Shangshu Cai

Shangshu Cai
Beijing Normal University | bnu · School of Geography

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10
Publications
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173
Citations
Citations since 2016
10 Research Items
173 Citations
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060

Publications

Publications (10)
Article
Full-text available
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, including tree height, diameter at breast height, and crown diameter, obtained via forest in situ measurements, which are labor intensive and time consuming. Some new technologies meas...
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
Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR...
Article
Full-text available
Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The propo...
Article
Full-text available
The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally require...
Article
Full-text available
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
Filtering is a key step for most airborne lidar post-applications in urban areas. To solve the problems of complex parameter settings and low filtering accuracy in complicated urban environments, an automatic morphological filter was proposed. In this paper, the optimal maximum filtering window can be determined automatically by applying a series o...
Article
Full-text available
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
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
Full-text available
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...

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Projects (2)
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.