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

Authors

Anton Kuzmin, University of Eastern Finland
Lauri Korhonen, University of Eastern Finland
Sonja Kivinen, University of Eastern Finland
Pekka Hurskainen, Finnish Environment Institute
Pasi Korpelainen, University of Eastern Finland
Topi Tanhuanpää, University of Eastern Finland
Matti Maltamo, University of Eastern Finland
Petteri Vihervaara, Finnish Environment Institute
Timo Kumpula, University of Eastern Finland

Document Type

Article

Journal/Book Title/Conference

Remote Sensing

Volume

13

Issue

9

Publisher

MDPI AG

First Page

1

Last Page

18

Publication Date

4-29-2021

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

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.

Recommended Citation

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. https://doi.org/10.3390/rs13091723