Multi-spectral unmanned aerial system imagery, UPE_U, north-west Greenland, July 2018: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification)
GB/NERC/BAS/PDC/01294
Abstract:
This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at UPE_U in north-west Greenland on 24 July 2018. The Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) a digital elevation model. 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 which was trained on measurements collected at S6, K-transect, south-west Greenland.
Funding was provided by the NERC standard grant NE/M021025/1.
Citation:
Tedstone, A., & Cook, J. (2020). Multi-spectral unmanned aerial system imagery, UPE_U, north-west Greenland, July 2018: 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/10.5285/2dd66461-94af-458f-a9d2-c24bb0bd0322
Access Data
GET DATA
PROJECT HOME PAGE
RELATED DATA SET METADATA
RELATED DATA SET METADATA
RELATED DATA SET METADATA
RELATED DATA SET METADATA
RELATED DATA SET METADATA
License
| Access: | No restrictions apply. |
|---|---|
| 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 |
|---|---|
| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
|
| Parameters: |
|
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 |
|
|---|---|---|
| Quality: | Good. | |
| 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. |
|
Extent
| Temporal Coverage: | |
|---|---|
| Start Date | 2018-07-24 |
| End Date | 2018-07-24 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | 72.88 |
| Northernmost | 72.88 |
| Longitude | |
| Westernmost | -53.56 |
| Easternmost | -53.55 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Location: | |
| Location | Greenland |
| Detailed Location | UPE_U, north-west Greenland Ice Sheet |
Instrumentation
| Data Collection: | Instrumentation: MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter. |
|---|
Storage
| Data Storage: | 1 UAS mission was processed to Level-2/3 out of the 4 in the underlying Level-1 dataset. The 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, NetCDF - Level-3 albedo and surface type classification, NetCDF |
|---|