scholarly journals A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 537 ◽  
Author(s):  
Jiarong Tian ◽  
Tingting Dai ◽  
Haidong Li ◽  
Chengrui Liao ◽  
Wenxiu Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Coniferous Forest ◽  
Tree Height ◽  
High Density ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Tree Farms ◽  
Uav Image ◽  
Structure Evaluation

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

scholarly journals Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

2021 ◽  
Vol 13 (1) ◽  
pp. 705-716
Author(s):  
Qiuji Chen ◽  
Xin Wang ◽  
Mengru Hang ◽  
Jiye Li
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Tree Height ◽  
Mining Area ◽  
Research Area ◽  
Airborne Lidar ◽  
Shaanxi Province ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

scholarly journals Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

2018 ◽  
Vol 10 (11) ◽  
pp. 1849 ◽  
Author(s):  
Jiayuan Lin ◽  
Meimei Wang ◽  
Mingguo Ma ◽  
Yi Lin
Keyword(s):  
Point Cloud ◽  
Coniferous Forest ◽  
Digital Camera ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Surface Model ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Subalpine Coniferous Forest

In tree Aboveground Biomass (AGB) estimation, the traditional harvest method is accurate but unsuitable for a large-scale forest. The airborne Light Detection And Ranging (LiDAR) is superior in obtaining the point cloud data of a dense forest and extracting tree heights for AGB estimation. However, the LiDAR has limitations such as high cost, low efficiency, and complicated operations. Alternatively, the overlapping oblique photographs taken by an Unmanned Aerial Vehicle (UAV)-loaded digital camera can also generate point cloud data using the Aerial Triangulation (AT) method. However, limited by the relatively poor penetrating capacity of natural light, the photographs captured by the digital camera on a UAV are more suitable for obtaining the point cloud data of a relatively sparse forest. In this paper, an electric fixed-wing UAV loaded with a digital camera was employed to take oblique photographs of a sparse subalpine coniferous forest in the source region of the Minjiang River. Based on point cloud data obtained from the overlapping photographs, a Digital Terrain Model (DTM) was generated by filtering non-ground points along with the acquisition of a Digital Surface Model (DSM) of Minjiang fir trees by eliminating subalpine shrubs and meadows. Individual tree heights were extracted by overlaying individual tree outlines on Canopy Height Model (CHM) data computed by subtracting the Digital Elevation Model (DEM) from the rasterized DSM. The allometric equation with tree height (H) as the predictor variable was established by fitting measured tree heights with tree AGBs, which were estimated using the allometric equation on H and Diameter at Breast Height (DBH) in sample tree plots. Finally, the AGBs of all of the trees in the test site were determined by inputting extracted individual tree heights into the established allometric equation. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted individual tree heights were 0.92 and 1.77 m, and the R2 and RMSE of the estimated AGBs of individual trees were 0.96 and 54.90 kg. The results demonstrated the feasibility and effectiveness of applying UAV-acquired oblique optical photographs to the tree AGB estimation of sparse subalpine coniferous forests.

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

2020 ◽  
Vol 2674 (11) ◽  
pp. 192-206
Author(s):  
Yaneev Golombek ◽  
Wesley E. Marshall
Keyword(s):  
Point Cloud ◽  
High Density ◽  
Light Detection And Ranging ◽  
Quality Level ◽  
United States Geological Survey ◽  
Point Cloud Data ◽  
Street Trees ◽  
Light Detection ◽  
Cloud Data ◽  
Traffic Lights

This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.

scholarly journals Extracting features buried within high density atom probe point cloud data through simplicial homology

2015 ◽  
Vol 159 ◽  
pp. 374-380 ◽  
Author(s):  
Srikant Srinivasan ◽  
Kaustubh Kaluskar ◽  
Scott Broderick ◽  
Krishna Rajan
Keyword(s):  
Point Cloud ◽  
Atom Probe ◽  
High Density ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Simplicial Homology ◽  
Probe Point

scholarly journals Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction

2014 ◽  
Vol XL-8 ◽  
pp. 573-580 ◽  
Author(s):  
S. D. Jawak ◽  
S. N. Panditrao ◽  
A. J. Luis
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Point Cloud ◽  
Extraction Process ◽  
Surface Model ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Building Footprint ◽  
3D Feature Extraction

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.

