Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control
Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the cu...
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ftdoajarticles:oai:doaj.org/article:a1227e36cba746f5844e2568e9a22996 2023-05-15T14:04:22+02:00 Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control Teng Li Baogang Zhang Xiao Cheng Matthew J. Westoby Zhenhong Li Chi Ma Fengming Hui Mohammed Shokr Yan Liu Zhuoqi Chen Mengxi Zhai Xinqing Li 2019-04-01T00:00:00Z https://doi.org/10.3390/rs11070784 https://doaj.org/article/a1227e36cba746f5844e2568e9a22996 EN eng MDPI AG https://www.mdpi.com/2072-4292/11/7/784 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11070784 https://doaj.org/article/a1227e36cba746f5844e2568e9a22996 Remote Sensing, Vol 11, Iss 7, p 784 (2019) landfast sea ice unmanned aerial system (UAS) Antarctic expedition structure from motion (SfM) surface features photogrammetry Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11070784 2022-12-30T20:28:50Z Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to ... Article in Journal/Newspaper Antarc* Antarctic Antarctica East Antarctica Prydz Bay Sea ice Directory of Open Access Journals: DOAJ Articles Antarctic East Antarctica Austral Prydz Bay Remote Sensing 11 7 784 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
landfast sea ice unmanned aerial system (UAS) Antarctic expedition structure from motion (SfM) surface features photogrammetry Science Q |
spellingShingle |
landfast sea ice unmanned aerial system (UAS) Antarctic expedition structure from motion (SfM) surface features photogrammetry Science Q Teng Li Baogang Zhang Xiao Cheng Matthew J. Westoby Zhenhong Li Chi Ma Fengming Hui Mohammed Shokr Yan Liu Zhuoqi Chen Mengxi Zhai Xinqing Li Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
topic_facet |
landfast sea ice unmanned aerial system (UAS) Antarctic expedition structure from motion (SfM) surface features photogrammetry Science Q |
description |
Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to ... |
format |
Article in Journal/Newspaper |
author |
Teng Li Baogang Zhang Xiao Cheng Matthew J. Westoby Zhenhong Li Chi Ma Fengming Hui Mohammed Shokr Yan Liu Zhuoqi Chen Mengxi Zhai Xinqing Li |
author_facet |
Teng Li Baogang Zhang Xiao Cheng Matthew J. Westoby Zhenhong Li Chi Ma Fengming Hui Mohammed Shokr Yan Liu Zhuoqi Chen Mengxi Zhai Xinqing Li |
author_sort |
Teng Li |
title |
Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
title_short |
Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
title_full |
Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
title_fullStr |
Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
title_full_unstemmed |
Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control |
title_sort |
resolving fine-scale surface features on polar sea ice: a first assessment of uas photogrammetry without ground control |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11070784 https://doaj.org/article/a1227e36cba746f5844e2568e9a22996 |
geographic |
Antarctic East Antarctica Austral Prydz Bay |
geographic_facet |
Antarctic East Antarctica Austral Prydz Bay |
genre |
Antarc* Antarctic Antarctica East Antarctica Prydz Bay Sea ice |
genre_facet |
Antarc* Antarctic Antarctica East Antarctica Prydz Bay Sea ice |
op_source |
Remote Sensing, Vol 11, Iss 7, p 784 (2019) |
op_relation |
https://www.mdpi.com/2072-4292/11/7/784 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11070784 https://doaj.org/article/a1227e36cba746f5844e2568e9a22996 |
op_doi |
https://doi.org/10.3390/rs11070784 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
7 |
container_start_page |
784 |
_version_ |
1766275408878108672 |