scholarly journals Spatial pattern analysis of forest trees based on the vectorial mark

2021 ◽  
Author(s):  
Honglu Xin ◽  
Toby Jackson ◽  
Yujie Cao ◽  
Huanyuan Zhang ◽  
Yi Lin ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Spatial Correlation ◽  
Spatial Patterns ◽  
Point Cloud ◽  
Laser Scanning ◽  
Pattern Analysis ◽  
Tree Height ◽  
Tree Size ◽  
Projection Area ◽  
Spatial Pattern Analysis

AbstractAnalysis of spatial patterns to describe the spatial correlation between a tree location and marks (i.e., structural variables), can reveal stand history, population dynamics, competition and symbiosis. However, most studies of spatial patterns have concentrated on tree location and tree sizes rather than on crown asymmetry especially with direct analysis among marks characterizing facilitation and competition among of trees, and thus cannot reveal the cause of the distributions of tree locations and quantitative marks. To explore the spatial correlation among quantitative and vectorial marks and their implication on population dynamics, we extracted vertical and horizontal marks (tree height and crown projection area) characterizing tree size, and a vectorial mark (crown displacement vector characterizing the crown asymmetry) using an airborne laser scanning point cloud obtained from two forest stands in Oxfordshire, UK. Quantitatively and vectorially marked spatial patterns were developed, with corresponding null models established for a significance test. We analyzed eight types of univariate and bivariate spatial patterns, after first proposing four types. The accuracy of the pattern analysis based on an algorithm-segmented point cloud was compared with that of a truly segmented point cloud. The algorithm-segmented point cloud managed to detect 70–86% of patterns correctly. The eight types of spatial patterns analyzed the spatial distribution of trees, the spatial correlation between tree size and facilitated or competitive interactions of sycamore and other species. These four types of univariate patterns jointly showed that, at smaller scales, the trees tend to be clustered, and taller, with larger crowns due to the detected facilitations among trees in the study area. The four types of bivariate patterns found that at smaller scales there are taller trees and more facilitation among sycamore and other species, while crown size is mostly homogeneous across scales. These results indicate that interspecific facilitation and competition mainly affect tree height in the study area. This work further confirms the connection of tree size with individual facilitation and competition, revealing the potential spatial structure that previously was hard to detect.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 936 ◽  
Author(s):  
Chen ◽  
Feng ◽  
Chen ◽  
Khan ◽  
Lian
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Point Cloud Data ◽  
Above Ground Biomass ◽  
Cloud Data ◽  
Non Destructive ◽  
Multiple Tree

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

scholarly journals Analysis of Dynamic Forest Structures Based on Hierarchical Features Extracted from Multi-Station LiDAR Scanning

2020 ◽  
Vol 3 (1) ◽  
pp. 21
Author(s):  
Xiuyun Lin ◽  
Yulin Gong ◽  
Yuan Sun ◽  
Jiawen Jiang ◽  
Yanli Zhang ◽  
...  
Keyword(s):  
Volume Change ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Point Clouds ◽  
Dynamic Monitoring ◽  
Scanning Method ◽  
Volume Model ◽  
Liriodendron Chinense ◽  
Hierarchical Features

This study aims at searching for characteristic parameters of tree trunks to establish a volume model and dynamic analysis of volume based on terrestrial laser scanning (TLS). We collected three phases of data over 5 years from an artificial Liriodendron chinense forest. The upper diameters of the tree stump and tree height data were obtained by using the multi-station scanning method. A novel hierarchical TLS point cloud feature named the height cumulative percentage (Hz%) was designed. The shape of the upper tree trunk extracted by the point cloud was equivalent to that of the analytical tree with inflection points at 25% and 50% of the height, and the dynamic volume change of the model, which was established by hierarchical features, was highly related to the volume change of the actual point cloud extraction. The obtained results reflected the fact that the Hz% value provided by multi-station scanning was closely related to the characteristic stumpage parameters and could be used to invert the dynamic forest structure. The volume model established based on point cloud hierarchical parameters in this study could be used to monitor the dynamic changes of forest volume and to provide a new reference for applying TLS point clouds for the dynamic monitoring of forest resources.

Towards the automatic 3D characterization of forest plots

2020 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordoñez ◽  
Covadonga Prendes ◽  
Stefan Doerr ◽  
Jose V. Roces-Diaz ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Structure ◽  
Tree Height ◽  
Point Clouds ◽  
Forest Plot ◽  
Multiscale Approach ◽  
Plot Structure

