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Based on the understanding of how vegetation reflectance differs through the electromagnetic spectrum the use of remote sensing applications for distinguishing between agricultural crop types and internal crop characteristics has been extensively researched during the past decade (Weigand et al., 1991; Cloutis et al., 1996; Mogensen et al., 1996; Cloutis et al., 1999; Metternicht et al., 2000; Senay et al., 2000; Thenkabail et al., 2000; McNairn et al., 2002). The trends being developed between specific crop types, maturity, nutrient levels and their reflectance values in spectral bands and relationship to vegetation indices (VI) are becoming well known and useful when limited ground truth data are available (Senay et al., 2000). An explanation of VI followed by examples of their use in agricultural application provides an indication that remote sensing of vegetation attributes could depict landscape variability and be a useful method of collecting vegetation data and an appropriate layer within the LMU classification technique.

2.3.2.2.1 Vegetation Indices

There is a wide range of image transformation/enhancement techniques available, such as image reduction and magnification, contrast enhancements, band ratioing, spatial filtering and special filtering, which includes principal components analysis and vegetation indices (VI). A VI is a mathematical combination of several bands of remote sensing data that utilises the significant differences in reflectance of vegetation in the blue, green, red and near-infrared wavebands (as explained in the previous section). The index is typically a sum, difference, ratio or other linear combination that reduces multi-band observations to a single numerical index (Weigand et al., 1991). VI are a simple tool for exploring or quickly evaluating the state of vegetation over large areas. They enhance the spectral contrast of vegetation and minimise the influence of other factors (e.g. topography, illumination). Their usefulness depends on the empirical relations that may be found between them and variables of interest, such as vegetation stress and biomass.

Jackson and Huete (1991) classify VI into two groups; ratios and linear combinations and a third group can be added called orthogonal transformations. Ratio VI or slope- based indices (Thiam and Eastman, 2003), represent simple arithmetic combinations that focus on the contrast of the reflectance in bands (Table 2.8). Linear VI or distance based indices (Thiam and Eastman, 2003), are designed to eliminate the effect of background soil brightness and detect the features only of vegetation (Table 2.8). Orthogonal transformations, undertake a transformation of the available bands to form a new set of uncorrelated bands in which a green vegetation index can be defined (Thiam and Eastman, 2003). Thiam and Eastman (2003) state that the link between orthogonal transformation techniques is that they all express green vegetation through the development of their second component. Some examples of VIs and their authors are listed in Table 2.8.

Table 2.8 Examples of Vegetation Indices

Name Acronym Group Author

Ratio Ratio Ratio (Rouse et al., 1974) Normalised Difference

Vegetation Index

NDVI Ratio (Rouse et al., 1973) Transformed Vegetation

Index

TVI Ratio (Deering et al., 1975) Normalised Difference

Vegetation Index-Green

NDVIgreen Ratio (Gitelson and Merzlyak, 1997)

Photosynthetic Vigour Ratio

PVR Ratio (SpecTerra Services, 1999)

Plant Pigment Ratio PPR Ratio (SpecTerra Services, 1999)

Perpendicular Vegetation Index

PVI Linear (Richardson and

Weigand, 1977) Perpendicular

Vegetation Index 3

PVI3 Linear (Qi et al., 1994) Soil Adjusted Vegetation

Index

SAVI Linear (Huete, 1988) Weighted Difference

Vegetation Index

WDVI Linear (Richardson and Weigand, 1977)

Atmospherically Resistant Vegetation Index

ARVI Linear (Kaufman and Tanré, 1992)

Soil Adjusted and Atmospheric Resistant Vegetation Index

SARVI Linear (Kaufman and Tanré, 1992)

Principal Components Analysis

PCA Orthogonal (Singh and Harrison, 1985)

Green Vegetation Index of the Tasseled Cap

GVI Orthogonal (Kauth and Thomas, 1976)

The underlying premise of using remote sensing to monitor crop condition is that important crop parameters related to growth and yield are manifested in the multi- spectral reflectance of crop canopies (Bauer, 1985). The Leaf Area Index (LAI), representing the ratio of leaf surface area to ground area, is the fundamental canopy parameter in two basic physiological processes: photosynthesis and evapotranspiration, which are most dependant on solar radiation (Bauer, 1985). Most models of crop growth and yield require an estimate of green LAI, and therefore the strong relationship of infrared reflectance to LAI of crop canopies is the basic mechanism for linking multispectral remote sensing data to crop growth and condition (Bauer, 1985; Clevers, 1997).

