Article
Environmental Sciences
Poching Teng, Yu Zhang, Takayoshi Yamane, Masayuki Kogoshi, Takeshi Yoshida, Tomohiko Ota, Junichi Nakagawa
Summary: During the winter pruning of deciduous fruit trees, the number and structure of pruning branches have a significant impact on the future growth and harvest volume of the fruit trees. This study utilized UAV-SfM and 3D lidar SLAM techniques to create 3D models of peach trees and proposed a method to distinguish branches using spatial point cloud density. The results showed that the 3D lidar SLAM technique had shorter modeling time and higher accuracy compared to UAV-SfM for winter pruning of peach trees. The method achieved a minimum RMSE of 3084 g with an R-2 value of 0.93 for the fresh weight of pruned branches.
Article
Environmental Sciences
Dominik Merkle, Alexander Reiterer
Summary: This study proposes a method for measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner and a tachymeter. The method is independent of time synchronization and works on sparse SLAM point clouds. The evaluation results show that the proposed method is efficient and MA-LIO performs superiorly compared to other SLAM algorithms.
Article
Environmental Sciences
Bin Wang, Jianyang Liu, Jianing Li, Mingze Li
Summary: Based on UAV LiDAR and hyperspectral data, this study designed different classification schemes to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of single tree species. The results showed that multisource remote sensing data had higher classification accuracy than single data source. Random forest and support vector machine classifiers had similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The addition of UAV LiDAR-extracted canopy morphological features slightly improved the classification accuracy of all three classifiers for tree species.
Article
Chemistry, Analytical
Ahmed Elamin, Nader Abdelaziz, Ahmed El-Rabbany
Summary: This research focuses on addressing the challenge of accurate pose estimation in UAV navigation by developing a multi-sensor integration system. The system includes a GNSS/IMU board, a LiDAR sensor, and a high-resolution camera. The results show significant improvements in performance compared to both complete GNSS outage and assistance from the GNSS PPP solution.
Article
Environmental Sciences
Jaroslav Kubista, Peter Surovy
Summary: A linear mixed-effects model was used to relate crown width to height, and random model parameters were estimated based on sample trees. The calibrated model can accurately detect individual tree tops in unmanned aerial laser scanning data of mixed species forest stands. Models calibrated with five or more samples achieved better results, but lower performance was observed in dense stands with trees that were between 5 and 10 m in height. This study concluded that locally calibrated models can serve as a universal starting point for selecting optimal window size in LMF procedures.
Article
Remote Sensing
Pengcheng Shi, Jiayuan Li, Yongjun Zhang
Summary: In this paper, a super-fast LiDAR global localization approach is proposed, which achieves state-of-the-art accuracy with superior efficiency. The method utilizes template descriptors to capture structural environments and approximates the vehicle's position via map candidate points. An offline map database is created to simulate vehicle orientations evenly. A loss function is designed to improve localization accuracy. The proposed method is extensively evaluated in public KITTI outdoor sequences and self-collected indoor datasets, showing significantly faster speed and high success rates.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Environmental Sciences
Kuo Liao, Yunhe Li, Bingzhang Zou, Dengqiu Li, Dengsheng Lu
Summary: This study compared the accuracy of tree height measurements using different methods and the influence of allometric models on tree volume estimation accuracy. The results showed significant impacts of different measurement methods on tree volume calculations, and incorporating UAV Lidar data with DBH field measurements can effectively improve tree volume estimation accuracy.
Article
Environmental Sciences
Ha Sier, Qingqing Li, Xianjia Yu, Jorge Pena Queralta, Zhuo Zou, Tomi Westerlund
Summary: This study benchmarks the current state-of-the-art LiDAR SLAM algorithms using a multi-modal LiDAR sensor setup. The proposed method combines multi-mode multi-LiDAR SLAM-assisted and ICP-based sensor fusion to generate ground truth maps. The results show significant differences in performance for different types of sensors and algorithms, and additional datasets with diverse environments are provided for further analysis.
Article
Computer Science, Information Systems
Michal Mihalik, Branislav Malobicky, Peter Peniak, Peter Vestenicky
Summary: In this article, a new approach to address the issue of active SLAM is proposed. The already functional SLAM algorithm was used and modified for the specific case. Matlab was the main software tool used, and all proposed methods were experimentally verified on a mobile robotic system. LiDAR was used as the primary sensor. After mapping the environment, a grid map was created, enabling autonomous mapping of the environment.
Article
Environmental Sciences
Ebadat Ghanbari Parmehr, Marco Amati
Summary: The study compares point clouds produced by UAV-photogrammetry and -LiDAR in an urban park, along with estimated tree canopy parameters. Results show a high correlation between UAV-photogrammetry and -LiDAR point clouds, with R-2 values exceeding 99.54%, and the estimated tree canopy parameters showing correlations above 95%.
Article
Forestry
Martin Slavik, Karel Kuzelka, Roman Modlinger, Peter Surovy
Summary: This study proposes a method of tree species classification using individual tree metrics derived from three-dimensional point cloud data obtained by unmanned aerial vehicle laser scanning. The metrics of 1045 trees were evaluated using a generalized linear model and random forest techniques, leading to automated assignment of individual trees into either coniferous or broadleaf groups. The inclusion of a spatial aggregation index called the Clark-Evans index significantly improved classification accuracy, with overall accuracies of 94.8% and 95.1% achieved using the generalized linear model and random forest approaches, respectively.
Article
Environmental Sciences
Yuyang Xie, Tao Yang, Xiaofeng Wang, Xi Chen, Shuxin Pang, Juan Hu, Anxian Wang, Ling Chen, Zehao Shen
Summary: Accurate tree positioning and measurement of structural parameters are crucial for forest inventory and mapping. This study utilized a backpack lidar system to measure a subtropical forest, and found that lidar had a certain error in diameter at breast height (DBH) measurements and tree positioning, mainly caused by the incompleteness of tree stem point clouds and the field measurements and point cloud density.
