The increased flexibility while maintaining mappability
To create a standardized, hierarchical, consistent, a priori
classification system containing systematic and strict class boundary
definitions means to build flexibility into the classification system.
In this context, “flexibility” can have various meanings. First of
all, flexibility should address the potential for the classification
system to describe enough classes to cope with the real world. At the
same time, however, flexibility should adhere to strict class boundary
definitions that should be unambiguous and clear. In addition, the
classes in such a system should be as neutral as possible in the
description of a land cover feature in order to answer to the needs
of a wide variety of end-users and disciplines.
Many current classification systems are not generally suitable for
mapping, and subsequent monitoring, purposes. The integrated approach
requires clear distinction of class boundaries. Furthermore, the use of
diagnostic criteria and their hierarchical arrangement to form a class
should be a function of the mappability, i.e. the ability to define a
clear boundary between two classes. Hence, diagnostic criteria should
be hierarchically arranged in order to ensure a high degree of geographical
accuracy at the highest levels of the classification.
How does one increase the classification system flexibility while
maintaining the principle of mappability and aiming at standardization?
These prerequisites can only be accomplished if the classification has
the possibility of generating a high number of classes with clear boundary
definitions. In other words, it should be possible to delineate a large
number of classes in order to match the enormous variation of land cover
features, while maintaining the clear distinction of class boundaries.
In current classification systems this possibility is hampered by the
manner in which these classifications are set up. Differences between
classes can only be derived from class descriptions. Therefore, it would
be very difficult for the user to distinguish between such classes just
basing upon class names or unsystematic descriptions, as is it the case
with most of the current classification systems.
