Abstract

This article presents a set of techniques developed to classify land cover on a per-parcel (herein termed per-field) basis by integrating fine spatial resolution simulated satellite sensor imagery with digital vector data. Classification, based on the spectral and spatial properties of the imagery, was carried out on a per-pixel basis. The resulting classified images were then integrated with vector data to classify on a per-field basis. Four tools were adopted or developed to increase the accuracy and utility of the per-field classification and a fifth was proposed. The spectral variability within agricultural fields resulted in misclassification within the per-pixel classification, and this was overcome using a per-field classification. Mixed land cover in urban areas also resulted in misclassification. A low pass smoothing filter and a “texture” filter applied to the per-pixel classified image increased the classification accuracy of this land cover prior to per-field classification. The flexibility of the integration process enabled the exploitation of spectral and spatial variation between pixels within individual parcels to produce new classes during per-field classification and to identify fields with a high likelihood of misclassification.