A comparison of three landscape classifications and investigation of the potential for using remotely sensed land cover data for landscape classification

Abstract

Numerical landscape classifications group land units (e.g. 1-km grid squares) with similar characteristics into distinct landscape types (termed land classes). Landscape classifications have considerable potential as tools in rural land-use planning. To date, a plethora of numerical classifications have been developed, yet little attempt has been made to quantify differences between classifications which may arise through the use of different attribute data and classification techniques. The present paper quantifies differences between three hierarchical landscape classifications developed using closely related algorithms, but using contrasting attribute data. Two classifications were based on land cover; from field survey and remote sensing, respectively. The third was based primarily on abiotic environmental variables. Quantitative comparisons between the three classifications indicate that similar land classes are recognisable at different levels of environmental and ecological organisation. It is concluded that the use of remotely sensed land cover in the production of landscape classification has many advantages over previous approaches. Foremost among these benefits are the ease with which digital land cover data can be handled within Geographic Information Systems and the consequential ability to produce classifications rapidly at differing spatial resolutions. The potential roles of numerical landscape classifications in rural planning are reviewed.