points, but collected at a much finer spatial scale.
Linking these two different sorts of data to come up with
an understanding of natural resource distribution is not
trivial.
One possible solution is using remote sensors to
map areas of interest into land and water cover types.
Each type can also be correlated with a particular level
or distribution of the resource of interest - say water
quality, or biomass, based on field measurements. If
such information is available, a map of a particular
locality in terms of cover types can then be directly
linked to levels of resource distributed in those types.
This assumes the following - that remote sensing based
classification corresponds to the distribution of different
land/water cover types (in spite of problems relating to
lack of spectral separability) and that these cover types
are tightly correlated with resource distributions. Both of
these are open questions, for the following reasons.
Land cover types such as forests are complex
mixtures of different percentages and kinds of soil, with
different levels of moisture and are composed of
different species, each with its own characteristic set of
coloured pigments, leaf size ranges and orientations
(Verbyla, 1995). These factors combine in theory to
make each cover type distinct from others in terms of
spectral reflectance values. In actuality, boundaries
between these types may often be arbitrary - while some
boundaries such as those between water bodies and land
may be clear-cut, others between different vegetation
types may be fuzzy, and hard to determine from a
remote sensor (Forman, 1995).
Even if boundaries are clear-cut, cover types
distinct on the ground are not always separable using
remote sensors, mainly due to mixed pixels. When cover
types vary at a scale finer than the resolution of the
remote sensor, each signal received by the sensor is
actually a composite of signals coming from various
types, which cannot always be separated. This depends
on the fineness with which one wishes to delineate these
ecosystem types - at a coarser scale of mapping, each
pixel would correspond to a single type. This problem,
therefore, fixes the spatial scale at which different types
can be discriminated from each other (Moody and
Woodcock, 1994).
Even if distinct, land and water cover types are
spectrally separable due to various characteristics - of
which difference in resource levels may be only one
(Kroner and Running, 1993). For example, land cover
types can be discriminated and defined in terms of
several factors, such as vegetation composition,
structure, biomass levels, percentage leaf cover,
phenology and soil composition. The extent to which a
type defines the distribution of a single specific resource
such as biomass is not very clear.
LINKING REMOTE SENSING WITH FIELD
DATA COLLECTION
We have investigated these questions through the
application of a two-level methodology, combining
relatively coarse though extensive remotely sensed data
with locally sampled, intensive and detailed field data on
species distributions. Our study was specifically aimed
towards assessment of a specific natural "resource",
namely biodiversity, using a combination of remote
sensors and field measurements. In theory, remote
sensors can be used to map ecosystems, as their spatial
and spectral resolution is adequate for this purpose
(Running et at., 1995). Such broad ecosystem-scale
mapping is in fact recommended for country wide
exercises in biodiversity assessment in the Global
Biodiversity Assessment (UNEP, 1995).
Species are constituents of ecosystems, along with
abiotic components. In principle, ecosystems delineate
species boundaries (Noss, 1996). Maps of ecosystem
distributions may therefore be used, in conjunction with
information on species frequencies in these ecosystem
types, to generate information on species diversity levels
(Condit, 1996). The Global Biodiversity Assessment in
fact recommends such a method, stating “broad scale
sampling generally requires the use of remote sensing
methods and measures at the levels of the biotope and
the landscape, while point-sampling involves measuring
a representative selection of localized sampling points.
Data from a series of points, when coupled with remote
sensing, may provide information that can be
extrapolated for global coverage”. However, the
correlation between species and ecosystems is not
absolute and ecosystem boundaries are often hard to
define (Hansen and di Castri, 1992).
Earlier studies in this area have not been
conclusive, (Treitz et at., 1992; Franklin et at., 1994;
Ravan et at., 1995), but our studies in the Western Ghats
of India (Nagendra and Gadgil, 1999) clearly
demonstrate the feasibility of such a methodology for
assessing Angiosperm (flowering plant) species
diversity. We observe that supervised classification of a
small (36.5 km") landscape into ecosystem types of a
hectare or more, can distinguish between ecosystem
types accurately enough that the classified types differ
significantly in the diversity of their flowering plants.
Unsupervised classification, with a specification of the
number of types to be classified, was unable to do so.
