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Key Issues in the Use of NDVI for Land Degradation Assessment

  • 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

A substantial body of research has established the correlation between NDVI and aboveground biomass, and knowledge of the theoretical basis for using satellite-derived NDVI as a general proxy for vegetation conditions has advanced (Mbow et al. 2014; Pettorelli et al. 2005; Sellers et al. 1994). Reduction of primary productivity is a reliable indicator of the decrease or destruction of the biological productivity, particularly in drylands (Wessels et al. 2004; Li et al. 2004). NPP expressed in g of C m−2 years−1 and quantifies net carbon fixed by vegetation. According to Cao et al. (2003), NPP is “the beginning of the carbon biogeochemical cycle,” defined mathematically as in Eq. (5.1):
$$ \mathrm{N}\mathrm{P}\mathrm{P}=f\left(\mathrm{NDVI,,,,PAR,,,,fPAR,,,,aPAR,,,,LAI}\right) $$
where fPAR is the fraction of absorbed photosynthetic active radiation, aPAR is the absorbed photosynthetic active radiation, and LAI is the leaf area index. Changes in NPP or, rather, its proxy NDVI induced by land degradation can be measured using a range of remote sensing techniques so remote sensing has become an essential tool for global, regional, and national studies of land degradation (Anyamba and Tucker 2012; Bai et al. 2008; Bajocco et al. 2012; de Jong et al. 2011b; Field et al. 1995; Horion et al. 2014; Le et al. 2014; Prince and Goward 1995). Many approaches have been developed to estimate NPP, notably the Global Production Efficiency Model (GLO‐PEM) (Prince and Goward 1995), the Light-Use Efficiency (LUE) Model (Monteith and Moss 1977), the Production Efficiency Approach (Goetz et al. 1999; Goward and Huemmrich 1992), and the Sim‐CYCLE (Ito and Oikawa 2002). And models have been developed to estimate NPP directly from remotely sensed NDVI at a global scale. Running et al. (2004) offered Eq. (5.2):
$$ \mathrm{N}\mathrm{P}\mathrm{P}=\varSigma \left(\varepsilon \times \mathrm{N}\mathrm{DVI}\times \mathrm{P}\mathrm{A}\mathrm{R}-{\mathrm{R}}_{lr}\right)-{\mathrm{R}}_g-{\mathrm{R}}_m $$
where ε is the conversion efficiency; PAR is photosynthetically active radiation; R lr is 24-h maintenance respiration of leaves and fine roots; R g is annual growth respiration required to construct leaves, fine roots, and new woody tissues; and R m is the maintenance respiration of live cells in woody tissues. Drawing on this relationship, Bai et al. (2008) adopted an empirical relationship to translate NDVI trends to NPP trends for their proxy global assessment of land degradation (Eq. 5.3):
$$ {\mathrm{NPP}}_{\mathrm{MOD}17}\left( kg\kern0.24em C\kern0.24em h{a}^{-1}{\mathrm{year}}^{-1}\right)=1106.37\times \varSigma \mathrm{NDVI}-564.55 $$
where NPPMOD17 is the annual mean NPP derived from MODIS MOD17 Collection four data and sum NDVI is the 4-year (2000–2003) mean annual sum NDVI derived from GIMMS.

Keywords

Fine Root Photosynthetic Active Radiation Land Degradation Geographically Weighted Regression Woody Tissue 
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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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

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