<p>Ground-based point clouds (from laser scanning or photogrammetry, and from static or mobile devices) give very detailed 3D information of forest plots. Also, if this information is complemented with data gathered from aerial vehicles, some parts of the forest structure that are not visible from the terrain can be represented (e.g. treetops). However, the heterogeneity of the point clouds, the complexity of some forest plots and the limitations of some data gathering/processing techniques lead to some occlusions and misrepresentations of the features in the plot. Therefore, complete automation of very detailed characterizations of all the items/features/structures in a forest plot is, most of the times, not possible yet.</p><p>On one hand, single trees (or small groups of them) can be modelled in detail from dense point clouds (e.g. using quantitative structure models), but this processes usually require  complete absence of leaves and  intense and/or active operator labouring. On the other hand, many methods automate the location of the trees in a plot and the estimation of basic parameters, like the diameters and, sometimes, the total tree height.</p><p>We are developing a fully automatic method that lies in between some very accurate but labour-intensive single-tree models, and the mere location and diameter calculation of the trees in a plot. Our method is able to automatically detect and locate the trees in a plot and calculate diameters, but it is also able to characterize the 3D tree structure: stem model, inclination and curvature; inclination and location of the main branches (in some cases); and tree crown individualization and diameter estimation. In addition, our method also classifies the points on understory vegetation.</p><p>Our method relies on the integration of algorithms that have been developed by our team, and includes the development of new modules. The first step consists in an initial classification of the point cloud using a multiscale approach based on local shapes. As a result, the point cloud is preliminarily classified into three classes: stems, branches and leaves, and ground. After that, a series of geometric operations lead to the final 3D characterization of the plot structure: (i) stem axes and section modelling (from the pre-classified points on the stems), (ii) distance points-closest stem axis and tree individualization, (iii) extraction and characterization of the main branches, and (iv) final classification of the points laying on stems, main branches, rest of the canopy, understory and ground.</p><p>We are testing the algorithm in several forest plots with coniferous and broadleaf trees. Initial results show values of completeness and correctness for tree detection and point classification over 90%.</p><p>Currently, there are already several cross-cutting projects using our method´s results as inputs: (i) Automatic calculation of taper functions (use: diameters along the stem and tree height), (ii) wood quality based on shape (use: diameters along the stem and insertion of main branches), and (iii) wildfire behaviour models (use: fuel classification and 3D structure to adapt the data to the format of the existing 3D fuel standard models).</p>

scholarly journals Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

2020 ◽  
Vol 12 (20) ◽  
pp. 3327 ◽  
Author(s):  
Eric Hyyppä ◽  
Xiaowei Yu ◽  
Harri Kaartinen ◽  
Teemu Hakala ◽  
Antero Kukko ◽  
...  
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Tree Height ◽  
Tree Level ◽  
Stem Volume ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Individual Trees

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

scholarly journals Unsupervised algorithms to detect single trees in a mixed-species and multi-layered Mediterranean forest using LiDAR data

2021 ◽  
Author(s):  
Cesar Alvites ◽  
Giovanni Santopuoli ◽  
Mauro Maesano ◽  
Gherardo Chirici ◽  
Federico Valerio Moresi ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Point Cloud ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Tree Height ◽  
Tree Level ◽  
Forest Stands ◽  
Growing Stock ◽  
Tree Detection ◽  
Large Trees

Accurate measurement of forest growing stock is a prerequisite for implementing Climate-Smart Forestry strategies. This study deals with the use of Airborne Laser Scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further splitted in three horizontal layers improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3 % to 1.5 % and the RMSE ranging from 0.14 % to 1.48 %.

scholarly journals CARBON SEQUESTRATION ESTIMATION OF STREET TREES BASED ON POINT CLOUD FROM VEHICLE-BORNE LASER SCANNING SYSTEM

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 313-319
Author(s):  
Y. Zhao ◽  
Q. Hu
Keyword(s):  
Carbon Sequestration ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Scanning System ◽  
Street Trees ◽  
Ecological Benefits ◽  
Cloud Data ◽  
Crown Width ◽  
Laser Scanning System

Continuous development of urban road traffic system requests higher standards of road ecological environment. Ecological benefits of street trees are getting more attention. Carbon sequestration of street trees refers to the carbon stocks of street trees, which can be a measurement for ecological benefits of street trees. Estimating carbon sequestration in a traditional way is costly and inefficient. In order to solve above problems, a carbon sequestration estimation approach for street trees based on 3D point cloud from vehicle-borne laser scanning system is proposed in this paper. The method can measure the geometric parameters of a street tree, including tree height, crown width, diameter at breast height (DBH), by processing and analyzing point cloud data of an individual tree. Four Chinese scholartree trees and four camphor trees are selected for experiment. The root mean square error (RMSE) of tree height is 0.11m for Chinese scholartree and 0.02m for camphor. Crown widths in X direction and Y direction, as well as the average crown width are calculated. And the RMSE of average crown width is 0.22m for Chinese scholartree and 0.10m for camphor. The last calculated parameter is DBH, the RMSE of DBH is 0.5cm for both Chinese scholartree and camphor. Combining the measured geometric parameters and an appropriate carbon sequestration calculation model, the individual tree’s carbon sequestration will be estimated. The proposed method can help enlarge application range of vehicle-borne laser point cloud data, improve the efficiency of estimating carbon sequestration, construct urban ecological environment and manage landscape.