In remote sensing a common approach for measuring or monitoring crop growth is the correlation of vegetation indices or ratios with crop variables, such as percentage of vegetation cover and LAI (Moran et al., 1997). Moran et al. (1997) suggest that measurements of crop properties at sample sites combined with multi-spectral imagery could produce accurate, timely maps of crop characteristics for defining precision management units. Some examples of works that have shown relationships between remotely sensed data and crop attributes are detailed hereafter.

Senay et al. (2000) used high resolution multi-spectral data to identify corn and soybean crops at various growth stages. They used multi-spectral sensor (MSS) data, with 12 spectral bands whose wavelength range included the visible, near-infrared and mid-infrared and was obtained at 1m resolution on four occasions representing different growth stages. Spectral analysis of the individual bands and three vegetation indices were performed. The correlation analysis between the MSS and ground reference data highlighted that generally the near-infrared bands were more highly correlated than were the visible or the mid-infrared band. On the individual acquisition dates the VI performed better than the red and green bands, but similar to the near-infrared band. However, the VI correlated better with plant height and plant nitrogen than any of the individual bands. The strongest correlations were between the NDVI and plant height and nitrogen content in the leaves.

NDVI has shown good correlation with plant growth variables (i.e. height, LAI, biomass and yield); in particular, it is highly related to yield and could be used to estimate yield-based within field management zones (Yang and Anderson, 1996). However, Corner et al. (1998) used NDVI from Landsat TM and high resolution multi-spectral video to estimate yield at different scales. Reasonable correlations were achieved at a regional scale (paddock scale) using Landsat TM. However, high resolution multi-spectral video, at a local scale (sub-paddock scale), resulted in poor correlations. The regional scale achievement was attributed to a considerable degree of spatial and temporal averaging, such as varying sowing dates and localised weather events. At a local scale other factors that cause variation in crop production predominate, and therefore high resolution NDVI measurements may be best used as a diagnostic tool to determine these factors, rather than as a yield predictor.

Previous investigations distinguishing internal canola crop variations using passive remote sensors (Cloutis et al., 1996; Mogensen et al., 1996; Cloutis et al., 1999) provide support to the hypothesis that variations in canola growth can be depicted by remote sensed imagery. For instance, Mogensen et al. (1996) investigated the use of a spectral reflectance index for determining early water stress on canola grown under controlled field conditions (lysimeter tanks). The reflectance index (RI) being defined as the ratio of incoming and reflected infrared radiation in the range of 740 to 820nm, to the incoming and reflected photosynthetically active radiation between 400 and 700nm. They simulated a drought type effect within the trial and used a relative reflectance index (RRI) defined as the ratio of the reflectance index of the droughted crops to the fully irrigated reference crops to analyse the effect of water stress on RI. The authors concluded that the RRI was an index sensitive to water stress, seeming most appropriate in the vegetative stage of growth, as changes in spectral response of crop surfaces due to senescence or changes in architecture due to leaf wilting of the crop may change the RI values.

The above works have highlighted the use of VIs for agricultural applications, in particular depicting variations in crop growth and in some cases suggesting their use for determining management units. Five VIs namely, NDVI, NDVIgreen, SAVI, PVR and PPR (Table 2.8) will be used to analyse relationships with crop growth variables in this research. The aim is to determine if they are able to depict landscape variability and in turn be useful inputs in the LMU classification. Thus, the selected VI are explained in detail hereafter.