Bradter, Ute
- University of Leeds
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.
Keywords
object orientated; knowledge based classifier; non-cropped; color infrared
Published in
Proceedings of SPIE
2013, volume: 8887
Title: Remote sensing for agriculture, ecosystems, and hydrology XIV : 24-26 September 2012, Edinburgh, United Kingdom
Publisher: SPIE
Conference
SPIE Remote Sensing : Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
SLU Authors
UKÄ Subject classification
Ecology
Environmental Sciences
Publication identifier
- DOI: https://doi.org/10.1117/12.2028356
Permanent link to this page (URI)
https://res.slu.se/id/publ/86580