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

Abstract

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%.

Precision restoration: a necessary approach to foster forest recovery in the 21st century

“…Vila‐Viçosa et al 2020); vegetation structure (Betbeder et al 2017); or soil moisture, texture, and salinity (Kim et al 2020). Finally, artificial intelligence, in particular deep learning, can boost these tasks through the exploitation of petabytes of remote sensing data, such as in the detection and counting of seedlings or adult crowns (Buters et al 2019; Albuquerque et al 2020), the identification of tree species (Egli & Höpke 2020), and the detection of scattered trees (Brandt et al 2020; Guirado et al 2021) that could serve for postdisturbance regeneration. In summary, the fusion of remote sensing, ecological niche modeling, and artificial intelligence may help to identify the appropriate locations (e.g.…”

Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

“…As UAVs can be flexibly deployed and freely move in 3D space, the air-to-ground wireless channel (higher probability with LOS links) is usually better than the ground-to-ground counterpart [6]. Therefore, the UAV-assisted VANETs have great potential in improving the quality-of-service (QoS) vehicle-related applications [7].…”

“…Based on f k−1 and λ k−1 , we can solve the optimization problem P3 by the constraint relaxation scheme and the relax-and-rounding method. 7: until Obtain the feasible solution x * i,j . 8: repeat 9: According to the Lagrangian dual decomposition, we can solve the optimization problem P7 and P8.…”

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

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