Wan Peng

Wan Peng
Beijing Normal University | bnu · School of Geography

PhD

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12
Publications
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Publications

Publications (12)
Preprint
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Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate inversion of forest parameters. However, forest occlusion poses challenges for marker-based registration methods, and some marker-fr...
Preprint
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Background Tree growth is an important indicator of forest health and can reflect changes in forest structure. Traditional tree growth estimates use easy-to-measure parameters (e.g., tree height, diameter at breast height (DBH), and crown diameter) obtained via forest in situ measurements, which are labor-intensive and time-consuming to perform and...
Article
With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood‐leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing larg...
Preprint
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Background: Detailed and accurate information about tree crown structures is crucial for researching tree physiology. Terrestrial laser scanning (TLS) is a promising technique for retrieving crown structure parameters but still faces bottlenecks in terms of data processing such as wood-leaf separation. Currently, most wood-leaf separation methods u...
Article
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Background The stem curve of standing trees is an essential parameter for accurate estimation of stem volume. This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning (TLS) data, evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-sc...
Article
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Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (...
Article
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Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS...
Article
Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping...
Article
Three-dimensional (3D) model is a major form of cultural heritage documentation. In most cases, the properties of digital artefacts (e.g. readability, coverage) are affected by the acquisition procedure (e.g. device, workflow, conditions) and the characteristics of the physical artefact (e.g. shape, size and materials). In this paper, we study how...
Article
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Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique...
Article
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Ground filtering is an essential procedure in almost all LiDAR applications. However, most existing ground filtering algorithms require different amounts of user input to manually set up initial parameters, such as terrain relief amplitude and average slope, which is subjective, time consuming, and prone to errors. Here, we propose a simple terrain...
Article
Full-text available
Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering meth...

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Project
One of our research field is to retrieve structural parameters of trees in forest plot, especially using LiDAR point cloud. To achieve this goal efficiently and accurately, we try to develop novel algorithms, including point cloud registration, ground filtering, separation of stem, branch and leaves, reconstruction of tree model, inversion of LAI (leaf area index) and LAD (leaf angle distribution).
Project
Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from LiDAR (light detection and ranging) data. Many filtering algorithms have been developed. However, even state-of-the-art filtering algorithms need to set up a number of complicated parameters carefully to achieve high accuracy. For the purpose of reducing the parameters users to set, and promoting the filtering algorithms, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. This method is based on cloth simulation which is a 3D computer graphics algorithm and is used for simulating cloth within a computer program. So our filtering algorithm is called cloth simulation filtering, CSF. More information of CSF and its parameters can be found at http://www.cloudcompare.org/doc/wiki/index.php?title=CSF_(plugin). CSF implemented the algorithm proposed by the paper "Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X, Yan G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing. 2016; 8(6):501.",which can be downloaded from https://www.researchgate.net/profile/Wuming_Zhang2. Please cite this paper, if you use this software in your work.