scholarly journals Extracting Crown Morphology with a Low-Cost Mobile Lidar Scanning System in the Natural Environment

2021 ◽  
Vol 50 (11) ◽  
pp. 3193-3204
Author(s):  
Kai Wang ◽  
Jun Zhou ◽  
Wenhai Zhang ◽  
Baohua Zhang
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Scanning System ◽  
Morphological Parameters ◽  
Tight Coupling ◽  
Crown Height ◽  
Crown Diameter ◽  
Crown Volume

To meet the demand for intelligent measurements of canopy morphological parameters, a mobile LiDAR scanning system with LiDAR and IMU as the main sensors was constructed. The system uses a LiDAR-IMU tight coupling odometry method to reconstruct a point cloud map of the area surveyed. After using the RANSAC algorithm to remove the map ground, the European clustering algorithm is used for point cloud segmentation. Finally, morphological parameters of the canopy, such as crown height, crown diameter, and crown volume, are extracted using statistical and voxel methods. To verify the algorithm, a total of 43 trees in multiple plots of the campus were tested and compared. The algorithm defined in this study was evaluated with manual measurements as reference, and the morphological parameters of the canopy obtained using the LOAM and LeGO-LOAM algorithms as the basic framework were compared. Experiments show that this method can be used to easily obtain the crown height, crown diameter, and crown volume of the area; the correlation coefficients of these parameters were 0.91, 0.87, and 0.83, respectively. Compared with the LOAM and LeGO-LOAM methods, they were increased by 0.004, 0.12, and 0.13 and 0.07, 0.15, and 0.04, respectively. The test results for this new system are positive and meet the requirements of horticulture and orchard measurements, indicating that it will have significant value as an application.

scholarly journals Estimación de parámetros de estructura de nogales utilizando láser escáner terrestre

2017 ◽  
pp. 67 ◽  
Author(s):  
J. Estornell ◽  
A. Velázquez-Martí ◽  
A. Fernández-Sarría ◽  
I. López-Cortés ◽  
J. Martí-Gavilá ◽  
...  
Keyword(s):  
Regression Models ◽  
Point Cloud ◽  
Juglans Regia ◽  
Laser Scanner ◽  
Point Clouds ◽  
Linear Regression Models ◽  
Crown Height ◽  
Total Height ◽  
Crown Diameter ◽  
Crown Volume

<p><em>Juglans regia </em>L. (walnut) is a tree of significant economic importance, usually cultivated for its seed used in the food market, and for its wood used in the furniture industry. The aim of this work was to develop regression models to predict crown parameters for walnut trees using a terrestrial laser scanner. A set of 30 trees was selected and the total height, crown height and crown diameter were measured in the field. The trees were also measured by a laser scanner and algorithms were applied to compute the crown volume, crown diameter, total and crown height. Linear regression models were calculated to estimate walnut tree parameters from TLS data. Good results were obtained with values of R<sup>2</sup> between 0.90 and 0.98. In addition, to analyze whether coarser point cloud densities might affect the results, the point clouds for all trees were subsampled using different point densities: points every 0.005 m, 0.01 m, 0.05 m, 0.1 m, 0.25 m, 0.5 m, 1 m, and 2 m. New regression models were calculated to estimate field parameters. For total height and crown volume good estimations were obtained from TLS parameters derived for all subsampled point cloud (0.005 m – 2 m).</p>

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals AUTOMATED UAV LIDAR STRIP ALIGNMENT IN FORESTED AREAS USING DENSITY-BASED CANOPY CLUSTERING

2020 ◽  
Vol V-1-2020 ◽  
pp. 81-88
Author(s):  
R. Fekry ◽  
W. Yao ◽  
L. Cao
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Research Work ◽  
Similarity Score ◽  
Scanning System ◽  
Matching Problem ◽  
Individual Tree ◽  
Tree Crowns ◽  
Key Points ◽  
Forested Areas

