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Proceedings Paper

Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK
Author(s): Jerome O'Connell; Ute Bradter; Tim G. Benton
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Paper Abstract

With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

Paper Details

Date Published: 16 October 2013
PDF: 15 pages
Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 888708 (16 October 2013); doi: 10.1117/12.2028356
Show Author Affiliations
Jerome O'Connell, Univ. of Leeds (United Kingdom)
Ute Bradter, Univ. of Leeds (United Kingdom)
Tim G. Benton, Univ. of Leeds (United Kingdom)


Published in SPIE Proceedings Vol. 8887:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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