A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments

High-resolution Corona imagery acquired by the United States through spy missions in the 1960s 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 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments.

Automated summary

The study presents a workflow for georeferencing Corona imagery in a highly desertified landscape, successfully locating four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery, with an overall accuracy of 11.77 m. The workflow is documented in sufficient detail for replication in similar environments.