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1 March 2009 Mapping urban and peri-urban agriculture using high spatial resolution satellite data
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In rapidly changing peri-urban environments where biophysical and socio-economic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to achieve improved classification results in complex heterogeneous landscapes. This study assessed the usefulness of object-oriented analysis of Quickbird high spatial resolution satellite data to classify urban and peri-urban agriculture in a limited peri-urban area of Hanoi, Vietnam. The results revealed that segmentation was essential in developing the object-oriented classification approach. Accurate segmentation of shape and size of an object enhanced classification with spectral, textural, morphological, and topological features. A qualitative, visual comparison of the classification results showed successful localisation and identification of most LCLU classes. Quantitative evaluation was conducted with a classification error matrix reaching an overall accuracy of 67% and a kappa coefficient of 0.61. In general, object-oriented classification of high spatial resolution satellite data proved the promising approach for LCLU analysis at village level. Capturing small-scale urban and peri-urban agricultural diversity offers a considerable potential for environmental monitoring. Challenges remain with the delineation of field boundaries and LCLU diversity on more spatially extensive datasets.
Dionys Forster, Yves Buehler, and Tobias Kellenberger "Mapping urban and peri-urban agriculture using high spatial resolution satellite data," Journal of Applied Remote Sensing 3(1), 033523 (1 March 2009).
Published: 1 March 2009

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