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...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftdoajarticles:oai:doaj.org/article:6652703eb20c462ab163071acefa992d 2023-05-15T15:09:14+02:00 Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome Karol Stanski Isla H. Myers-Smith Christopher G. Lucas 2021-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2021.3110365 https://doaj.org/article/6652703eb20c462ab163071acefa992d EN eng IEEE https://ieeexplore.ieee.org/document/9531417/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3110365 https://doaj.org/article/6652703eb20c462ab163071acefa992d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 9287-9296 (2021) Object recognition remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2021 ftdoajarticles https://doi.org/10.1109/JSTARS.2021.3110365 2022-12-30T21:01:50Z 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 E. vaginatum 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: https://github.com/karoleks4/flower-detection .) Article in Journal/Newspaper Arctic Global warming Tundra Directory of Open Access Journals: DOAJ Articles Arctic IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 9287 9296 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Object recognition remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Object recognition remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 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 |
topic_facet |
Object recognition remote sensing Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
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 E. vaginatum 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: https://github.com/karoleks4/flower-detection .) |
format |
Article in Journal/Newspaper |
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 |
title |
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_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_sort |
flower detection using object analysis: new ways to quantify plant phenology in a warming tundra biome |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doi.org/10.1109/JSTARS.2021.3110365 https://doaj.org/article/6652703eb20c462ab163071acefa992d |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Global warming Tundra |
genre_facet |
Arctic Global warming Tundra |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 9287-9296 (2021) |
op_relation |
https://ieeexplore.ieee.org/document/9531417/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2021.3110365 https://doaj.org/article/6652703eb20c462ab163071acefa992d |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3110365 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
14 |
container_start_page |
9287 |
op_container_end_page |
9296 |
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1766340465169268736 |