Applications of NDVI for Land Degradation Assessment

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


In the late 1960s, several researchers began using red and near-infrared reflected light to study vegetation (Pearson and Miller 1972). In the late 1960s, ratios of red and near-infrared light were used to assess turf grass condition and tropical rain forest leaf area index (Birth and McVey 1968; Jordan 1969). Compton Tucker was the first to use it for determining total dry matter accumulation, first from hand-held instruments (Tucker 1979), and then from NOAA AVHRR satellite data (Tucker et al. 1981, 1985), demonstrating that the growing season integral of frequent NDVI measurements represented the summation of photosynthetic potential as total dry matter accumulation. Starting in July 1981, a continuous time series of global NDVI data at a spatial resolution of 8 km has been available from the AVHRR instrument mounted on NOAA weather satellites. Soon, researchers realized the value of NDVI time-series remote sensing (Goward et al. 1985; Justice et al. 1985; Townshend et al. 1985; Tucker et al. 1985). This early work was the spur for development of the higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The application of satellite NDVI data has blossomed into many fields of natural resources investigation (see Annex 1). One particular appeal of remote sensing in the study of large geographic areas, or at multiple times over the year(s), is the potential for cost savings (Pettorelli 2013). We examine the use of NDVI in research on land-use and land-cover change, drought, desertification, soil erosion, vegetation fires, biodiversity monitoring and conservation, and soil organic carbon (SOC).


Soil Organic Carbon Soil Erosion Enhance Vegetation Index Normalize Difference Water Index Water Erosion Prediction Project 
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.


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© 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

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