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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ftunivhelsihelda:oai:helda.helsinki.fi:10138/573796 2024-04-28T08:18:37+00:00 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 2024-03-27T13:17:46Z application/pdf http://hdl.handle.net/10138/573796 unknown https://doi.org/10.3390/rs13091723 Kuzmin, A.; Korhonen, L.; Kivinen, S.; Hurskainen, P.; Korpelainen, P.; Tanhuanpää, T.; Maltamo, M.; Vihervaara, P.; Kumpula, T. Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests. Remote Sens. 2021, 13, 1723. http://hdl.handle.net/10138/573796 2024 ftunivhelsihelda https://doi.org/10.3390/rs13091723 2024-04-03T15:21:21Z 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 ... Other/Unknown Material Fennoscandia HELDA – University of Helsinki Open Repository Remote Sensing 13 9 1723 |
institution |
Open Polar |
collection |
HELDA – University of Helsinki Open Repository |
op_collection_id |
ftunivhelsihelda |
language |
unknown |
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 ... |
author |
Kuzmin, Anton Korhonen, Lauri Kivinen, Sonja Hurskainen, Pekka Korpelainen, Pasi Tanhuanpää, Topi Maltamo, Matti Vihervaara, Petteri Kumpula, Timo |
spellingShingle |
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 |
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 |
publishDate |
2024 |
url |
http://hdl.handle.net/10138/573796 |
genre |
Fennoscandia |
genre_facet |
Fennoscandia |
op_relation |
https://doi.org/10.3390/rs13091723 Kuzmin, A.; Korhonen, L.; Kivinen, S.; Hurskainen, P.; Korpelainen, P.; Tanhuanpää, T.; Maltamo, M.; Vihervaara, P.; Kumpula, T. Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests. Remote Sens. 2021, 13, 1723. http://hdl.handle.net/10138/573796 |
op_doi |
https://doi.org/10.3390/rs13091723 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
9 |
container_start_page |
1723 |
_version_ |
1797582520152227840 |