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Article

CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations

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Laboratory for Climatology and Remote Sensing, Philipps-University of Marburg, Deutschhausstr. 12, 35032 Marburg, Germany
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Map-Site, Rossweg 15b, 35094 Lahntal/Gossfelden, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(23), 3892; https://doi.org/10.3390/rs12233892
Received: 26 October 2020 / Revised: 24 November 2020 / Accepted: 26 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Mapping Tree Species Diversity)
Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%. View Full-Text
Keywords: tree species classification; CNN; UAV; RGB tree species classification; CNN; UAV; RGB
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MDPI and ACS Style

Egli, S.; Höpke, M. CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sens. 2020, 12, 3892. https://doi.org/10.3390/rs12233892

AMA Style

Egli S, Höpke M. CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sensing. 2020; 12(23):3892. https://doi.org/10.3390/rs12233892

Chicago/Turabian Style

Egli, Sebastian, and Martin Höpke. 2020. "CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations" Remote Sensing 12, no. 23: 3892. https://doi.org/10.3390/rs12233892

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