Abstract. Recently, LiDAR point cloud data acquired by Unmanned Aerial Vehicles (UAVs) are used in many scientific disciplines and like the former photogrammetric techniques these data are usually collected in overlapping strips. Generation of comprehensive models of the scanned areas requires these strips to be aligned together which is a challenging process due to the multi sensor scanning system including the scanning sensor, the GNSS receiver and the IMU sensor. The main errors result from the inaccurate GNSS locations and flight path shifts as well as failure of the GNSS signals in complex urban or forest environments. For that reasons, the development of an automatic feature-dependant method in urban areas or individual tree-based in forest areas where there are no distinct features for strip adjustment in these environments become a must. This research work focuses on automated co-registration/alignment multiple point cloud strips of forested areas acquired from UAV LiDAR (or referred to as ULS) lack of artificial ground control. The main limitations of ULS data of forests are the relatively low sampling density of near ground areas and stem nullity due to the top-view scanning mode of ULS. To obviate this, this work explicates the tree crowns shape to identify the key points required for co-registration by applying a density based clustering algorithm (DBSCAN) to the tree crowns and models resulting clusters with Gaussian mixture models by learning the best parameters using maximum likelihood estimation to define the key points. A feature vector is assigned to each point by quantifying its angular and linear relationship with respect to the local system origin. Next, the similarity score matrix is computed by a fixed geometric relationship between the distance and angle similarity. Then, the maximum weight matching problem is solved for the similarity score to gain point-to-point correspondence. Finally, the optimal 2D rigid transformation parameters (one rotation and two translations without scale factor ) are obtained using permutations to try out for all possible paired combinations and count the number of inlier points satisfying a tolerance of planimetric deviation after alignment within a user defined threshold. The results of two test forest plots with different tree species and ULS point densities show a mean planimetric enhancement from 1.79 m to 0.22 ± 0.13 m for plot one and from 2.33 ± 0.53 m to 0.61 ± 0.21 m.

Surface interaction of sweet potato peels (Ipomoea batata) with Cd(II) and Pb(II) ions in aqueous medium

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Aqueous Medium ◽  
Sweet Potato ◽  
Adsorption Process ◽  
Low Cost ◽  
Correlation Coefficients ◽  
Order Kinetic ◽  
Thermodynamic Evaluation ◽  
Dissociative Mechanism ◽  
Pseudo Second Order ◽  
Potato Peels

The removal of Cd(II) and Pb(II) ions from aqueous medium was studied using potato peels biomass. The adsorption process was evaluated using Atomic Absorption Spectrophotometer (AAS). The Vibrational band of the potato peels was studied using Fourier Transform Infrared Spectroscopy (FTIR). The adsorption process was carried out with respect to concentration, time, pH, particle size and the thermodynamic evaluation of the process was carried at temperatures of 30, 40, 50 and 60(0C), respectively. The FTIR studies revealed that the potato peels was composed of –OH, -NH, –C=N, –C=C and –C-O-C functional groups. The optimum removal was obtained at pH 8 and contact time of 20 min. The adsorption process followed Freundlich adsorption and pseudo second-order kinetic models with correlation coefficients (R2) greater than 0.900. The equilibrium adsorption capacity showed that Pb(II) ion was more adsorbed on the surface of the potato peels biomass versus Cd (II) ion (200.91 mg/g &gt; 125.00 mg/g). The thermodynamic studies indicated endothermic, dissociative mechanism and spontaneous adsorption process. This study shows that sweet potato peels is useful as a low-cost adsorbent for the removal of Cd(II) and Pb(II) ions from aqueous medium.

scholarly journals Refining tree size and dose-response functions for control of invasive Pinus contorta

2021 ◽  
pp. 1-36
Author(s):  
Carol A. Rolando ◽  
Brian Richardson ◽  
Thomas S.H. Paul ◽  
Chanatda Somchit
Keyword(s):  
Dose Response ◽  
Pinus Contorta ◽  
Tree Size ◽  
Volume Index ◽  
Mortality Data ◽  
Field Calculation ◽  
Crown Area ◽  
Crown Diameter ◽  
Crown Volume ◽  
Canopy Area

