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	Clasificacion de cultivos y de sus medidas agroambientales mediante segmentacion de imagenes QuickBird
	Lopez-Granados, F.; Castillejo-Gonzalez, I. L.; Pena-Barragan, J. M.; Jurado-Exposito, M.; de la Orden, M. Sanchez; Garcia-Torres, L.; Garcia-Ferrer, A.
通讯作者	Lopez-Granados, F
来源期刊	REVISTA DE TELEDETECCION
ISSN	1133-0953
EISSN	1988-8740
出版年	2009
期号	31 页码:52-63
英文摘要	Soil management in crops is mainly based on intensive tillage operations, which have a great relevancy in terms of increase of atmospheric CO2, desertification, erosion and land degradation. Due to these negative environmental impacts, the European Union only subsidizes cropping systems which require the implementation of certain no-tillage systems and agro-environmental measures, such as keeping the winter cereal residues and non-burning of stubble to reduce erosion, and to increase the organic matter, the fertility of soils and the crop production. Nowadays, the follow-up of these agrarian policy actions is achieved by ground visits to sample targeted farms; however, this procedure is time-consuming and very expensive. To improve this control procedure, a study of the accuracy performance of several classification methods has been examined to verify if remote sensing can offer the ability to efficiently identify crops and their agro-environmental measures in a typical agricultural Mediterranean area of dry conditions. Five supervised classification methods based on different decision rule routines, Parallelepiped (P), Minimum Distance (MD), Mahalanobis Classifier Distance (MC), Spectral Angle Mapper (SAM), and Maximum Likelihood (ML), were examined to determine the most suitable classification algorithm for the identification of agro-environmental measures such as winter cereal stubble and burnt stubble areas and other land uses such as river side trees, vineyard, olive orchards, spring sown crops, roads and bare soil. An object segmentation of the satellite information was also added to compare the accuracy of the classification results of pixel and object as Minimum Information Unit (MIU). A multispectral QuickBird image taken in early summer was used to test these MIU and classification methods. The resulting classified images indicated that object-based analyses clearly outperformed pixel ones, yielding overall accuracies higher than 85% in most of the classifications. The choice of a classification method can markedly influence the accuracy of classification maps.
英文关键词	Burnt and non-burnt crop stubble Crop inventory Image segmentation
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000215977100005
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/329230
作者单位	[Lopez-Granados, F.; Pena-Barragan, J. M.; Jurado-Exposito, M.; Garcia-Torres, L.] CSIC, Inst Agr Sostenible, Cordoba 14080, Spain; [Castillejo-Gonzalez, I. L.; de la Orden, M. Sanchez; Garcia-Ferrer, A.] Univ Cordoba, Dept Ingn Graf & Geomat, Cordoba, Argentina
推荐引用方式 GB/T 7714	Lopez-Granados, F.,Castillejo-Gonzalez, I. L.,Pena-Barragan, J. M.,et al. Clasificacion de cultivos y de sus medidas agroambientales mediante segmentacion de imagenes QuickBird[J],2009(31):52-63.
APA	Lopez-Granados, F..,Castillejo-Gonzalez, I. L..,Pena-Barragan, J. M..,Jurado-Exposito, M..,de la Orden, M. Sanchez.,...&Garcia-Ferrer, A..(2009).Clasificacion de cultivos y de sus medidas agroambientales mediante segmentacion de imagenes QuickBird.REVISTA DE TELEDETECCION(31),52-63.
MLA	Lopez-Granados, F.,et al."Clasificacion de cultivos y de sus medidas agroambientales mediante segmentacion de imagenes QuickBird".REVISTA DE TELEDETECCION .31(2009):52-63.