Anna E. Tengberg

Applications of NDVI for Land Degradation Assessment

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).

Key Issues in the Use of NDVI for Land Degradation Assessment

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):

Main Global NDVI Datasets, Databases, and Software

\mathrm{N}\mathrm{P}\mathrm{P}=f\left(\mathrm{NDVI,,,,PAR,,,,fPAR,,,,aPAR,,,,LAI}\right)

World Water Week 2015

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):

Experts’ Opinions on the Use of NDVI for Land Degradation Assessment

\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

Development of Land Degradation Assessments

where e 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):