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 deve...
| Published in: | Journal of Environmental Management Vol. 134; pp. 117 - 127 |
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| Main Authors: | , , , |
| Format: | Article |
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Academic Press Inc.
Feb2014
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=94366555&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 94366555 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Feb2014 vid: 134 pid: 735 pub: Academic Press Inc. artinfo: ui: 94366555 10.1016/j.jenvman.2014.01.006 ppf: 117 ppct: 10 formats: tig: atl: Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. aug: au: Diaz-Varela, R.A. Zarco-Tejada, P.J. Angileri, V. Loudjani, P. affil: 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 Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain su: Terraces (Agriculture) Research methodology Object-oriented methods (Computer science) Automatic identification Digital elevation models Drone aircraft Cameras Multispectral imaging sug: subj: Commercial and service industry machinery manufacturing Photographic and Photocopying Equipment Manufacturing Photographic equipment and supplies merchant wholesalers Photographic Equipment and Supplies Merchant Wholesalers Electronics Stores Camera and photographic supplies stores Terraces (Agriculture) Research methodology Object-oriented methods (Computer science) Automatic identification Digital elevation models Drone aircraft Cameras Multispectral imaging keyword: Agricultural terraces Common agricultural policy Digital surface model Object-oriented analysis Unmanned aerial vehicles Very high resolution imagery Agricultural terraces Common agricultural policy Digital surface model Object-oriented analysis Unmanned aerial vehicles Very high resolution imagery ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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