A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

Yang, Jie; Wen, Xiaorong; Wang, Qiulai; Ye, Jinsheng; Sun, Yuan

doi:10.3390/f13101534

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…These datasets which have been previously collected and validated serve as a benchmark, which enables model training without manually producing tagged data where appropriate as well as comparison of results. Existing tagged tree-level RGB photo [3,10] and point cloud [33,35] data will be used as references for methodology, training, and evaluation where possible. By comparing the outcomes derived from the proposed approach with these established datasets, discrepancies can be identified and addressed.…”

Section: Methodsmentioning

confidence: 99%

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Young

2023

Preprint

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Accurate forest inventory data are essential for tracking carbon sequestration, estimating carbon emissions from deforestation, assessing plant and animal habitats for biodiversity, and predicting environmental risks such as wildfires. Traditional methods of data collection have faced challenges in either scale or precision. The advent of terrestrial laser scanners addressed some of these issues but faced limitations in cost and mobility. This paper proposes a new approach using mobile LIDAR for forest inventory data collection. By integrating advancements in computer vision, the methodology aims to provide comprehensive individual tree data, including parameters like diameter at breast height, volume estimations, species identification, and temporal tracking of individual trees. This proposed research direction addresses current gaps in the use of LIDAR and camera inference for forestry data where existing work does not generate domain context-aware data by narrowly focusing collection on isolated tree attributes.

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Section: Methodsmentioning

confidence: 99%

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Young

2023

Preprint

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“…Currently, the acquisition of NDVI data is mainly based on satellite data. If it is possible to use LIDAR data, more detailed data can be obtained, which may lead to better results (Yang and Wen et al, 2022a;Yang and Wen et al, 2022b). It was noted that although the Siberian crane is a migratory bird that chooses to winter in the MLYRB, its distribution is not limited to the wetlands around Poyang and Dongting Lakes, but also extends to surrounding farmland and coastal areas.…”

Section: Potential Reasons For Changes In the Habitat Quality Of Wint...mentioning

confidence: 99%

Projection of the potential distribution of suitable habitats for Siberian crane (Grus leucogeranus) in the middle and lower reaches of the Yangtze River basin

Liu

Zhang

et al. 2023

Front. Earth Sci.

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Introduction: The middle and lower reaches of the Yangtze River basin (hereafter MLYRB) are one of the most important wintering grounds for waterbirds along the East Asian-Australasian Flyway (EAAF). Climate change and human activities have greatly altered the habitats of waterbirds in this region.Methods: The potential distribution of suitable habitats for the Siberian crane (Grus leucogeranus) in the MLYRB was projected using Maximum Entropy Models (MaxEnt) and three Global Climate Models (GCMs). Additionally, estimations for temperature and precipitation before 2060 were made using CMIP6 multi-model and multi-scenario data.Results: 1) the MaxEnt model was highly applicable (AUC = 0.939) for analyzing the suitable habitat distribution and climatic suitability of Siberian cranes in the study area; 2) Precipitation of the driest quarter and altitude were the main factors affecting the potential suitable habitat distribution of Siberian cranes, accounting for 40% of the total contribution rate each; 3) The climatically suitable areas for the distribution of suitable habitats of Siberian cranes in the study area was mainly concentrated in parts of Poyang Lake, Dongting Lake, Taihu Lake, and the mainstream of the Yangtze River; 4) In the BCC-CSM2-MR, CanESM5, and CNRM-CM6-1 models, the suitable habitat area for Siberian cranes is expected to decrease under different SSP scenarios from 2021 to 2060 compared to 1970–2000.Discussion: The BCC-CSM2-MR model’s SSP2-4.5 scenario shows the most significant decline in the suitable habitat area for Siberian cranes, with a maximum decrease of approximately 35.7%, followed by a maximum decrease of about 26.2% and 16.4% under the CNRM-CM6-1 and CanESM5 models’ SSP2-4.5 scenario, respectively. In general, the suitable habitat areas for Siberian cranes are projected to decline, indicating the need for comprehensive evaluation and uncertainty research using more models.

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“…First of all, terrestrial LiDAR scanners can capture detailed geometry of objects with a precision of up to 3 mm from multiple standing positions ( RIEGL, 2023 ). This method proves capable of capturing a tree’s trunk and branches with more than 10 mm diameter ( Gobeawan et al., 2018 ; Yang et al., 2022 ) during its leaf-off state ( Kükenbrink et al., 2022 ). Raw data is stored in the form of a discrete point cloud.…”

Section: Introductionmentioning

confidence: 99%

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

Shu,

Yazdi,

Rötzer

et al. 2024

Front. Plant Sci.

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IntroductionResprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree’s resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning.MethodsTable-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts.ResultsThe accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%.DiscussionThe results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.

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A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

Cited by 7 publications

References 35 publications

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Projection of the potential distribution of suitable habitats for Siberian crane (Grus leucogeranus) in the middle and lower reaches of the Yangtze River basin

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning

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