Virtual laser scanning (VLS) in forestry – Investigating appropriate 3D forest representations for LiDAR simulations with HELIOS++

2021 ◽  
Author(s):  
Hannah Weiser ◽  
Lukas Winiwarter ◽  
Jannika Schäfer ◽  
Fabian Ewald Fassnacht ◽  
Katharina Anders ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Projection Area ◽  
Species Classification ◽  
Voxel Size ◽  
Point Distribution ◽  
3D Point Clouds ◽  
Forest Models ◽  
Tree Metrics

<p>Virtual laser scanning (VLS) is a valuable method to complement expensive laser scanning data acquisition in the field. VLS refers to the simulation of LiDAR to create 3D point clouds from models of scenes, platforms and sensors mimicking real world acquisitions. In forestry, this can be used to generate training and testing data with complete ground truth for algorithms performing essential tasks such as tree detection or tree species classification. Furthermore, VLS allows for the in-depth investigation of the influence of different acquisition parameters on the point clouds and thus also the behaviour of algorithms, which is important when relating point cloud metrics to forest inventory variables. Finally, VLS can be used for acquisition planning and optimisation, as different configurations can be tested regarding their ability to create data of the required quality with minimal effort. For these purposes, we developed the open source Heidelberg LiDAR Operations Simulator HELIOS++ (written in C++) which is available on GitHub (https://github.com/3dgeo-heidelberg/helios), as a precompiled command line tool, and as Python package (pyhelios). HELIOS++ provides a high-fidelity framework for full 3D laser scanning simulations with multiple platforms and a flexible system to represent the scene. HELIOS++ models the beam divergence and supports the recording of the full waveform.</p><p>One important premise for the usefulness of VLS data is the use of an adequate 3D scene in the simulation. In this context, we conducted a study investigating point clouds simulated based on opaque voxel-based forest models computed from terrestrial laser scanning data using different voxel sizes. Coupling the LiDAR simulation with a database containing point clouds of single trees from terrestrial, UAV-borne and airborne acquisitions, allowed us to compare metrics derived from real and simulated data. Furthermore, by including the tree neighbourhood in the scene, we were able to consider occlusion effects between the trees.</p><p>We found that the voxel size is an important parameter, where values of e.g. 0.25 m lead to unrealistic occlusion effects of the mid- and understory, as only few gaps remain in the forest models through which the laser beam can pass. This results in fewer multiple returns, the vertical point distribution is shifted upwards, and tree metrics such as crown projection area and crown base height are estimated poorly. Smaller voxel sizes are therefore preferable, though the appropriate voxel size depends on the resolution of the input point cloud. With very small voxels, the voxel model may become too transparent. To achieve realistic simulations without the need for a high number of voxels we suggest variable downscaling of voxel cubes based on appropriate local metrics such as the plant area density. This approach decreases the computational requirements for the simulation, as fewer primitives are present in the scene. In our study, the use of such scaled voxels derived for a grid size of 0.25 m achieves equally and partly more reliable estimates of point cloud and tree metrics than regular voxels at fixed side lengths of 0.05 and 0.02 m.</p>

Spray Volume, Canopy Density, and Other Factors Involved in Thinner Efficacy

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553d-553
Author(s):  
C.R. Unrath
Keyword(s):  
Tree Height ◽  
Tree Canopy ◽  
Tree Size ◽  
Water Volume ◽  
Canopy Density ◽  
The North ◽  
Run Off ◽  
Tree Canopies ◽  
Average Water ◽  
Water Volumes

Historically, most airblast chemical applications to apple orchards used a single “average” water volume, resulting in variability of coverage with tree size and also the greatest variable in chemical thinning. This coverage variability can be eliminated by properly quantifying the tree canopy, as tree row volume (TRV), and relating that volume to airblast water rate for adequate coverge. Maximum typical tree height, cross-row limb spread, and between-row spacing are used to quantify the TRV. Further refinement is achieved by adjusting the water volume for tree canopy density. The North Carolina TRV model allows a density adjustment from 0.7 gal/1000 ft3 of TRV for young, very open tree canopies to 1.0 gal/1000 ft3 of TRV for large, thick tree canopies to deliver a full dilute application for maximum water application (to the point of run-off). Most dilute pesticide applications use 70% of full dilute to approach the point of drip (pesticide dilute) to not waste chemicals and reduce non-target environmental exposure. From the “chemical load” (i.e., lb/acre) calculated for the pesticide dilute application, the proper chemical load for lower (concentrate) water volumes can be accurately determined. Another significant source of variability is thinner application response is spray distribution to various areas of the tree. This variability is related to tree configuration, light, levels, fruit set, and natural thinning vs. the need for chemical thinning. Required water delivery patterns are a function of tree size, form, spacing, and density, as well as sprayer design (no. of nozzles and fan size). The TRV model, density adjustments, and nozzle patterns to effectively hit the target for uniform crop load will be addressed.

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