Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

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Kuzmin , A , Korhonen , L , Kivinen , S , Hurskainen , P , Korpelainen , P , Tanhuanpää , T , Maltamo , M , Vihervaara , P & Kumpula , T 2021 , ' Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests ' , Remote Sensing , vol. 13 , no. 9 , 1723 . https://doi.org/10.3390/rs13091723

Title: Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests
Author: Kuzmin, Anton; Korhonen, Lauri; Kivinen, Sonja; Hurskainen, Pekka; Korpelainen, Pasi; Tanhuanpää, Topi; Maltamo, Matti; Vihervaara, Petteri; Kumpula, Timo
Other contributor: University of Helsinki, Earth Change Observation Laboratory (ECHOLAB)
University of Helsinki, Department of Forest Sciences


Date: 2021-04-29
Language: eng
Number of pages: 18
Belongs to series: Remote Sensing
ISSN: 2072-4292
DOI: https://doi.org/10.3390/rs13091723
URI: http://hdl.handle.net/10138/329900
Abstract: 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.
Subject: 1171 Geosciences
tree species classification
European aspen
UAV
biodiversity
deciduous trees
machine learning
multispectral data
boreal forest
TREE SPECIES CLASSIFICATION
PHOTOGRAMMETRIC POINT CLOUDS
OLD-GROWTH
LIDAR DATA
ASSESSING BIODIVERSITY
INDIVIDUAL TREES
IMAGERY
VEGETATION
INVENTORY
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