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.

UAV-derived Estimates of Vertical and Horizontal Structure across Forest Density Gradients

2020 ◽  
Author(s):  
Temuulen Sankey ◽  
Adam Belmonte
Keyword(s):  
Density Gradient ◽  
Forest Structure ◽  
Survival Rates ◽  
Tree Height ◽  
Single Species ◽  
High Density ◽  
Individual Tree ◽  
Horizontal Structure ◽  
Forest Density ◽  
Uav Image

<p>Restoring forest ecosystems and predicting forest response to projected climate change have become an increasingly high priority for land managers. Restoration and management goals require accurate, quantitative estimates of vertical and horizontal forest structure, which has relied upon either field-based measurements, manned airborne, or satellite remote sensing datasets. We use unmanned aerial vehicle (UAV) image-derived structure from motion (SfM) models and high resolution multispectral and thermal orthoimagery to: 1) quantify vertical and horizontal forest structure at both fine- (< 4 ha) and mid-scales (4-400 ha) across a forest density gradient, and 2) quantify horizontal structure, health, and survival rates in a genetics experimental garden also with a density gradient. In both cases, we find that UAV multispectral and thermal image-derived SfM model estimates of individual tree height and canopy diameter are most accurate in low-density conditions, with accuracies degrading significantly in high-density conditions. In addition, UAV thermal images demonstrate significant differences in tree health and survival rates among various populations and genotypes within a single species.  Mid-scale estimates of canopy cover and forest density follow a similar pattern across the density gradient, demonstrating the effectiveness of UAV image-derived estimates in low to medium-density conditions as well as the challenges associated with high-density conditions.</p>

scholarly journals A HADOOP-BASED ALGORITHM OF GENERATING DEM GRID FROM POINT CLOUD DATA

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1209-1214 ◽  
Author(s):  
X. Jian ◽  
X. Xiao ◽  
H. Chengfang ◽  
Z. Zhizhong ◽  
W. Zhaohui ◽  
...  
Keyword(s):  
Point Cloud ◽  
Programming Model ◽  
Transmission Rate ◽  
High Density ◽  
Airborne Lidar ◽  
Cost Ratio ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Traditional Procedure ◽  
Point Set

Airborne LiDAR technology has proven to be the most powerful tools to obtain high-density, high-accuracy and significantly detailed surface information of terrain and surface objects within a short time, and from which the Digital Elevation Model of high quality can be extracted. Point cloud data generated from the pre-processed data should be classified by segmentation algorithms, so as to differ the terrain points from disorganized points, then followed by a procedure of interpolating the selected points to turn points into DEM data. The whole procedure takes a long time and huge computing resource due to high-density, that is concentrated on by a number of researches. Hadoop is a distributed system infrastructure developed by the Apache Foundation, which contains a highly fault-tolerant distributed file system (HDFS) with high transmission rate and a parallel programming model (Map/Reduce). Such a framework is appropriate for DEM generation algorithms to improve efficiency. Point cloud data of Dongting Lake acquired by Riegl LMS-Q680i laser scanner was utilized as the original data to generate DEM by a Hadoop-based algorithms implemented in Linux, then followed by another traditional procedure programmed by C++ as the comparative experiment. Then the algorithm’s efficiency, coding complexity, and performance-cost ratio were discussed for the comparison. The results demonstrate that the algorithm's speed depends on size of point set and density of DEM grid, and the non-Hadoop implementation can achieve a high performance when memory is big enough, but the multiple Hadoop implementation can achieve a higher performance-cost ratio, while point set is of vast quantities on the other hand.

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