This study investigates the value of scale-space representations of spatial features for the identification and mapping of invasive plant species in high-resolution multi-spectral imagery. Scale-space representations combine the spatial domain with a scale dimension such that spatial features can be represented at multiple-spatial scales. In this work, Gaussian pyramids (GPs) are constructed to create discrete representations of the spatial features across the scale-space. A case study is employed to evaluate the performance of classifiers constructed using features at various levels within the GP scale-space to a classifier constructed using all features across the scale space. This case study explores the identification and mapping of spotted knapweed in a grassland ecosystem using multispectral imagery acquired using a remotely piloted aircraft system (RPAS). Given the large number of features, feature optimization was critical for developing high-performing classifiers. Classification was performed using two machine learning classifiers, random forest and support vector machine (SVM). The results of this case study show that very high-spatial resolution features do not produce the best image classifications, but rather that there is an optimal scale, lower than that of the raw imagery, of the image features that produces the best classification accuracy. The results also show that classification is not improved by the inclusion of features at multiple spatial scales. These findings suggest not only that feature spatial scale optimization can improve image analysis, but also that this optimization can inform RPAS flight planning to improve mission efficiency.