Hierarchical vs. Non-Hierarchical Systems
Classification systems come in two basic formats, hierarchical and
non-hierarchical. LCCS is a hierarchical system.
Most systems are hierarchically structured because such a classification
offers more consistency. In facts it can accommodate different levels
of information, from structured broad-level classes to further
systematic sub-division into more detailed sub-classes. At each level
the defined classes are mutually exclusive. At the higher levels of the
classification system few diagnostic criteria are used, whereas at the
lower levels the number of diagnostic criteria increases. Criteria used
at one level of the classification should not be repeated at another,
i.e. lower level.
A Priori and A Posteriori Systems
The classification process can be done in two ways: either a priori or
a posteriori. LCCS is an a priori classification system.
In an a priori classification system, the classes are abstractions
of the types actually occurring. The approach is based upon definition
of classes before any data collection actually takes place. This means
that all possible combinations of diagnostic criteria must be dealt
with beforehand in the classification. Basically, in the field, each
sample plot is identified and labelled according to the classification
adopted. The main advantage is that classes are standardized
independently from the area and the means used. The disadvantage,
however, is that this method is rigid, as some of the field samples
may not be easily assignable to one of the pre-defined classes.
The a posteriori classification differs fundamentally by its direct
approach and its freedom from preconceived notions. The approach is
based upon the definition of classes after clustering similarity or
dissimilarity of field samples collected. The advantage of this type
of classification is its flexibility and adaptability compared with
the implicit rigidity of an a priori classification. The a posteriori
approach implies a minimum of generalization. This type of classification
better fits the collected field observations in a specific area. However,
because an a posteriori classification depends on the specific area
described and is adapted to local conditions, it is unable to define
standardized classes. Clustering of samples to define the classes can
only be done after data collection, and the relevance of certain
criteria in a certain area may be limited when used elsewhere or in
ecologically quite different regions.
