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

Abstract

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 km²). 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 κ coefficient of 0.77 for the per-field vector segmentation compared to an overall accuracy of 56–60% and a κ 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 κ coefficient 0.7. In diversified agriculture, per-field vector segmentation and classification achieved higher classification results.

Keywords:

• field boundary
• high-pass filter
• high spatial resolution satellite data
• object-oriented classification
• per-field classification

Acknowledgement

We wish to express our thanks to the Soil and Fertiliser Institute of Vietnam (SFI), especially to Vu Dinh Tuan and Dinh Viet Hung for their assistance during the field data collection campaign. Further, we would like to thank Sylvie Peter of Eawag/Sandec for the English editing. This research was funded by the Swiss National Centre of Competence in Research, NCCR North-South.