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Título

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle

Autor Díaz-Varela, Ramón A.; Zarco-Tejada, Pablo J. ; Angileri, V.; Loudjania, P.
Palabras clave Agricultural terraces
Common agricultural policy
Object-oriented analysis
Very high resolution imagery
Digital surface model
Unmanned aerial vehicles
Fecha de publicación 15-feb-2014
EditorAcademic Press
Citación Journal of Environmental Management 134: 117-126 (2014)
ResumenAgricultural 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 11cm 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 0.5m 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. © 2014 Elsevier Ltd.
URI http://hdl.handle.net/10261/94935
DOI10.1016/j.jenvman.2014.01.006
Identificadoresdoi: 10.1016/j.jenvman.2014.01.006
issn: 0301-4797
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