Spatially explicit ecological models rely on spatially exhaustive data layers that have scales appropriate to the ecological processes of interest. Such data layers are often categorical raster maps derived from high-resolution, remotely sensed data that must be scaled to a lower spatial resolution to make them compatible with the scale of ecological analysis. Statistical functions commonly used to aggregate categorical data are majority-, nearest-neighbor- and random-rule. For heterogeneous landscapes and large scaling factors, however, use of these functions results in two critical issues: (1) ignoring large portions of information present in the high-resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (2) maintaining classes from the high-resolution dataset at the lower spatial resolution assumes validity of the classification scheme at the low-resolution scale, failing to represent recurring mixes of heterogeneous classes present in the low-resolution grid cells. The proposed new scaling algorithm resolves these issues, aggregating categorical data while simultaneously controlling for information loss by generating a non-hierarchical, representative, classification system valid at the aggregated scale.
Implementing scaling parameters, that control class-label precision effectively reduced information loss of scaled landscapes as class-label precision increased. In a neutral-landscape simulation study, the algorithm consistently preserved information at a significantly higher level than the other commonly used algorithms. When applied to maps of real landscapes, the same increase in information retention was observed, and the scaled classes were detectable from lower-resolution, remotely sensed, multi-spectral reflectance data with high accuracy. The framework developed in this research facilitates scaling-parameter selection to address trade-offs among information retention, label fidelity, and spectral detectability of scaled classes.
When generating high spatial resolution land-cover maps, quantifying effects of sampling intensity, feature-space dimensionality and classifier method on overall accuracy, confidence estimates, and classifier efficiency allowed optimization of the mapping method. Increase in sampling intensity boosted accuracies in a reasonably predictable fashion. However, adding a second image acquired when ground conditions and vegetation phenology differed from those of the first image had a much greater impact, increasing classification accuracy even at low sampling intensities, to levels not reached with a single season image.
