A novel, disaggregated approach to land cover survey is developed on the basis of land
cover attributes; the parameters typically used to delineate land cover classes. The
recording of land cover attributes, via objective measurement techniques, is advocated
as it eliminates the requirement for surveyors to delineate and classify land cover; a
process proven to be subjective and error prone. Within the North York Moors
National Park, a field methodology is developed to characterise five attributes: species
composition, cover, height, structure and density.
The utility of land cover attributes to act as land cover ‘building blocks’ is
demonstrated via classification of the field data to the Monitoring Landscape Change
in the National Parks (MLCNP), National Land Use Database (NLUD) and Phase 1
Habitat Mapping (P1) schemes. Integration of the classified field data and a SPOT5
satellite image is demonstrated within per-pixel and object-orientated classification
environments. Per-pixel classification produced overall accuracies of 81%, 80% and
76% at the field samples for the MLCNP, NLUD and P1 schemes, respectively. However,
independent validation produced significantly lower accuracies. These decreases are
demonstrated to be a function of sample fraction. Object-orientated classification,
exemplified for the MLCNP schema at 3 segmentation scales, achieved accuracies
approaching 75%.
The aggregation of attributes to classes underutilises the potential of the remotely
sensed data to describe landscape variability. Consequently, classification and
geostatistical techniques capable of land cover attribute parameterisation, across the
study area, are reviewed and exemplified for a sub-pixel classification.
Land cover attributes provide a flexible source of field data which has been proven to
support multiple land cover classification schemes and classification scales (sub-pixel,
pixel and object). This multi-scaled/schemed approach enables the differential
treatment of regions, within the remote sensing image, as a function of landscape
characteristics and the users’ requirements providing a flexible mapping solution
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