Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 03)
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Dataset Open Access |
| Lenguaje: | en |
| Publicado: |
PANGAEA
2024
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| _version_ | 1867172192232407040 |
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| author | Rettelbach, Tabea Nitze, Ingmar Grünberg, Inge Hammar, Jennika Schäffler, Simon Hein, Daniel Gessner, Matthias Bucher, Tilman Brauchle, Jörg Hartmann, Jörg Sachs, Torsten Boike, Julia Grosse, Guido |
| author_facet | Rettelbach, Tabea Nitze, Ingmar Grünberg, Inge Hammar, Jennika Schäffler, Simon Hein, Daniel Gessner, Matthias Bucher, Tilman Brauchle, Jörg Hartmann, Jörg Sachs, Torsten Boike, Julia Grosse, Guido |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.22 pts/m² and 1.15 pts/m², respectively. This dataset covers approximately 31.21 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_962736 |
| institution | PANGAEA |
| language | en |
| publishDate | 2024 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 03) Rettelbach, Tabea Nitze, Ingmar Grünberg, Inge Hammar, Jennika Schäffler, Simon Hein, Daniel Gessner, Matthias Bucher, Tilman Brauchle, Jörg Hartmann, Jörg Sachs, Torsten Boike, Julia Grosse, Guido AC; Airborne Data; Aircraft; Alaska; Arctic; Arctic Landscape Dynamics; Binary Object; Binary Object (File Size); Digital Surface Model; File content; MACS; Orthoimagery; P6_224_Perma_X_2021_2107020801; P6-224_Perma_X_2021; Permafrost; Perma-X 2021 Western Alaska; point clouds; POLAR 6; Project; Structure-from-Motion As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.22 pts/m² and 1.15 pts/m², respectively. This dataset covers approximately 31.21 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations. |
| title | Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 03) |
| topic | AC; Airborne Data; Aircraft; Alaska; Arctic; Arctic Landscape Dynamics; Binary Object; Binary Object (File Size); Digital Surface Model; File content; MACS; Orthoimagery; P6_224_Perma_X_2021_2107020801; P6-224_Perma_X_2021; Permafrost; Perma-X 2021 Western Alaska; point clouds; POLAR 6; Project; Structure-from-Motion |
| url | https://doi.org/10.1594/PANGAEA.962736 |