Understanding the dynamic properties of trees using the motions constructed from multi-beam flash light detection and ranging measurements.

Measuring the three-dimensional motion of trees at every position remains challenging as it requires dynamic measurement technology with sufficient spatial and temporal resolution. Consequently, this study explores the use of a novel multi-beam flash light detection and ranging (LiDAR) sensor to tackle such a sensing barrier. A framework is proposed to record tree vibrations, to construct the motions of tree skeletons from the point-cloud frames recorded by the LiDAR sensor and to derive the dynamic properties of trees. The feasibility of the framework is justified through measurement on a Ficus microcarpa under pull-and-release tests. The relative differences for the first two modal frequencies between the LiDAR and linear variable differential transformer measurements in the displacement Fourier spectra are 0.1% and 2.5%, respectively. The framework is further adopted to study the dynamic response of different trees subjected to typhoons, including a Liquidambar formosana, three Araucaria heterophylla trees, a Sterculia lanceolata, a Celtis sinensis, a Tabebuia chrysantha and a Cinnamomum camphora. Results suggest that broadleaved trees might exhibit vibration in a wide frequency band, whereas the coniferous trees could follow a distinct dominant frequency.

Domain randomization-enhanced deep learning models for bird detection.

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.