Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests
European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in bore...
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ftutahsudc:oai:digitalcommons.usu.edu:aspen_bib-8927 2023-05-15T16:12:18+02:00 Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo MDPI AG 2021-04-29T07:00:00Z application/pdf https://digitalcommons.usu.edu/aspen_bib/7926 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8927&context=aspen_bib unknown Hosted by Utah State University Libraries https://digitalcommons.usu.edu/aspen_bib/7926 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8927&context=aspen_bib Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu. http://creativecommons.org/licenses/by/4.0/ PDM CC-BY Aspen Bibliography tree species classification European aspen UAV biodiversity deciduous trees machine learning multispectral data boreal forest Agriculture Ecology and Evolutionary Biology Forest Sciences Genetics and Genomics Plant Sciences text 2021 ftutahsudc 2022-03-07T22:04:57Z European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests. Text Fennoscandia Utah State University: DigitalCommons@USU |
institution |
Open Polar |
collection |
Utah State University: DigitalCommons@USU |
op_collection_id |
ftutahsudc |
language |
unknown |
topic |
tree species classification European aspen UAV biodiversity deciduous trees machine learning multispectral data boreal forest Agriculture Ecology and Evolutionary Biology Forest Sciences Genetics and Genomics Plant Sciences |
spellingShingle |
tree species classification European aspen UAV biodiversity deciduous trees machine learning multispectral data boreal forest Agriculture Ecology and Evolutionary Biology Forest Sciences Genetics and Genomics Plant Sciences Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
topic_facet |
tree species classification European aspen UAV biodiversity deciduous trees machine learning multispectral data boreal forest Agriculture Ecology and Evolutionary Biology Forest Sciences Genetics and Genomics Plant Sciences |
description |
European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests. |
author2 |
MDPI AG |
format |
Text |
author |
Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo |
author_facet |
Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo |
author_sort |
Kuzmin, Anton |
title |
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
title_short |
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
title_full |
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
title_fullStr |
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
title_full_unstemmed |
Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests |
title_sort |
detection of european aspen (populus tremula l.) based on an unmanned aerial vehicle approach in boreal forests |
publisher |
Hosted by Utah State University Libraries |
publishDate |
2021 |
url |
https://digitalcommons.usu.edu/aspen_bib/7926 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8927&context=aspen_bib |
genre |
Fennoscandia |
genre_facet |
Fennoscandia |
op_source |
Aspen Bibliography |
op_relation |
https://digitalcommons.usu.edu/aspen_bib/7926 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8927&context=aspen_bib |
op_rights |
Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu. http://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
PDM CC-BY |
_version_ |
1765997583933636608 |