Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK

Jerome O'Connell; Ute Bradter; Tim G. Benton

doi:10.1117/12.2028356

16 October 2013 Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK

Jerome O'Connell, Ute Bradter, Tim G. Benton

Author Affiliations +

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 888708 (2013) https://doi.org/10.1117/12.2028356
Event: SPIE Remote Sensing, 2013, Dresden, Germany

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.

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Jerome O'Connell, Ute Bradter, and Tim G. Benton "Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 888708 (16 October 2013); https://doi.org/10.1117/12.2028356

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