Studying and investigating the use of CORONA images in remote sensing of surrounding geomorphology

Geomorphology has its origins in the nineteenth century with the results of exploration, and the realization that the surface of the earth had been shaped over a long time through the operation of processes that are largely in operation today (uniformitarianism). The observations by Charles Darwin during the voyage of the Beagle extended this view, particularly his remarkable deduction that fringing reefs might become barrier reefs which in turn might form atolls as a result of gradual subsidence of volcanic islands combined with vertical reef growth. In the first part of the twentieth century geomorphology was dominated by the ‘geographical cycle’ of erosion of William Morris Davis who anticipated landscape denudation through a series of stages culminating in peneplanation, and subsequent rejuvenation by uplift. This highly conceptual model, across landscapes in geological time, was also applied to the coast by Davis who envisaged progressive erosion of the coast reducing shoreline irregularities with time. Such landscape-scale studies were extended by Douglas Johnson who emphasized the role of submergence or emergence as a result of sea-level change. High-resolution Corona imagery acquired by the United States through spy missions in the ۱۹۶۰s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of ۱۱.۷۷ m, and the workflow is documented in sufficient detail for replication in similar environments.

Keywords:

Corona imagery , remote sensing , georeferencing , semi-arid ecosystems