Mapping diversified peri-urban agriculture - potential of object-based versus per-field land cover/land use classification

High spatial resolution satellite data contribute to improving land cover/land use (LCLU) classification in agriculture. A classification procedure based on Quickbird satellite image data was developed to map LCLU of diversified agriculture at sub-communal and communal level (7 km(2)). Segmentation performance of the panchromatic band in combination with high pass filters (HPF) was tested first. Accuracy of field boundary delineation was evaluated by an object-based segmentation, a per-field and a manual classification, along with a quantitative accuracy assessment. Sub-communal classification revealed an overall accuracy of 84% with a kappa coefficient of 0.77 for the per-field vector segmentation compared to an overall accuracy of 56-60% and a k coefficient of 0.37-0.42 for object-based approaches. Per-field vector segmentation was thus superior and used for LCLU classification at communal level. Overall accuracy scored 83% and the k coefficient 0.7. In diversified agriculture, per-field vector segmentation and classification achieved higher classification results.