Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.
Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery.
Monitoring Agricultural Resources Unit, Institute for Environment and Sustainability, European Commission Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy; Department of Botany, GI-1934-TB, IBADER, University of Santiago de Compostela, Escola Politécnica Superior, Campus Universitario s/n, E-27002 Lugo, Spain. Electronic address: ramondiaz06@gmail.com.
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Diaz-Varela
Monitoring Agricultural Resources Unit, Institute for Environment and Sustainability, European Commission Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy; Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain.
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Zarco-Tejada
Monitoring Agricultural Resources Unit, Institute for Environment and Sustainability, European Commission Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy.
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Angileri
Monitoring Agricultural Resources Unit, Institute for Environment and Sustainability, European Commission Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy.
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Loudjani
D000383:Agriculture; D007091:Image Processing, Computer-Assisted; D058998:Remote Sensing Technology
Agricultural terraces; Common agricultural policy; Digital surface model; Object-oriented analysis; Unmanned aerial vehicles; Very high resolution imagery