A geomatics data integration technique for coastal change monitoring - Mills, J. P.; Buckley, S. J.; Mitchell, H. L.; Clarke, P. J.; Edwards, S. J. (Earth surface processes and landforms, 2005)
This paper reports research carried out to develop a novel method of monitoring coastal change, using an approach based on digital elevation models (DEMs). In recent years change monitoring has become an increasingly important issue, particularly for landforms and areas that are potentially hazardous to human life and assets. The coastal zone is currently a sensitive policy area for those involved with its management, as phenomena such as erosion and landslides affect the stability of both the natural and the built environment. With legal and financial implications of failing to predict and react to such geomorphological change, the provision of accurate and effective monitoring is essential. Long coastlines and dynamic processes make the application of traditional surveying difficult, but recent advances made in the geomatics discipline allow for more effective methodologies to be investigated. A solution is presented, based on two component technologies – the Global Positioning System (GPS) and digital small format aerial photogrammetry – using data fusion to elim‐inate the disadvantages associated with each technique individually. A sparse but highly accurate DEM, created using kinematic GPS, was used as control to orientate surfaces derived from the relative orientation stage of photogrammetric processing. A least squares surface matching algorithm was developed to perform the orientation, reducing the need for costly and inefficient ground control point survey. Change detection was then carried out between temporal data epochs for a rapidly eroding coastline (Filey Bay, North Yorkshire). The surface matching algorithm was employed to register the datasets and determine dif‐ferences between the DEM series. Large areas of change were identified during the lifetime of the study. Results of this methodology were encouraging, the flexibility, redundancy and automation potential allowing an efficient approach to landform monitoring. Copyright © 2005 John Wiley & Sons, Ltd.