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 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.Peer Reviewe

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

Agricultural features such as terraces provide a number of environmental services, and therefore its maintenance is supported by measures implemented in the European Common Agricultural Policy (CAP). In the framework of the CAP implementation and control 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 in the case of terraced permanent crops (e.g. olive trees). In the present work we present a novel methodology for the automatic and cost-efficient identification of terraces in this scenario using exclusively imagery from commercial off-the-shelf (COTS) cameras on board of unmanned aerial vehicles (UAV). Using state of the art computer vision techniques, we generated ortho imagery and digital surface models (DSM) at 11 cm spatial resolution with low user intervention. These data were used in a second stage to identify terraces using a multi-scale object oriented classification method. The results showed the potential of this method even in high complexity agricultural areas, both regarding the DSM reconstruction and the classification stage. The UAV-derived DSM showed a RMSE lower than 0.5 m when assessing the heights of the terraces against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90 % based exclusively on spectral and altitudinal information derived from the UAV imagery.JRC.H.4-Monitoring Agricultural Resource