The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees.

Research on the aerodynamic characteristics of leaves is part of the study of wind-induced tree disasters and has relevance to plant biological processes. The frontal area, which varies with the structure of leaves, is an important physical parameter in studying the aerodynamic characteristics of leaves. In order to measure the frontal area of a leaf in a wind tunnel, a method based on improved U-Net is proposed. First, a high-speed camera was used to collect leaf images in a wind tunnel; secondly, the collected images were corrected, cut and labeled, and then the dataset was expanded by scaling transformation; thirdly, by reducing the depth of each layer of the encoder and decoder of U-Net and adding a batch normalization (BN) layer and dropout layer, the model parameters were reduced and the convergence speed was accelerated; finally, the images were segmented based on the improved U-Net to measure the frontal area of the leaf. The training set was divided into three groups in the experiment. The experimental results show that the MIoUs were 97.67%, 97.78% and 97.88% based on the improved U-Net training on the three datasets, respectively. The improved U-Net model improved the measurement accuracy significantly when the dataset was small. Compared with the manually labeled image data, the RMSEs of the frontal areas measured by the models based on the improved U-Net were 1.56%, 1.63% and 1.60%, respectively. The R² values of the three measurements were 0.9993. The frontal area of a leaf can be accurately measured based on the proposed method.

The sway frequency is an important component of the dynamic characteristics of trees. Video-based methods can be used to measure the sway frequencies of trees. The key to successfully measuring tree sway frequency using video methods lies in whether the tracking method employed is appropriate. Based on six algorithms, i.e., Boosting, TLD, MIL, KCF, MOSSE and CSR-DCF, the tracking performance and accuracy of tree sway frequency measurements were investigated under two conditions: artificial excitation and environmental excitation. The results show that the following: (1) In terms of the tracking speed of tree sway, MOSSE > KCF > CSR-DCF > Boosting > MIL > TLD. (2) The TLD algorithm is not suitable for tree sway tracking. Boosting, MIL, MOSSE, KCF and CSR-DCF can be used for tree sway tracking. (3) Boosting, MIL and MOSSE are suitable for measuring the sway frequency of artificially excited branches and environmentally excited trees. (4) KCF and CSR-DCF algorithms are not suitable for the measurement of branch sway frequency under artificial excitation conditions but can be used for the measurement of tree sway frequency under environmental excitation conditions. However, it should be noted that this experiment only takes a Betula platyphylla Sukaczev tree and a Betula platyphylla Sukaczev branch as the research object to verify the effectiveness and feasibility of each tracking method, and does not verify the generalization ability of the above methods (on multiple tree species and multiple trees).