Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification)
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 an...
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Format: | Dataset |
Language: | English |
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UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation
2020
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Online Access: | https://dx.doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01293 |
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openpolar |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
"EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","ICE SHEETS" "EARTH SCIENCE","CRYOSPHERE","SNOW/ICE","ALBEDO" "EARTH SCIENCE","LAND SURFACE","LAND USE/LAND COVER" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","ALBEDO" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","REFLECTANCE" UAS albedo ice remote sensing |
spellingShingle |
"EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","ICE SHEETS" "EARTH SCIENCE","CRYOSPHERE","SNOW/ICE","ALBEDO" "EARTH SCIENCE","LAND SURFACE","LAND USE/LAND COVER" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","ALBEDO" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","REFLECTANCE" UAS albedo ice remote sensing Tedstone, Andrew Cook, Joseph Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
topic_facet |
"EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","ICE SHEETS" "EARTH SCIENCE","CRYOSPHERE","SNOW/ICE","ALBEDO" "EARTH SCIENCE","LAND SURFACE","LAND USE/LAND COVER" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","ALBEDO" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","REFLECTANCE" UAS albedo ice remote sensing |
description |
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. : 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. : Instrumentation: MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter. ASD FieldSpec Pro 3. : Good. Flights only undertaken on clear-sky days unless otherwise specified. |
format |
Dataset |
author |
Tedstone, Andrew Cook, Joseph |
author_facet |
Tedstone, Andrew Cook, Joseph |
author_sort |
Tedstone, Andrew |
title |
Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
title_short |
Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
title_full |
Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
title_fullStr |
Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
title_full_unstemmed |
Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
title_sort |
multi-spectral unmanned aerial system imagery, s6, south-west greenland, july 2017: levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) |
publisher |
UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation |
publishDate |
2020 |
url |
https://dx.doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01293 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_relation |
https://blackandbloom.org https://blackandbloom.org https://dx.doi.org/https://doi.org/10.5285/a87b7897-354c-4435-a1bc-e6053e7569e0 https://dx.doi.org/https://doi.org/10.5285/0579d4a8-e315-41d7-af43-25fb50c7d3da https://dx.doi.org/10.5285/ab953cb8-8675-4a85-b561-add6ceba015f https://dx.doi.org/10.5285/2dd66461-94af-458f-a9d2-c24bb0bd0322 https://dx.doi.org/10.5285/8e0a573d-61a4-4a6f-9fca-fc34cbd5fb45 |
op_rights |
Open Government Licence V3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 |
op_doi |
https://doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc https://doi.org/https://doi.org/10.5285/a87b7897-354c-4435-a1bc-e6053e7569e0 https://doi.org/https://doi.org/10.5285/0579d4a8-e315-41d7-af43-25fb50c7d3da https://doi.org/10.5285/ab953cb8-8 |
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1766019500734414848 |
spelling |
ftdatacite:10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc 2023-05-15T16:29:47+02:00 Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification) Tedstone, Andrew Cook, Joseph 2020 text/plain text/csv application/x-hdf application/netcdf https://dx.doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01293 en eng UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation https://blackandbloom.org https://blackandbloom.org https://dx.doi.org/https://doi.org/10.5285/a87b7897-354c-4435-a1bc-e6053e7569e0 https://dx.doi.org/https://doi.org/10.5285/0579d4a8-e315-41d7-af43-25fb50c7d3da https://dx.doi.org/10.5285/ab953cb8-8675-4a85-b561-add6ceba015f https://dx.doi.org/10.5285/2dd66461-94af-458f-a9d2-c24bb0bd0322 https://dx.doi.org/10.5285/8e0a573d-61a4-4a6f-9fca-fc34cbd5fb45 Open Government Licence V3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 "EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","ICE SHEETS" "EARTH SCIENCE","CRYOSPHERE","SNOW/ICE","ALBEDO" "EARTH SCIENCE","LAND SURFACE","LAND USE/LAND COVER" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","ALBEDO" "EARTH SCIENCE","LAND SURFACE","SURFACE RADIATIVE PROPERTIES","REFLECTANCE" UAS albedo ice remote sensing Dataset dataset UAS,albedo,ice,remote sensing 2020 ftdatacite https://doi.org/10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc https://doi.org/https://doi.org/10.5285/a87b7897-354c-4435-a1bc-e6053e7569e0 https://doi.org/https://doi.org/10.5285/0579d4a8-e315-41d7-af43-25fb50c7d3da https://doi.org/10.5285/ab953cb8-8 2021-11-05T12:55:41Z 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. : 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. : Instrumentation: MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter. ASD FieldSpec Pro 3. : Good. Flights only undertaken on clear-sky days unless otherwise specified. Dataset Greenland DataCite Metadata Store (German National Library of Science and Technology) Greenland |