Data-informed sampling and mapping: an approach to ensure plot-based classifications locate, classify and map rare and restricted vegetation types

Abstract

A new approach to vegetation sample selection, classification and mapping is described that accounts for rare and restricted vegetation communities. The new method (data-informed sampling and mapping: D-iSM) builds on traditional preferential sampling and was developed to guide conservation and land-use planning. It combines saturation coverage of vegetation point data with a preferential sampling design to produce locally accurate vegetation classifications and maps. Many existing techniques rely entirely or in part on random sampling, modelling against environmental variables, or on assumptions that photo-patterns detected through aerial photographic interpretation or physical landscape features can be attributed to a specific vegetation type. D-iSM uses ground data to inform both classification and mapping phases of a project. The approach is particularly suited to local- and regional-scale situations where disputes between conservation and development often lead to poor planning decisions, as well as in circumstances where highly restricted vegetation types occur within a wider mosaic of more common communities. Benefits of the D-iSM approach include more efficient and more representative floristic sampling, more realistic and repeatable classifications, increased user accuracy in vegetation mapping and increased ability to detect and map rare vegetation communities. Case studies are presented to illustrate the method in real-world classification and mapping projects.