The spatial analysis of spectral data: extracting the neglected dataCreated by Brian Lees
Remotely sensed data are a key input to GIS-based spatial decision support systems for land cover and land use application areas. One of the major sources of error in the input of processed remotely sensed data to GIS is in the process of classification. Particularly important is the degradation of the data from the interval to nominal level of measurement. This is less significant in cultural landscapes where boundaries predominate, but it becomes an important source of error in natural, and disturbed natural, environments where gradients exist. Use of the G i * local statistic as an alternative approach to processing remotely sensed data proved very successful, replicating the level of discrimination achieved by conventional classification and field labelling in a much shorter time, whilst avoiding the errors associated with conversion of the data from the interval to nominal level of measurement.