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Román A, Navarro G, Tovar-Sánchez A, Zarandona P, Roque-Atienza D, Barbero L. ShetlandsUAVmetry: unmanned aerial vehicle-based photogrammetric dataset for Antarctic environmental research. Sci Data 2024; 11:202. [PMID: 38355698 PMCID: PMC10866955 DOI: 10.1038/s41597-024-03045-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024] Open
Abstract
The study of the functioning and responses of Antarctica to the current climate change scenario is a priority and a challenge for the scientific community aiming to predict and mitigate impacts at a regional and global scale. Due to the difficulty of obtaining aerial data in such extreme, remote, and difficult-to-reach region of the planet, the development of remote sensing techniques with Unmanned Aerial Vehicles (UAVs) has revolutionized polar research. ShetlandsUAVmetry comprises original datasets collected by UAVs during the Spanish Antarctic Campaign 2021-2022 (January to March 2022), along with the photogrammetric products resulting from their processing. It includes data recorded during twenty-eight distinct UAV flights at various study sites on Deception and Livingston islands (South Shetland Islands, Antarctica) and consists of a total of 15,691 high-resolution optical RGB captures. In addition, this dataset is accompanied by additional associated files that facilitate its use and accessibility. It is publicly accessible and can be downloaded from the figshare data repository.
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Affiliation(s)
- Alejandro Román
- Institute of Marine Sciences of Andalusia (ICMAN), Spanish National Research Council (CSIC), Department of Ecology and Coastal Management, 11510, Puerto Real, Spain.
| | - Gabriel Navarro
- Institute of Marine Sciences of Andalusia (ICMAN), Spanish National Research Council (CSIC), Department of Ecology and Coastal Management, 11510, Puerto Real, Spain
| | - Antonio Tovar-Sánchez
- Institute of Marine Sciences of Andalusia (ICMAN), Spanish National Research Council (CSIC), Department of Ecology and Coastal Management, 11510, Puerto Real, Spain
| | - Pedro Zarandona
- University of Cádiz, Department of Earth Sciences, International Campus of Excellence in Marine Science (CEIMAR), 11510, Puerto Real, Spain
| | - David Roque-Atienza
- King Abdullah University of Science and Technology (KAUST), 23955, Thuwal, Saudi Arabia
| | - Luis Barbero
- University of Cádiz, Department of Earth Sciences, International Campus of Excellence in Marine Science (CEIMAR), 11510, Puerto Real, Spain
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Raniga D, Amarasingam N, Sandino J, Doshi A, Barthelemy J, Randall K, Robinson SA, Gonzalez F, Bollard B. Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI. SENSORS (BASEL, SWITZERLAND) 2024; 24:1063. [PMID: 38400222 PMCID: PMC10892490 DOI: 10.3390/s24041063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/30/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024]
Abstract
Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.
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Affiliation(s)
- Damini Raniga
- School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane City, QLD 4000, Australia; (D.R.); (N.A.); (F.G.)
- Securing Antarctica’s Environmental Future (SAEF), Queensland University of Technology, Brisbane City, QLD 4000, Australia
| | - Narmilan Amarasingam
- School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane City, QLD 4000, Australia; (D.R.); (N.A.); (F.G.)
- Securing Antarctica’s Environmental Future (SAEF), Queensland University of Technology, Brisbane City, QLD 4000, Australia
| | - Juan Sandino
- School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane City, QLD 4000, Australia; (D.R.); (N.A.); (F.G.)
- Securing Antarctica’s Environmental Future (SAEF), Queensland University of Technology, Brisbane City, QLD 4000, Australia
| | - Ashray Doshi
- Securing Antarctica’s Environmental Future (SAEF), University of Wollongong, Wollongong, NSW 2522, Australia; (A.D.); (J.B.); (K.R.); (S.A.R.); (B.B.)
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Johan Barthelemy
- Securing Antarctica’s Environmental Future (SAEF), University of Wollongong, Wollongong, NSW 2522, Australia; (A.D.); (J.B.); (K.R.); (S.A.R.); (B.B.)
- NVIDIA, Santa Clara, CA 95051, USA
| | - Krystal Randall
- Securing Antarctica’s Environmental Future (SAEF), University of Wollongong, Wollongong, NSW 2522, Australia; (A.D.); (J.B.); (K.R.); (S.A.R.); (B.B.)
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Sharon A. Robinson
- Securing Antarctica’s Environmental Future (SAEF), University of Wollongong, Wollongong, NSW 2522, Australia; (A.D.); (J.B.); (K.R.); (S.A.R.); (B.B.)
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Felipe Gonzalez
- School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane City, QLD 4000, Australia; (D.R.); (N.A.); (F.G.)
- Securing Antarctica’s Environmental Future (SAEF), Queensland University of Technology, Brisbane City, QLD 4000, Australia
| | - Barbara Bollard
- Securing Antarctica’s Environmental Future (SAEF), University of Wollongong, Wollongong, NSW 2522, Australia; (A.D.); (J.B.); (K.R.); (S.A.R.); (B.B.)
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
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