Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed data

Abstract

Previously established results of Craig (1976, 1979) and Craig and Labovitz (1980) demonstrated that Landsat data are autocorrelated and can be described by a univariate linear stochastic process known as an auto regressive integrated moving average model of degree 1, 0, 1, or ARIMA (1, 0, 1). This model has two coefficients of interest for interpretation: φandθ In a comparison of Landsat Thematic Mapper Simulator (TMS) data and Landsat MSS data several results are established:(1)The form of the relatedness as described by this model is not dependent upon system look angle or pixel size.(2)The coefficient φ increases with decreasing pixel size and increasing topographic complexity.(3)Changes in topography have a greater influence upon φ than changes in land cover class.(4)Theθ seems to vary with the amount of atmospheric haze. These patterns of variation in φandθ are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, to characterize topography, and to reduce the number of bytes needed to store remotely sensed data.