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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/21/5395 |
| _version_ | 1797466595132440576 |
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| author | Brooke Iacone Ginger R. H. Allington Ryan Engstrom |
| author_facet | Brooke Iacone Ginger R. H. Allington Ryan Engstrom |
| author_sort | Brooke Iacone |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:41:59Z |
| format | Article |
| id | doaj.art-5d3f86a109f145e699191e80ecf0a8cc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:59Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5d3f86a109f145e699191e80ecf0a8cc2023-11-24T06:38:20ZengMDPI AGRemote Sensing2072-42922022-10-011421539510.3390/rs14215395A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid EnvironmentsBrooke Iacone0Ginger R. H. Allington1Ryan Engstrom2Department of Geography, The George Washington University, Washington, DC 20052, USADepartment of Geography, The George Washington University, Washington, DC 20052, USADepartment of Geography, The George Washington University, Washington, DC 20052, USAHigh-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.https://www.mdpi.com/2072-4292/14/21/5395Corona imageryremote sensinggeoreferencingsemi-arid ecosystems |
| spellingShingle | Brooke Iacone Ginger R. H. Allington Ryan Engstrom A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments Remote Sensing Corona imagery remote sensing georeferencing semi-arid ecosystems |
| title | A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments |
| title_full | A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments |
| title_fullStr | A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments |
| title_full_unstemmed | A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments |
| title_short | A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments |
| title_sort | methodology for georeferencing and mosaicking corona imagery in semi arid environments |
| topic | Corona imagery remote sensing georeferencing semi-arid ecosystems |
| url | https://www.mdpi.com/2072-4292/14/21/5395 |
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