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Part of the book series: SpringerBriefs in Environmental Science ((BRIEFSENVIRONMENTAL))

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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).

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Notes

  1. 1.

    Initiatives such as those by Michael de Smith and Paul Longley of University College London, and Mike Goodchild of University of California, Santa Barbara, provide information on the main activity for which each software product is designed, their license status (commercial or free), as well as links to their access and further information: http://www.spatialanalysisonline.com/software.html. The Wikipedia page that compares geographic information systems and remote sensing software in terms of license status, operating system, and other operational specifications can be found at: http://en.wikipedia.org/wiki/Comparison_of_geographic_information_systems_software.

References

  • 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 20–24

    Google Scholar 

  • 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):2547–2563

    Article  Google Scholar 

  • Cepicky J, Becchi L (2007) Geospatial processing via internet on remote servers-PyWPS. OSGeo J 1(5):39–42

    Google Scholar 

  • De Beurs K, Henebry G (2005) A statistical framework for the analysis of long image time series. Int J Remote Sens 26(8):1551–1573

    Article  Google Scholar 

  • 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):553–597

    Article  Google Scholar 

  • 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

  • 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):221–231

    Article  Google Scholar 

  • 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):C04021

    Article  Google Scholar 

  • 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):166–175

    Article  Google Scholar 

  • Higginbottom TP, Symeonakis E (2014) Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sens 6(10):9552–9575

    Article  Google Scholar 

  • Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7(11):1417–1434

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: GPCP version 2.1. Geophys Res Lett 36(17):L17808

    Article  Google Scholar 

  • 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):3347–3363

    Article  Google Scholar 

  • Jensen J (2007) Remote sensing of the environment. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • Khorram S, Koch FH, van der Wiele CF, Nelson SA (2012) Remote sensing. Springer, New York

    Book  Google Scholar 

  • Mather P, Koch M (2011) Computer processing of remotely-sensed images: an introduction. Wiley, Chichester

    Book  Google Scholar 

  • Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52(3):588–606

    Article  Google Scholar 

  • 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 1021–1025

    Google Scholar 

  • Pinzon J, Tucker C (2014) A non-stationary 1981–2012 AVHRR NDVI3G time series. Remote Sens 6(8):6929–6960

    Article  Google Scholar 

  • 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):3624–3648

    Article  Google Scholar 

  • 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 163–170

    Google Scholar 

  • Scheftic W, Zeng X, Broxton P, Brunke M (2014) Intercomparison of seven NDVI products over the United States and Mexico. Remote Sens 6(2):1057–1084

    Article  Google Scholar 

  • Schneider, U., et al. 2008. Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre (GPCC), DWD, Internet Publikation, 112

    Google Scholar 

  • 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):3519–3545

    Article  Google Scholar 

  • 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

  • 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

  • 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 203

    Google Scholar 

  • 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):373–397

    Article  Google Scholar 

  • 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):4485–4498

    Article  Google Scholar 

  • 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):3364–3389

    Article  Google Scholar 

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Yengoh, G.T., Dent, D., Olsson, L., Tengberg, A.E., Tucker, C.J. (2015). Main Global NDVI Datasets, Databases, and Software. In: Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales. SpringerBriefs in Environmental Science. Springer, Cham. https://doi.org/10.1007/978-3-319-24112-8_8

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