Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification)
GB/NERC/BAS/PDC/01293
Abstract:
This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at S6 on the south-west Greenland K-transect during July 2017. Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) digital elevation models (only for 2017-07-20 and 2017-07-21). Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm. Training data ingested by the classification algorithm are also provided.
Funding was provided by the NERC standard grant NE/M021025/1.
Citation:
Tedstone, A., & Cook, J. (2020). Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc
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License
| Access: | No restrictions apply. |
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| Use: | This data is governed by the NERC data policy http://www.nerc.ac.uk/research/sites/data/policy/ and supplied under Open Government Licence v.3 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. |
Basic Information
| Creation Date: | 2020-01-08 |
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| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
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| Parameters: |
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Additional Information
| Reference: | Cook et al. (accepted) Glacier algae accelerate melt rates on the western Greenland Ice Sheet, The Cryosphere Tedstone et al. (accepted) Algal growth and weathering crust state drive variability in Greenland Ice Sheet ice albedo, The Cryosphere |
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| Quality: | Good. Flights only undertaken on clear-sky days unless otherwise specified. | |
| Lineage/Methodology: | Multispectral imagery were acquired using a MicaSense RedEdge camera mounted on a Steadidrone Mavik-M quadcopter flown at a height of 30 m above the ice surface with 60% overlap and 40% sidelap. Radiometric calbiration and geometric distortion correction applied in post-processing. Data converted from radiance to reflectance using calibrated reflectance panels. Images mosaiced using AgiSoft PhotoScan at 5 cm final ground resolution. The orthomosaics were used in three ways: (i) converted to albedo using a narrowband-to-broadband approximation (Knap et al 1999, Int. J. Remote Sens.), (ii) classified into surface types, and (iii) digital elevation models derived photogrametrically in Agisoft PhotoScan at 5 cm ground resolution. To classify images by surface type we used a supervised classification approach following Cook et al. (2020, The Cryosphere), trained on ground spectra collected at S6 with a FieldSpec Pro 3 (Analytical Spectral Devices, Boulder, USA) during the 2016 and 2017 field seasons at S6. Briefly, we used 171 directional reflectance measurements. The measurements were labelled by visual examination as snow ('SN'), water ('WA'), clean ice ('CI'), light algae ('LA'), heavy algae ('HA') and dispersed cryoconite ('CC'). After ground spectra were acquired we took destructive ground samples (see Tedstone et al 2020 TC for more details). We split the field dataset randomly into training (70%) and test (30%) sets. These data were used to train a Random Forest classifier. We trained the algorithm to predict surface type, utilising all 5 bands of data. Narrowband-to-broadband approximations for albedo calculations were employed because empirical Bi-directional Reflectance Distribution Functions (BRDFs) are not available for the surface types that we mapped. We used the photogrammetric DEMs to derive (i) study area slope angle and (ii) local topographic variability. To calculate the slope angle we applied a gaussian filter with a window of 0.25 m to remove very-high-frequency topographic features, then we calculated the average slope across each study area. To examine local topographic variability ('roughness') we applied a gaussian filter with a window of 4.95 m, then subtracted it from the DEM to yield a detrended surface. |
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Extent
| Temporal Coverage: | |
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| Start Date | 2017-07-15 |
| End Date | 2017-07-24 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | 67.08 |
| Northernmost | 67.08 |
| Longitude | |
| Westernmost | -49.35 |
| Easternmost | -49.35 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Data Resolution: | |
| Latitude Resolution | N/A |
| Longitude Resolution | N/A |
| Horizontal Resolution Range | < 1 meter |
| Vertical Resolution | N/A |
| Vertical Resolution Range | N/A |
| Temporal Resolution | N/A |
| Temporal Resolution Range | N/A |
| Location: | |
| Location | Greenland |
| Detailed Location | S6, K-Transect, south-west Greenland Ice Sheet |
Instrumentation
| Data Collection: | Instrumentation: MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter. ASD FieldSpec Pro 3. |
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Storage
| Data Storage: | 7 UAS missions were processed to Level-2/3 out of the 8 in the underlying Level-1 dataset. Each mission consists of three files, all projected to a common UTM 22N grid: - Level-2 ground reflectance, NetCDF, one variable per spectral band: Band 1: 465-485 nm Band 2: 550-570 nm Band 3: 663-673 nm Band 4: 820-860 nm Band 5: 712-722 nm - Level-2 digital elevation model (only for 2017-07-20, 2017-07-21), NetCDF - Level-3 albedo and surface type classification, NetCDF Ancillary supporting files used to generate Level-2 data consist of: - pixel_gcps_kely_formatted.csv. Coordinates of the ground control points used to generate the georeferenced orthomosaics. - uav_sb_locations.csv. Coordinates of the destructively-sampled locations in the survey area. - Calmodel_DL03-1706018-SC.config. Calibration Model parameters of the RedEdge camera used to acquire the imagery data. Ancillary supporting files used to generate Level-3 data consist of: - HCRF_master_machine_snicar.csv. Field spectra measurements. - field_spectra_classes.csv. Allocates each field spectra measurement to a surface type class, for ingestion to supervised classification algorithm. The HCRF data used to train the model used in the manuscript associated with this data repository can be found in 10.5281/zenodo.3564501. |
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