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Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed dataPreviously established results demonstrate that LANDSAT data are autocorrelated and can be described by a univariate linear stochastic process known as 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 phi(1) and theta(1). In a comparison of LANDSAT thematic mapper simulator (TMS) data and LANDSAT MSS data several results were established: (1) The form of the relatedness as described by this model is not dependent upon system look angle or pixel size. (2) The phi(1) coefficient increases with decreasing pixel size and increasing topographic complexity. (3) Changes in topography have a greater influence upon phi(1) than changes in land cover class. (4) The theta(1) seems to vary with the amount of atmospheric haze. These patterns of variation in phi(1) and theta(1) are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, characterize topography, and reduce the number of bytes needed to store remotely sensed data.
Document ID
19810011003
Acquisition Source
Legacy CDMS
Document Type
Preprint (Draft being sent to journal)
Authors
Labovitz, M. L. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Toll, D. L. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Kennard, R. E. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
September 4, 2013
Publication Date
December 1, 1980
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
NASA-TM-82064
Accession Number
81N19530
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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