Evaluation of a Low-Cost Photogrammetric System for the Retrieval of 3D Tree Architecture

\ua9 Author(s) 2023.Reconstruction of major branches of a tree is an important first step for the monitoring of tree sway and assessment of structural stability. Photogrammetric systems can provide a low-cost alternative for the acquisition of three-dimensional data, while also enabling long-term monitoring of a tree of interest. This study introduces a low-cost photogrammetric system based on two Raspberry Pi cameras, which is used to reconstruct the tree architecture for the purpose of stability monitoring. Images of five trees are taken at a range of distances and the resulting point clouds are evaluated in terms of point density and distribution with the reference to TLS. While the photogrammetric point clouds are sparse, it was found that they are capable of reconstructing the tree trunk and lower-order branches, which are most relevant for sway monitoring and tree stability assessment. The most optimal distance for the reconstruction of the relevant branches was found to be 9-10 m, with a baseline of 120 cm

Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering

Accurate Individual Tree Segmentation (ITS) is fundamental to fine-scale forest structure and management studies. Light detection and ranging(Lidar) from Unoccupied Aerial Systems (UAS) has shown strengths in ITS and tree parameter estimation at stand and landscape scales. However, dense woodlands with tightly interspersed canopies and highly diverse tree species challenge the performance of ITS, and current research has not delved into the impact of mixed tree species and different leaf conditions on algorithm accuracy. Therefore, this study firstly evaluates the performance of open-source ITS methods, including both deep learning and non-deep learning algorithms, on data with mixed tree species and different leaf conditions, then proposes a hierarchical filtering and clustering (HFC) algorithm to mitigate the influence and improve the robustness. Hierarchical filtering consists of intensity filtering, Singular Value Decomposition (SVD) filtering, and Statistical Outlier Removal (SOR). Hierarchical clustering involves point-based clustering, cluster merging, and filtered point assignment. Through experiments on three distinct UAS Lidar datasets of forests with mixed tree species and different leaf conditions, HFC achieved the optimal segmentation results while maintaining high robustness. The variations of F1-score are 1–3 percentage points for mixed tree species and 1–2 percentage points for different leaf conditions across different datasets

The Bristol CMIP6 Data Hackathon

This is the final version. Available on open access from Wiley via the DOI in this recordThe Bristol CMIP6 Data Hackathon formed part of the Met Office Climate Data Challenge Hackathon series during 2021, bringing together around 100 UK early career researchers from a wide range of environmental disciplines. The purpose was to interrogate the under-utilised but currently most advanced climate model inter-comparison project datasets to develop new research ideas, create new networks and outreach opportunities in the lead up to COP26. Experts in different science fields, supported by a core team of scientists and data specialists at Bristol, had the unique opportunity to explore together interdisciplinary environmental topics summarised in this article

The Bristol CMIP6 Data Hackathon

The Bristol CMIP6 Data Hackathon formed part of the Met Office Climate Data Challenge Hackathon series during 2021, bringing together around 100 UK early career researchers from a wide range of environmental disciplines. The purpose was to interrogate the under-utilised but currently most advanced climate model inter-comparison project datasets to develop new research ideas, create new networks and outreach opportunities in the lead up to COP26. Experts in different science fields, supported by a core team of scientists and data specialists at Bristol, had the unique opportunity to explore together interdisciplinary environmental topics summarised in this article