Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI
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...
Published in: | Sensors |
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Main Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
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Multidisciplinary Digital Publishing Institute
2024
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Online Access: | https://eprints.qut.edu.au/246328/ |
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Queensland University of Technology: QUT ePrints |
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ftqueensland |
language |
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topic |
machine learning convolution neural network (CNN) moss UAV Antarctica gradient boosting Drone |
spellingShingle |
machine learning convolution neural network (CNN) moss UAV Antarctica gradient boosting Drone Raniga, Damini Amarasingam, Narmilan Sandino, Juan Doshi, Ashray Barthelemy, Johan Randall, Krystal Robinson, Sharon A. Gonzalez, Luis Felipe Bollard, Barbara Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
topic_facet |
machine learning convolution neural network (CNN) moss UAV Antarctica gradient boosting Drone |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Raniga, Damini Amarasingam, Narmilan Sandino, Juan Doshi, Ashray Barthelemy, Johan Randall, Krystal Robinson, Sharon A. Gonzalez, Luis Felipe Bollard, Barbara |
author_facet |
Raniga, Damini Amarasingam, Narmilan Sandino, Juan Doshi, Ashray Barthelemy, Johan Randall, Krystal Robinson, Sharon A. Gonzalez, Luis Felipe Bollard, Barbara |
author_sort |
Raniga, Damini |
title |
Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
title_short |
Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
title_full |
Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
title_fullStr |
Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
title_full_unstemmed |
Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI |
title_sort |
monitoring of antarctica's fragile vegetation using drone-based remote sensing, multispectral imagery and ai |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2024 |
url |
https://eprints.qut.edu.au/246328/ |
genre |
Antarc* Antarctic Antarctica East Antarctica |
genre_facet |
Antarc* Antarctic Antarctica East Antarctica |
op_source |
Sensors |
op_relation |
https://eprints.qut.edu.au/246328/1/sensors_24_01063.pdf https://www.mdpi.com/1424-8220/24/4/1063 doi:10.3390/s24041063 Raniga, Damini, Amarasingam, Narmilan, Sandino, Juan, Doshi, Ashray, Barthelemy, Johan, Randall, Krystal, Robinson, Sharon A., Gonzalez, Luis Felipe, & Bollard, Barbara (2024) Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI. Sensors, 24(4), Article number: 1063. http://purl.org/au-research/grants/arc/SR200100005 http://purl.org/au-research/grants/arc/T3_P028 http://purl.org/au-research/grants/arc/T2-P023 http://purl.org/au-research/grants/arc/T2-P016 http://purl.org/au-research/grants/arc/T2-P036 https://eprints.qut.edu.au/246328/ Faculty of Engineering; School of Electrical Engineering & Robotics |
op_rights |
free_to_read http://creativecommons.org/licenses/by/4.0/ The authors This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au |
op_doi |
https://doi.org/10.3390/s24041063 |
container_title |
Sensors |
container_volume |
24 |
container_issue |
4 |
container_start_page |
1063 |
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1799482824285224960 |
spelling |
ftqueensland:oai:eprints.qut.edu.au:246328 2024-05-19T07:29:59+00:00 Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI Raniga, Damini Amarasingam, Narmilan Sandino, Juan Doshi, Ashray Barthelemy, Johan Randall, Krystal Robinson, Sharon A. Gonzalez, Luis Felipe Bollard, Barbara 2024-02-06 application/pdf https://eprints.qut.edu.au/246328/ unknown Multidisciplinary Digital Publishing Institute https://eprints.qut.edu.au/246328/1/sensors_24_01063.pdf https://www.mdpi.com/1424-8220/24/4/1063 doi:10.3390/s24041063 Raniga, Damini, Amarasingam, Narmilan, Sandino, Juan, Doshi, Ashray, Barthelemy, Johan, Randall, Krystal, Robinson, Sharon A., Gonzalez, Luis Felipe, & Bollard, Barbara (2024) Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI. Sensors, 24(4), Article number: 1063. http://purl.org/au-research/grants/arc/SR200100005 http://purl.org/au-research/grants/arc/T3_P028 http://purl.org/au-research/grants/arc/T2-P023 http://purl.org/au-research/grants/arc/T2-P016 http://purl.org/au-research/grants/arc/T2-P036 https://eprints.qut.edu.au/246328/ Faculty of Engineering; School of Electrical Engineering & Robotics free_to_read http://creativecommons.org/licenses/by/4.0/ The authors This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au Sensors machine learning convolution neural network (CNN) moss UAV Antarctica gradient boosting Drone Contribution to Journal 2024 ftqueensland https://doi.org/10.3390/s24041063 2024-04-24T00:09:08Z 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 ... Article in Journal/Newspaper Antarc* Antarctic Antarctica East Antarctica Queensland University of Technology: QUT ePrints Sensors 24 4 1063 |