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Title
Application of remote sensing and GIS for sustainable development

1) The identity of the cover type
2) The size of the individual element (patch) in which
resource levels are assessed (Forman and Godron,
1987; Forman, 1995). For example, a large
deciduous forest patch may contain tigers, while a
smaller forest patch may not be able to support
such species.
3) The shape of the patch. To illustrate, an elongated
stream will have different sedimentation levels
from a more spherical lake with the same total
amount of water.
4) The identities of surrounding cover types. For
example, a garden in a city surrounded by high
traffic roads will have higher levels of atmospheric
pollutants compared to a similar garden that does
not have such roads near by.
The extent to which factors other than the identity
of the cover type influence the distribution of resource
levels needs to be understood, as these are crucial inputs
required while modeling the levels of distribution from
cover maps. Again, in this context, we have carried out
preliminary investigations on the extent to which various
factors influence the distribution of flowering plant
species in the sample landscape discussed previously.
We found that the major factor influencing the set of
plant species found at particular points was the
ecosystem type. Flowever, about 30% of the variation in
plant species composition could not be explained by the
identity of the ecosystem patch and was due to other
factors like size, shape, and neighboring element’s.
Similar investigations are required for distribution of
other natural resources, and for water cover types as
well as land cover types. As water is redistributed from
place to place far more than soil, variables like water
quality levels will have dynamics quite different from
that of say soil quality. The relative importance of
various factors like patch size, shape and neighbouring
elements will hence differ considerably depending on
whether one is dealing with land or water systems.
Organizing an efficient system of mapping and
collection of field data over a country as large and
varied as India requires the consideration of several
issues. At the first level, of using remotely sensed data
tor mapping land and water cover, one needs to prepare
a set of cover types which can be identified accurately
by the sensors, relate well to the levels of various natural
resources being monitored, and reduce inter-observer
variation in identification. Collecting training data for
supervised classification also involves several statistical
issues, notably regarding the distribution of training sites
(Atkinson, 1991; Dobertin and Biging, 1996). Ideally,
training sites should be spatially well distributed over
the area to be mapped, so that the range of variation in
the cover type can be included. However, logistically,
collecting information from areas clustered close to each
other is far easier than collecting the same amount of
data from sites scattered far apart. Similar issues arise
while assessing map accuracy (Janssen and van der Wei.
1994).
In addition, the seasons at which remotely sensed
data is ideal for monitoring a particular resource need to
be determined separately for each resource. For
example, to monitor carbon utilization in a landscape it
may be ideal to use the monsoon months as deciduous
trees would also have their full complement of leaves.
However, to differentiate deciduous from evergreen
forest cover it would be better to use pre-rainfall data,
where deciduous trees are bare and can be differentiated
from evergreen more easily (Roy, 1993). Again crop
monitoring may require certain data collected at specific
dates.
Data collection in the field has also to be
organized, taking into consideration various factors. The
number, size and distribution of samples in the field will
crucially affect the quality of information gathered. This
is an entire statistical area in itself, of sampling theory,
which we shall not elaborate on here for lack of space.
However, these issues need to be given serious thought
as data collection poses logistic as well as scientific
constraints, and both of these may have varying
requirements.
Finally, analyzing the correlation between these
two kinds of data collected by different means and at
different scales is not a trivial task. This data has to be
used to model the distribution of natural resources in the
future too, based on cover maps, and in order to do this
the extent to which various factors like type identity,
shape or neighboring elements affect these distributions
needs to be understood.
ORGANIZING PRE-EXISTING INFORMATION
The task before us, to assess the distribution of
natural resources over space and time, is fairly large, as
well as extremely important. Means have to be devised
for carrying out this task as efficiently as possible. A
large number of maps, at different scales and using
different sets of cover types, already exist with some
information about natural resource distribution. If such
information, already existing but of different kinds, can
be collated and analyzed then it would make our task
much easier. As an exercise, therefore, we looked at
recent land cover maps published by various individuals