Abstract
Little work has been done on spatial patterns of erosion and deposition making it difficult to extrapolate point measurements of erosion rate. This paper describes how it is possible to detect, describe and possibly forecast such patterns on a broad scale using remotely sensed data from Landsat which have been transformed to produce an index of erosion and deposition intensity. A stochastic approach to modelling is used because natural systems are too complex for the currently available analytical model. The stochastic process used is a simultaneous autoregressive spatial model defined on a finite lattice. Methods for fitting this model, generating synthetic data and obtaining model input data by deconvolution for use in subsequent forecasting with another model are described. Studies of 100 × 100 pixel (45 km 2) test areas in central Australia indicate that a first-order eight-neighbourhood model structure is the most appropriate. They also indicate that there are consistent changes in model parameter values as the amount of erosion in a landscape increases. This suggests that if the input series for a particular area may be derived, the model for a more eroded condition may be applied to obtain a forecast of the spatial patterns which would occur if the area should suffer more intense erosion.
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