Scaling of classification systems—effects of class precision on detection accuracy from medium resolution multispectral data

Abstract

ContextLand-cover class definitions are scale-dependent. Up-scaling categorical data must account for that dependence, but most decision rules aggregating categorical data do not produce scale-specific class definitions. However, non-hierarchical, empirically derived classification systems common in phytosociology define scale-specific classes using species co-occurrence patterns.ObjectivesEvaluate tradeoffs in class precision and representativeness when up-scaling categorical data across natural landscapes using the multi-dimensional grid-point (MDGP)-scaling algorithm, which generates scale-specific class definitions; and compare spectral detection accuracy of MDGP-scaled classes to ‘majority-rule’ aggregated classes.MethodsVegetation maps created from 2-m resolution WorldView-2 data for two Everglades wetland areas were scaled to the 30-m Landsat grid with the MDGP-scaling algorithm. A full-factorial analysis evaluated the effects of scaled class-label precision and class representativeness on compositional information loss and detection accuracy of scaled classes from multispectral Landsat data.ResultsMDGP‐scaling retained between 3.8 and 27.9% more compositional information than the majority rule as class-label precision increased. Increasing class-label precision and information retention also increased spectral class detection accuracy from Landsat data between 1 and 8.6%. Rare class removal and increase in class-label similarity were controlled by the class representativeness threshold, leading to higher detection accuracy than the majority rule as class representativeness increased.ConclusionsWhen up-scaling categorical data across natural landscapes, negotiating trade-offs in thematic precision, landscape-scale class representativeness and increased information retention in the scaled map results in greater class-detection accuracy from lower-resolution, multispectral, remotely sensed data. MDGP-scaling provides a framework to weigh tradeoffs and to make informed decisions on parameter selection.