10 
Ecosystem types could not be distinguished with a 
sufficient accuracy by unsupervised classification, and 
supervised classification was therefore essential at this 
stage of the exercise. 
EXTENDING THIS METHODOLOGY - 
CHALLENGES 
Such a methodology can in principle be extended 
to cover all of India and a variety of natural resources. 
However, while conceptualizing such an activity, 
several scientific challenges come to mind, which need 
to be addressed before beginning this exercise. To begin 
with, the extent to which a remote sensing based 
classification of LSE types can provide information on 
the distribution of other natural resources like biomass, 
land erosion or soil quality needs to be assessed. The 
first step is to decide on an appropriate set of land and 
water cover types for the country. This set should fulfil 
the following requirements : 
1) An observer in the field should be in a position to 
assign any particular locale to a particular land/ 
water cover type on the basis of a pre-defined set 
of criteria such that inter-observer variation in such 
assignment is within acceptable levels. 
2) It should be possible to.assign a pixel characterized 
by certain spectral signature to a particular cover 
type on the basis of either visual interpretation, 
supervised or unsupervised classification so that 
the resultant map is within some acceptable degree 
of error in relation to the ground truth. 
3) The different land / water cover types should differ 
from each other significantly enough in the 
distribution of the natural resource of interest that 
they harbor, so that assignment of a cover type to a 
given locale carries useful information on the 
likely occurrence of this resource. 
These raise questions of interest from a statistical 
perspective. The number of land/water cover types 
needs to be defined optimally as increasing or 
decreasing this number would have advantages and 
disadvantages. As the number of cover types is 
increased, keeping the resolution of the remotely sensed 
data constant, inter-observer variation in assigning 
locales to specific cover types will increase 
correspondingly (McGwire, 1992; Fisher, 1994). Inter 
observer variability will of course depend upon the 
fineness with which one wishes to discriminate between 
LSE types on the ground. To quote two extreme cases - 
classifying a landscape into just two categories, forest 
and non-forest, will result in practically no inter 
observer variability. A detailed classification into 
evergreen, disturbed evergreen and highly disturbed 
secondary evergreen will definitely have higher inter 
observer variability during field assignment, as well as 
increase the classification accuracy of a remote-sensing 
based map. 
However, this does not necessarily mean that as 
broad as possible a classification scheme be adopted. As 
the list of cover types for a defined area increases, the 
level of detail in the classification will increase, and the 
correspondence between field determined levels of 
resource distribution and specific cover types will 
correspondingly increase. Some compromise has to be 
made considering all of these factors, to decide the level 
of detail to which LSE type classification can be taken. 
The challenge is to determine that set of land/water 
cover types for which this tradeoff results in tightest 
connections between cover type and level of resource. 
Of course, all of this depends on the resolution 
(spatial, spectral and temporal) of remote sensors being 
used. Increase in sensor resolution lead to decrease in 
classification errors (Moody and Woodcock, 1994), and 
hence allow for the discrimination of a larger number of 
land/water cover types. In our studies, for example, we 
have used IRS LISS-2 sensors with a spatial resolution 
of 36.25 by 36.25 m, to carry out detailed mapping in 
twelve landscapes distributed across the Ghats, 10-50 sq. 
km in area, using supervised classification. In initial 
mapping exercises, we initially attempted to map these 
twelve landscapes into a total of thirty-five cover types 
(between fifteen and twenty-five land and water cover 
types per landscape), for Angiosperm diversity 
assessment. High classification errors resulted in our 
reducing the set of cover types to twenty-four (between 
five and nine per landscape). From our experience with 
these landscapes, and discussions with a large number of 
experienced field investigators working in the Western 
Ghats, we prepared a list of about hundred land and 
water cover types for mapping the entire Western Ghats 
and west coast of India, an area of over 170,000 km". 
Based on this list we have carried out mapping exercises 
in additional landscapes. However, with the advent of 
the LISS-3 sensors in 1996 with a higher data resolution 
(Kasturirangan et al, 1996), we can distinguish more 
cover types with greater accuracy. This will then 
strengthen the correspondence between cover type and 
species diversity. 
Several other issues remain to be analyzed, before 
the wider application of a two-step methodology of the 
kind discussed above, for natural resource monitoring. 
To begin with, it is apparent that the correspondence 
between a cover type and level of the natural resource 
under consideration will depend on the following -