Land cover attributes and their utility within land cover mapping: a practical example

A disaggregated approach to land cover survey is developed utilising data primitives. A field methodology is developed to characterise five attributes: species composition, cover, height, structure and density. The utility of these data primitives, as land cover 'building blocks' is demonstrated via classification of the field data to multiple land cover schema. Per-pixel classification algorithms, trained on the basis of the classified field data, are utilised to classify a SPOT 5 satellite image. The resultant land cover maps have overall accuracies approaching 80%. However, significantly lower validation accuracies are demonstrated to be a function of sample fraction. The aggregation of attributes to classes under-utilises the potential of remote sensing data to describe variability in vegetation composition across the landscape. Consequently, land cover attribute parameterisation techniques are discussed. In conclusion, it is demonstrated that data primitives provide a flexible field data source proven to support multiple land cover classification schemes and scales.