The thesis evaluates the potential for using multiple remote sensing techniques to identify archaeological features buried beneath vegetation. The research is carried out on data acquired by the Natural Environmental Research Council (henceforth NERC), who have provided multispectral, LIght Detection and Ranging (henceforth LİDAR) and high resolution colour aerial photographic data. The returns from these techniques are compared with ground-based geophysical survey data carried out by the Landscape Research Centre (henceforth LRC) on four major projects funded by English Heritage (henceforth EH), as well as aerial photographs collected by the LRC. The methodology adopted for this research uses primarily qualitative techniques, where images from different forms of remote sensing are processed, georeferenced and then compared for their ability to identify archaeological features. A process has been developed where the returns from each form of remote sensing are interpreted and then digitised as vector polygons with individual database entries for each polygon, which allows a direct comparison between the different datasets to be conducted. The timing of the data acquisition is shown to be critical if the data is to be used for small scale anomaly detection, as is the case with archaeological features. This is particularly true for airborne sensors, although the returns from ground-based geophysical surveys can also be affected if carried out under unfavourable circumstances. It was established that the different underlying drift geologies of the Vale of Pickering also affected the returns from remote sensing sources, with the calcareous and sandy zones of the southern part of the research area generally (though not exclusively) providing better results than the more alluvial zones to the north. The use of different forms of airborne remote sensing and geophysical survey are demonstrated to be complementary, with each form of remote sensing identifying different, though not always exclusively different, archaeological anomalies.