Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery
Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016122003697 |
| _version_ | 1797796439164715008 |
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| author | Hans Ole Ørka Jãnis Gailis Mathias Vege Terje Gobakken Kenneth Hauglund |
| author_facet | Hans Ole Ørka Jãnis Gailis Mathias Vege Terje Gobakken Kenneth Hauglund |
| author_sort | Hans Ole Ørka |
| collection | DOAJ |
| description | Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery. • The method described produces analysis-ready satellite data mosaics. • The satellite data mosaics contain pixel metadata usable for further analysis. • The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms. |
| first_indexed | 2024-03-13T03:33:06Z |
| format | Article |
| id | doaj.art-198f8f0f19604a4682ac4830876c53b1 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-13T03:33:06Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-198f8f0f19604a4682ac4830876c53b12023-06-24T05:16:57ZengElsevierMethodsX2215-01612023-01-0110101995Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imageryHans Ole Ørka0Jãnis Gailis1Mathias Vege2Terje Gobakken3Kenneth Hauglund4Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, Ås NO-1432, Norway; Corresponding author.Science [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, NorwayScience [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, NorwayFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, Ås NO-1432, NorwayScience [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, NorwayToday's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery. • The method described produces analysis-ready satellite data mosaics. • The satellite data mosaics contain pixel metadata usable for further analysis. • The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms.http://www.sciencedirect.com/science/article/pii/S2215016122003697Geomosaic - analysis-ready satellite data mosaics |
| spellingShingle | Hans Ole Ørka Jãnis Gailis Mathias Vege Terje Gobakken Kenneth Hauglund Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery MethodsX Geomosaic - analysis-ready satellite data mosaics |
| title | Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery |
| title_full | Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery |
| title_fullStr | Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery |
| title_full_unstemmed | Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery |
| title_short | Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery |
| title_sort | analysis ready satellite data mosaics from landsat and sentinel 2 imagery |
| topic | Geomosaic - analysis-ready satellite data mosaics |
| url | http://www.sciencedirect.com/science/article/pii/S2215016122003697 |
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