Quickbird image segmentation for mapping crops and their agro-environmental associated measures

Soil management in crops is mainly based on intensive tillage operations, which have a great relevancy in terms of increase of atmospheric CO 2 , desertification, erosion and land degrada tion. Due to these negative environmental im pacts, the European Union only subsidizes cropping systems which require the implemen tation of certain no-tillage systems and agro-en vironmental 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 produc tion. 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 typi cal agricultural Mediterranean area of dry condi tions. Five supervised classification methods based on different decision rule routines, Para llelepiped (P), Minimum Distance (MD), Maha lanobis Classifier Distance (MC), Spectral Angle