Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome
Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly access...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9531417/ |
| _version_ | 1811204439641423872 |
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| author | Karol Stanski Isla H. Myers-Smith Christopher G. Lucas |
| author_facet | Karol Stanski Isla H. Myers-Smith Christopher G. Lucas |
| author_sort | Karol Stanski |
| collection | DOAJ |
| description | Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species <italic>E. vaginatum</italic> that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: <uri>https://github.com/karoleks4/flower-detection</uri>.) |
| first_indexed | 2024-04-12T03:13:06Z |
| format | Article |
| id | doaj.art-6652703eb20c462ab163071acefa992d |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-12T03:13:06Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-6652703eb20c462ab163071acefa992d2022-12-22T03:50:17ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01149287929610.1109/JSTARS.2021.31103659531417Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra BiomeKarol Stanski0https://orcid.org/0000-0001-7567-9722Isla H. Myers-Smith1https://orcid.org/0000-0002-8417-6112Christopher G. Lucas2https://orcid.org/0000-0002-6655-8627School of Informatics, University of Edinburgh, Edinburgh, U.K.School of GeoSciences, University of Edinburgh, Edinburgh, U.K.School of Informatics, University of Edinburgh, Edinburgh, U.K.Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species <italic>E. vaginatum</italic> that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: <uri>https://github.com/karoleks4/flower-detection</uri>.)https://ieeexplore.ieee.org/document/9531417/Object recognitionremote sensing |
| spellingShingle | Karol Stanski Isla H. Myers-Smith Christopher G. Lucas Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Object recognition remote sensing |
| title | Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome |
| title_full | Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome |
| title_fullStr | Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome |
| title_full_unstemmed | Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome |
| title_short | Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome |
| title_sort | flower detection using object analysis new ways to quantify plant phenology in a warming tundra biome |
| topic | Object recognition remote sensing |
| url | https://ieeexplore.ieee.org/document/9531417/ |
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