free and open source GIS software map—a guide to facilitate research, development, and adoption. Comput Environ Urban Syst 39:136–150. doi:http://dx.doi.org/10.1016/j.compenvurbsys.2012.10.003
  27. Strittholt J, Steininger M (2007) Trends in selected biomes, habitats, and ecosystems: forests. In: Strand H, Höft R, Strittholt J et al (eds) Sourcebook on remote sensing and biodiversity indicators, vol Technical Series No. 32. Secretariat of the Convention on Biological Diversity, Montreal, p 203Google Scholar
  28. Townshend JR, Masek JG, Huang C, Vermote EF, Gao F, Channan S, Sexton JO, Feng M, Narasimhan R, Kim D (2012) Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. Int J Digital Earth 5(5):373–397CrossRefGoogle Scholar
  29. Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498CrossRefGoogle Scholar
  30. Yin H, Udelhoven T, Fensholt R, Pflugmacher D, Hostert P (2012) How normalized difference vegetation index (ndvi) trends from advanced very high resolution radiometer (AVHRR) and système probatoire d’observation de la terre vegetation (spot vgt) time series differ in agricultural areas: an inner mongolian case study. Remote Sens 4(11):3364–3389CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Genesis T. Yengoh
    • 1
  • David Dent
    • 2
  • Lennart Olsson
    • 1
  • Anna E. Tengberg
    • 1
  • Compton J. TuckerIII
    • 3
  1. 1.Lund University Centre for Sustainability Studies - LUCSUSLundSweden
  2. 2.Chestnut Tree Farm, Forncett EndNorthfolkUK
  3. 3.Department of Hydrospheric and Biospheric SciencesNASA Goddard Space Flight CenterGreenbeltUSA

Personalised recommendations