Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring
Mapping of Antarctic Specially Protected Areas (ASPAs) is a critical aspect of conservation efforts. However, traditional ground-based methods can be time-consuming, expensive, and risky, particularly in remote and extreme environments such as Antarctica. Another challenge is the limited availabilit...
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ftqueensland:oai:eprints.qut.edu.au:245290 2024-02-11T09:58:48+01:00 Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring Sandino, Juan Bollard, Barbara Doshi, Ashray Barthelemy, Johan Gonzalez, Felipe 2023-07-31 application/pdf https://eprints.qut.edu.au/245290/ unknown https://eprints.qut.edu.au/245290/1/SCAR23_hyperspectral_slider.pdf Sandino, Juan, Bollard, Barbara, Doshi, Ashray, Barthelemy, Johan, & Gonzalez, Felipe (2023) Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring. In XIII SCAR Biology Symposium 2023, 2023-07-31 - 2023-08-04, Christchurch,New Zealand. (Unpublished) http://purl.org/au-research/grants/arc/SR200100005 http://purl.org/au-research/grants/arc/T2-P023 http://purl.org/au-research/grants/arc/T2-P016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/245290/ Faculty of Engineering; School of Electrical Engineering & Robotics free_to_read http://creativecommons.org/licenses/by/4.0/ Consult author(s) regarding copyright matters 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 XIII SCAR Biology Symposium 2023 Contribution to conference 2023 ftqueensland 2024-01-15T23:26:52Z Mapping of Antarctic Specially Protected Areas (ASPAs) is a critical aspect of conservation efforts. However, traditional ground-based methods can be time-consuming, expensive, and risky, particularly in remote and extreme environments such as Antarctica. Another challenge is the limited availability of high-quality and up-to-date satellite data, which can hinder the accuracy of the mapping process. This limitation highlights the need for more advanced remote sensing techniques that can provide more precise and real-time data. The use of drones and spectral imaging has emerged as an efficient and cost-effective solution for mapping and has demonstrated significant potential in environmental monitoring and assessment. However, there is still the need for robust and accurate machine learning algorithms that can handle the large amount of data collected by drones and spectral imaging devices. As such, the development of advanced machine learning models that can process and analyse the data in real-time is a crucial area of research for the mapping and monitoring of ASPAs. This study demonstrates the use of innovative drones, multispectral/hyperspectral data, and applied AI for detecting and mapping the distribution of moss, lichens, and other vegetation in an ASPA managed by the Australian Antarctic Division (AAD). By utilizing machine learning algorithms, we processed the data to identify the spectral signatures of different habitats and species. The integration of advanced technologies resulted in accurate mapping and monitoring of vegetation in extreme environments compared to traditional methods. This technology has significant potential to aid in the monitoring and assessment of the impact of climate change on the Antarctic ecosystem. Overall, our study highlights the value of innovative technologies in environmental monitoring and provides insight into the future of conservation and management practices in Antarctica. Conference Object Antarc* Antarctic Antarctica Australian Antarctic Division Queensland University of Technology: QUT ePrints Antarctic The Antarctic Handle The ENVELOPE(161.983,161.983,-78.000,-78.000) |
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Queensland University of Technology: QUT ePrints |
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ftqueensland |
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description |
Mapping of Antarctic Specially Protected Areas (ASPAs) is a critical aspect of conservation efforts. However, traditional ground-based methods can be time-consuming, expensive, and risky, particularly in remote and extreme environments such as Antarctica. Another challenge is the limited availability of high-quality and up-to-date satellite data, which can hinder the accuracy of the mapping process. This limitation highlights the need for more advanced remote sensing techniques that can provide more precise and real-time data. The use of drones and spectral imaging has emerged as an efficient and cost-effective solution for mapping and has demonstrated significant potential in environmental monitoring and assessment. However, there is still the need for robust and accurate machine learning algorithms that can handle the large amount of data collected by drones and spectral imaging devices. As such, the development of advanced machine learning models that can process and analyse the data in real-time is a crucial area of research for the mapping and monitoring of ASPAs. This study demonstrates the use of innovative drones, multispectral/hyperspectral data, and applied AI for detecting and mapping the distribution of moss, lichens, and other vegetation in an ASPA managed by the Australian Antarctic Division (AAD). By utilizing machine learning algorithms, we processed the data to identify the spectral signatures of different habitats and species. The integration of advanced technologies resulted in accurate mapping and monitoring of vegetation in extreme environments compared to traditional methods. This technology has significant potential to aid in the monitoring and assessment of the impact of climate change on the Antarctic ecosystem. Overall, our study highlights the value of innovative technologies in environmental monitoring and provides insight into the future of conservation and management practices in Antarctica. |
format |
Conference Object |
author |
Sandino, Juan Bollard, Barbara Doshi, Ashray Barthelemy, Johan Gonzalez, Felipe |
spellingShingle |
Sandino, Juan Bollard, Barbara Doshi, Ashray Barthelemy, Johan Gonzalez, Felipe Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
author_facet |
Sandino, Juan Bollard, Barbara Doshi, Ashray Barthelemy, Johan Gonzalez, Felipe |
author_sort |
Sandino, Juan |
title |
Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
title_short |
Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
title_full |
Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
title_fullStr |
Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
title_full_unstemmed |
Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring |
title_sort |
enhancing antarctic conservation with advanced remote sensing and machine learning techniques for aspa scale mapping and monitoring |
publishDate |
2023 |
url |
https://eprints.qut.edu.au/245290/ |
long_lat |
ENVELOPE(161.983,161.983,-78.000,-78.000) |
geographic |
Antarctic The Antarctic Handle The |
geographic_facet |
Antarctic The Antarctic Handle The |
genre |
Antarc* Antarctic Antarctica Australian Antarctic Division |
genre_facet |
Antarc* Antarctic Antarctica Australian Antarctic Division |
op_source |
XIII SCAR Biology Symposium 2023 |
op_relation |
https://eprints.qut.edu.au/245290/1/SCAR23_hyperspectral_slider.pdf Sandino, Juan, Bollard, Barbara, Doshi, Ashray, Barthelemy, Johan, & Gonzalez, Felipe (2023) Enhancing Antarctic Conservation with Advanced Remote Sensing and Machine Learning Techniques for ASPA Scale Mapping and Monitoring. In XIII SCAR Biology Symposium 2023, 2023-07-31 - 2023-08-04, Christchurch,New Zealand. (Unpublished) http://purl.org/au-research/grants/arc/SR200100005 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/245290/ Faculty of Engineering; School of Electrical Engineering & Robotics |
op_rights |
free_to_read http://creativecommons.org/licenses/by/4.0/ Consult author(s) regarding copyright matters 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 |
_version_ |
1790594559680446464 |