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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Dataset Open Access |
| Language: | en |
| Published: |
PANGAEA
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.1594/PANGAEA.962004 |
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| _version_ | 1867172191497355264 |
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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 MOSES airborne campaign led by the Alfred Wegener Institute in 2018, 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 10 cm, as well as photogrammetric point clouds in RGB and NIR at 14.48 px/m³ and 4.74 px/m³ respectively. This dataset covers approximately 21.39 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. 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_962004 |
| 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 Trail Valley Creek, Canada (subset 05) 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; AIRMETH, SMART, AIRCOAST, PermaSAR; Arctic Landscape Dynamics; Binary Object; Binary Object (File Size); Canada; Digital Surface Model; File content; MACS; Modular Observation Solutions for Earth Systems; MOSES; Northwest Territories, Canada; Orthoimagery; P5_212_AIRMETH_2018_1808221501; P5-212_AIRMETH_2018; Permafrost; point clouds; POLAR 5; Project; Structure-from-Motion As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, 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 10 cm, as well as photogrammetric point clouds in RGB and NIR at 14.48 px/m³ and 4.74 px/m³ respectively. This dataset covers approximately 21.39 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. 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 Trail Valley Creek, Canada (subset 05) |
| topic | AC; Airborne Data; Aircraft; AIRMETH, SMART, AIRCOAST, PermaSAR; Arctic Landscape Dynamics; Binary Object; Binary Object (File Size); Canada; Digital Surface Model; File content; MACS; Modular Observation Solutions for Earth Systems; MOSES; Northwest Territories, Canada; Orthoimagery; P5_212_AIRMETH_2018_1808221501; P5-212_AIRMETH_2018; Permafrost; point clouds; POLAR 5; Project; Structure-from-Motion |
| url | https://doi.org/10.1594/PANGAEA.962004 |