Understanding spatial uncertainty in empirical remote sensing models

Abstract

This thesis addresses the uncertainty in empirical remote sensing models.  Specifically the empirical line method (ELM) for atmospheric correction of airborne remotely sensed data is examined.

First, the pairing of the field and remotely sensed data for input into the regression model is considered.  The typical approach to the ELM averages over all field measurements in each ground target (GT).  However, this approach is problematic.  Disadvantages were addressed by pairing the field measurements directly with the pixel-based remotely sensed data either using the point-pixel approach or block-pixel approach.  The latter is favoured since it explicitly addresses the support issue.  It is recommended that at least 50 and preferably 100 measurements should be obtained for each GT.

This thesis quantified the impact of positional uncertainty on the outcome of the ELM.  When a moderate level of positional uncertainty was introduced, this led to bias in the parameter estimates for the point-pixel approach, although this could be minimised by using a sample size of at least 50 and preferably 100 measurements for each GT.  For the geostatistical block-pixel approach introducing positional uncertainty led to an increase in the variogram at short lags but did not, affect parameter estimation for the ELM.

Finally, adopting the point-pixel or block-pixel approach led to a regression model with heteroskedastic and spatially correlated residuals.  However, these conditions are not handled in standard regression models.  Hence a model that incorporates both weighting and spatial correlation was adopted.  When this approach was applied to real data, it led to an increase in the uncertainty in the ELM.  Spatial correlation may still be presenting a random sample.  Hence adopting a random sampling strategy does not obviate the need to model this phenomenon.

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