7 Land-use and Catchment Characteristics
141
Table 7.2. Overview of Vegetation Indices
Intrinsic Indices
Difference Vegetation Index
Ratio Vegetation Index
Normalized Difference Veg. Index
Normalized Difference Wetness Index
Green Vegetation Index
DVI = NIR-R
RVI = NIR
R
VI- NIR-R
ND - NIR+R
NDWI = SWIR - MIR
SWIR+ MIR
GVI = NIR + SWIR
R+MIR
Soil-line Related Indices
Perpendicular VI
Weighted difference VI
Soil Adjusted VI
Soil Adjusted RatiQ VI
Transformed Soil Adjusted VI
{
PVI = NIR-aR-b
v'1+a 2 '
NIRsoil = a Rsoil + b
NIR '1
WDVI=NIR- ~R
Rsoil
SAVI = (1 + L)(NIR - R)
NIR+R+L
SARVI =
NIR
R+b/a
TSAVI = _a_*~(_N_IR_-__ a_*_R_--:-b
R+a*NIR-a*b
Leaf Area Index
Relation between Leaf Area Index and
SAVI
where a = 0.96916, b = 0.084726, L = 0.5
and Cl = 0.69, C2 = 0.59, C3 = 0.91
such cases, deterministic methods are not sufficient. A probabilistic approach, however, may be able to describe the spectral variations within classes and to minimize
the risk of erroneous class assignments.
Classification determines a thematic class from a user-defined set for each image
pixel. The choice is made on the basis of reflection measurements stored in that
pixel. The collection of measurements in one pixel is called measurement vector or
feature vector. With M spectral bands the feature vector has M components and
corresponds to a point in an M -dimensional feature space. The task of classification
is to assign a class label to each feature vector, which means to subdivide the feature