Review
Environmental Sciences
Weifeng Chen, Chengjun Zhou, Guangtao Shang, Xiyang Wang, Zhenxiong Li, Chonghui Xu, Kai Hu
Summary: This paper reviews the development and application of LIDAR and visual SLAM technology in the field of mobile robotics, highlights the limitations of single sensor-based SLAM technology, and predicts that the fusion of multiple sensors will be the mainstream direction in the future.
Article
Agriculture, Multidisciplinary
Qianwei Liu, Jinliang Wang, Weifeng Ma, Jianpeng Zhang, Yuncheng Deng, Dajiang Shao, Dongfan Xu, Yicheng Liu
Summary: The proposed method utilizes tree height registration to achieve accurate registration of ULS and TLS data, showing good applicability in forest lands with varying slopes. Rotation and translation matrices are calculated through SVD, enabling rough registration and fine registration to be achieved effectively.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Environmental Sciences
Michal Polak, Jakub Mirijovsky, Alba E. Hernandiz, Zdenek Spisek, Radoslav Koprna, Jan F. Humplik
Summary: The tool developed allows for precise and high-throughput analysis of plant growth by automatically extracting individual field plots and measuring their growth characteristics. The algorithm, designed in Python 3, showed promising results in agricultural research and is open-source for public use.
Article
Forestry
Stefano Puliti, Jonathan P. Dash, Michael S. Watt, Johannes Breidenbach, Grant D. Pearse
Article
Plant Sciences
Maxime Bombrun, Jonathan P. Dash, David Pont, Michael S. Watt, Grant D. Pearse, Heidi S. Dungey
FRONTIERS IN PLANT SCIENCE
(2020)
Article
Geography, Physical
Grant D. Pearse, Alan Y. S. Tan, Michael S. Watt, Matthias O. Franz, Jonathan P. Dash
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2020)
Article
Environmental Sciences
Michael S. Watt, Henning Buddenbaum, Ellen Mae C. Leonardo, Honey Jane Estarija, Horacio E. Bown, Mireia Gomez-Gallego, Robin J. L. Hartley, Grant D. Pearse, Peter Massam, Liam Wright, Pablo J. Zarco-Tejada
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Environmental Sciences
Ellen Mae C. Leonardo, Michael S. Watt, Grant D. Pearse, Jonathan P. Dash, Henrik J. Persson
REMOTE SENSING OF ENVIRONMENT
(2020)
Article
Environmental Sciences
Robin J. L. Hartley, Ellen Mae Leonardo, Peter Massam, Michael S. Watt, Honey Jane Estarija, Liam Wright, Nathanael Melia, Grant D. Pearse
Article
Environmental Sciences
Grant D. Pearse, Michael S. Watt, Julia Soewarto, Alan Y. S. Tan
Summary: This study compared the accuracy of deep learning and XGBoost models for classifying New Zealand pohutukawa trees, finding that the deep learning model achieved higher accuracy when leveraging phenology data, even with substantial variation in flowering intensity.
Article
Environmental Sciences
Robin J. L. Hartley, Sam J. Davidson, Michael S. Watt, Peter D. Massam, Samuel Aguilar-Arguello, Katharine O. Melnik, H. Grant Pearce, Veronica R. Clifford
Summary: This study evaluated the use of unmanned aerial vehicle (UAV) technologies for fuel characterisation. UAV laser scanning (ULS) point clouds were used to predict field measurements of total above ground biomass (TAGB) and above ground available fuel (AGAF). The study found that ULS-derived structural metrics offered higher levels of precision compared to non-destructive field measurements. Additionally, UAV photogrammetric data and deep learning techniques were used to classify vegetation into different fuel categories with high levels of precision. The findings suggest that UAV technologies have important applications in research, wildfire risk assessment, and fuel management.
Article
Agronomy
Michael S. Watt, Tomas Poblete, Dilshan de Silva, Honey Jane C. Estarija, Robin J. L. Hartley, Ellen Mae C. Leonardo, Peter Massam, Henning Buddenbaum, Pablo J. Zarco-Tejada
Summary: Dothistroma needle blight is a widespread and damaging disease of pine trees caused by fungi, resulting in chlorosis, necrosis, and premature needle loss. This study used hyperspectral data collected from a UAV to improve predictions of disease severity using plant functional traits determined from a 3D radiative transfer model. The final model accurately predicted disease severity with an R2 of 0.85.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Genetics & Heredity
Sarah Addison, Charlotte Armstrong, Kathryn Wigley, Robin Hartley, Steven Wakelin
Summary: The assembly and function of the phyllosphere microbiome on plant leaves are crucial for plant fitness and ecosystem health. This study expands the development of model microbiome systems for tree species, particularly coniferous gymnosperms, by exploring the phyllosphere microbiome of Pinus radiata. Canopy sampling height is the most important factor influencing the diversity of bacterial and fungal communities on the leaves. Bacterial communities are dominated by Alpha-proteobacteria and Acidobacteria Gp1, while fungal communities are mainly represented by Arthoniomycetes, Dothideomycetes, and Phaeococcomyces.
ENVIRONMENTAL MICROBIOME
(2023)
Review
Remote Sensing
Robin John ap Lewis Hartley, Isaac Levi Henderson, Chris Lewis Jackson
Summary: This article presents a review of BVLOS operations using unmanned aircraft in forest environments, discussing the unique challenges, international regulatory environment, and technological, operational, and other considerations. It also provides gaps for future research and a basis for further exploration.