Implementing the formal language of the vegetation classification expert systems (ESy) in the statistical computing environment R

Abstract

Aims:

The machine-readable formal language of classification expert systems has become a standard for applying plot assignment rules in vegetation classification. Here we present an efficient algorithm implementing the vegetation classification expert system in the statistical programming language R. Methods:

The principal idea of the R implementation is to solve the assignments to vegetation types not sequentially plot by plot but to parse the assignment rules into (nested) components that each can be evaluated by simultaneous vector-based processing of all plots in a database. Results and conclusions:

We demonstrate the algorithm taking the EUNIS classification expert system of European habitat types (EUNIS-ESy) as an example. The R code version of the vegetation classification expert system is particularly useful in large vegetation-plot databases because it solves all logical operations vector-wise across all plots, allowing for efficient evaluation of membership expressions and formulas. Another advantage of the R implementation is that membership formulas are not only readable but can also be produced as a machine-written result, for example as the output of classification algorithms run in R.