Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard
A methodology was tested for high‐resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength – RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resoluti...
Published in: | Geografiska Annaler: Series A, Physical Geography |
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Language: | English |
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Online Access: | http://hdl.handle.net/10451/35937 https://doi.org/10.1111/geoa.12088 |
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ftunivlisboa:oai:repositorio.ul.pt:10451/35937 2023-05-15T13:05:43+02:00 Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard Mora, Carla Vieira, Goncalo Pina, Pedro Lousada, Maura Christiansen, Hanne H. 2018-12-17T15:43:21Z http://hdl.handle.net/10451/35937 https://doi.org/10.1111/geoa.12088 eng eng Taylor & Francis https://www.tandfonline.com/doi/full/10.1111/geoa.12088 Mora, Carla, Vieira, Gonçalo, Pina, Pedro, Lousada, Maura, Christiansen, Hanne H. (2015). Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard. Geografiska Annaler: Series A, Physical Geography, 97:3, 473-488, DOI:10.1111/geoa.12088 0435-3676 1468-0459 http://hdl.handle.net/10451/35937 doi:10.1111/geoa.12088 openAccess high‐resolution remote sensing near infrared UAV vegetation Svalbard article 2018 ftunivlisboa https://doi.org/10.1111/geoa.12088 2022-05-25T18:39:04Z A methodology was tested for high‐resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength – RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resolution). The scenes were fused at 10 and 20 cm to evaluate their applicability for vegetation mapping in an alluvial fan in dventdalen, Svalbard. Ground‐truthing was used to create training and accuracy evaluation sets. Supervised classification tests were conducted with different band sets, including the original and derived ones, such as and principal component analysis bands. The fusion of all original bands at 10 cm resolution provided the best accuracies. The best classifier was systematically the maximum neighbourhood algorithm, with overall accuracies up to 84%. Mapped vegetation patterns reflect geoecological conditioning factors. The main limitation in the classification was differentiating between the classes graminea, moss and Salix, and moss, graminea and Salix, which showed spectral signature mixing. Silty‐clay surfaces are probably overestimated in the south part of the study area due to microscale shadowing effects. The results distinguished vegetation zones according to a general gradient of ecological limiting factors and show that + high‐resolution imagery are excellent tools for identifying the main vegetation groups within the lowland fan study site of dventdalen, but do not allow for detailed discrimination between species. info:eu-repo/semantics/publishedVersion Article in Journal/Newspaper Adventdalen Arctic Svalbard Tundra Universidade de Lisboa: repositório.UL Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Svalbard Geografiska Annaler: Series A, Physical Geography 97 3 473 488 |
institution |
Open Polar |
collection |
Universidade de Lisboa: repositório.UL |
op_collection_id |
ftunivlisboa |
language |
English |
topic |
high‐resolution remote sensing near infrared UAV vegetation Svalbard |
spellingShingle |
high‐resolution remote sensing near infrared UAV vegetation Svalbard Mora, Carla Vieira, Goncalo Pina, Pedro Lousada, Maura Christiansen, Hanne H. Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
topic_facet |
high‐resolution remote sensing near infrared UAV vegetation Svalbard |
description |
A methodology was tested for high‐resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength – RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resolution). The scenes were fused at 10 and 20 cm to evaluate their applicability for vegetation mapping in an alluvial fan in dventdalen, Svalbard. Ground‐truthing was used to create training and accuracy evaluation sets. Supervised classification tests were conducted with different band sets, including the original and derived ones, such as and principal component analysis bands. The fusion of all original bands at 10 cm resolution provided the best accuracies. The best classifier was systematically the maximum neighbourhood algorithm, with overall accuracies up to 84%. Mapped vegetation patterns reflect geoecological conditioning factors. The main limitation in the classification was differentiating between the classes graminea, moss and Salix, and moss, graminea and Salix, which showed spectral signature mixing. Silty‐clay surfaces are probably overestimated in the south part of the study area due to microscale shadowing effects. The results distinguished vegetation zones according to a general gradient of ecological limiting factors and show that + high‐resolution imagery are excellent tools for identifying the main vegetation groups within the lowland fan study site of dventdalen, but do not allow for detailed discrimination between species. info:eu-repo/semantics/publishedVersion |
format |
Article in Journal/Newspaper |
author |
Mora, Carla Vieira, Goncalo Pina, Pedro Lousada, Maura Christiansen, Hanne H. |
author_facet |
Mora, Carla Vieira, Goncalo Pina, Pedro Lousada, Maura Christiansen, Hanne H. |
author_sort |
Mora, Carla |
title |
Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
title_short |
Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
title_full |
Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
title_fullStr |
Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
title_full_unstemmed |
Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard |
title_sort |
land cover classification using high‐resolution aerial photography in adventdalen, svalbard |
publisher |
Taylor & Francis |
publishDate |
2018 |
url |
http://hdl.handle.net/10451/35937 https://doi.org/10.1111/geoa.12088 |
long_lat |
ENVELOPE(16.264,16.264,78.181,78.181) |
geographic |
Adventdalen Arctic Svalbard |
geographic_facet |
Adventdalen Arctic Svalbard |
genre |
Adventdalen Arctic Svalbard Tundra |
genre_facet |
Adventdalen Arctic Svalbard Tundra |
op_relation |
https://www.tandfonline.com/doi/full/10.1111/geoa.12088 Mora, Carla, Vieira, Gonçalo, Pina, Pedro, Lousada, Maura, Christiansen, Hanne H. (2015). Land cover classification using high‐resolution aerial photography in Adventdalen, Svalbard. Geografiska Annaler: Series A, Physical Geography, 97:3, 473-488, DOI:10.1111/geoa.12088 0435-3676 1468-0459 http://hdl.handle.net/10451/35937 doi:10.1111/geoa.12088 |
op_rights |
openAccess |
op_doi |
https://doi.org/10.1111/geoa.12088 |
container_title |
Geografiska Annaler: Series A, Physical Geography |
container_volume |
97 |
container_issue |
3 |
container_start_page |
473 |
op_container_end_page |
488 |
_version_ |
1766392140422709248 |