Abstract Exotic conifers are rapidly spreading in many regions of New Zealand, as well as in many other countries, with detrimental impacts on both natural ecosystems and some productive sector environments. Herbicides, in particular the active ingredient (a.i.) triclopyr, are an important tool to manage invasive conifers, yet there is a paucity of information that quantifies the amount of herbicide required to kill trees of different sizes when applied as a basal bark treatment. Two sequential experiments were conducted to define the amount of triclopyr required to kill individual invasive Pinus contorta trees of different sizes when applied in a methylated seed oil to bark (either the whole stem or base of the tree) and to determine which tree size variates (height (HT), diameter at breast height (DBH), crown diameter (CD)), or derived attributes (crown area, crown volume index) best characterised this dose-response relationship. The outcomes of the dose-response research were compared to field operations where triclopyr was applied to the bark of trees from an aerial platform. Applying the herbicide to the whole stem, as opposed to the base of the tree only, significantly increased treatment efficacy. The tree size variates DBH, CD, crown area and crown volume index all provided good fits to the tree mortality data, with >91% prediction accuracy. Of these variates, crown diameter provided the most practical measure of tree size for ease of in-field calculation of dose by an operator. Herbicide rates used in field operations were 7 to 8 times higher than lethal doses calculated from experimental data. Our results highlight the potential for substantial reductions in herbicide rates for exotic conifer control, especially if dose-response data are combined with remotely sensed quantitative measurements of canopy area or volume using new precision technologies such as unmanned aerial vehicles.

scholarly journals Low-Cost Calibration of Matching Error between Lidar and Motor for a Rotating 2D Lidar

2021 ◽  
Vol 11 (3) ◽  
pp. 913
Author(s):  
Chang Yuan ◽  
Shusheng Bi ◽  
Jun Cheng ◽  
Dongsheng Yang ◽  
Wei Wang
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Shape Error ◽  
3D Point Cloud ◽  
Motor Shaft ◽  
Matching Error ◽  
Triangular Plate ◽  
Attitude Error ◽  
Shaft Angle ◽  
The Cost

For a rotating 2D lidar, the inaccurate matching between the 2D lidar and the motor is an important error resource of the 3D point cloud, where the error is shown both in shape and attitude. Existing methods need to measure the angle position of the motor shaft in real time to synchronize the 2D lidar data and the motor shaft angle. However, the sensor used for measurement is usually expensive, which can increase the cost. Therefore, we propose a low-cost method to calibrate the matching error between the 2D lidar and the motor, without using an angular sensor. First, the sequence between the motor and the 2D lidar is optimized to eliminate the shape error of the 3D point cloud. Next, we eliminate the attitude error with uncertainty of the 3D point cloud by installing a triangular plate on the prototype. Finally, the Levenberg–Marquardt method is used to calibrate the installation error of the triangular plate. Experiments verified that the accuracy of our method can meet the requirements of the 3D mapping of indoor autonomous mobile robots. While we use a 2D lidar Hokuyo UST-10LX with an accuracy of ±40 mm in our prototype, we can limit the mapping error within ±50 mm when the distance is no more than 2.2996 m for a 1 s scan (mode 1), and we can limit the mapping error within ±50 mm at the measuring range 10 m for a 16 s scan (mode 7). Our method can reduce the cost while the accuracy is ensured, which can make a rotating 2D lidar cheaper.

scholarly journals Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5697
Author(s):  
Chang Sun ◽  
Shihong Yue ◽  
Qi Li ◽  
Huaxiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Two Phase Flow ◽  
Low Cost ◽  
Fast Response ◽  
New Method ◽  
Phase Flow ◽  
Reference Distribution ◽  
Two Phase ◽  
Long Time ◽  
Solid Liquid

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.

scholarly journals Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 741 ◽  
Author(s):  
Haihui Yang ◽  
Xiaochan Wang ◽  
Guoxiang Sun
Keyword(s):  
Intelligent Control ◽  
Point Cloud ◽  
Three Dimensional ◽  
Fruit Tree ◽  
Tree Canopy ◽  
Morphological Parameters ◽  
Kinect Sensor ◽  
Self Calibration ◽  
Morphological Measurement ◽  
Tree Width

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

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