Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

2018 ◽  
Author(s):  
Daniele Marinelli ◽  
Claudia Paris ◽  
Lorenzo Bruzzone
Keyword(s):  
Parameter Estimation ◽  
Point Clouds ◽  
Lidar Data ◽  
Low Density ◽  
Density Point ◽  
Tree Crown ◽  
Individual Tree

scholarly journals From LiDAR waveforms to Hyper Point Clouds: a novel data product to characterize vegetation structure

2018 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu ◽  
Lonesome Malambo ◽  
Kaiguang Zhao ◽  
Keith Krause
Keyword(s):  
Vegetation Structure ◽  
Point Clouds ◽  
Structure Characterization ◽  
Tree Level ◽  
Surface Model ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Segment Tree ◽  
Crown Delineation

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

scholarly journals From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure

2018 ◽  
Vol 10 (12) ◽  
pp. 1949 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu ◽  
Lonesome Malambo ◽  
Kaiguang Zhao ◽  
Keith Krause
Keyword(s):  
Vegetation Structure ◽  
Point Clouds ◽  
Structure Characterization ◽  
Tree Level ◽  
Surface Model ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Segment Tree ◽  
Crown Delineation

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

Individual Tree Measurement in Tropical Environment using Terrestrial Laser Scanning

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Muhammad Zulkarnain Abdul Rahman ◽  
Zulkepli Majid ◽  
Md Afif Abu Bakar ◽  
Abd Wahid Rasib ◽  
Wan Hazli Wan Kadir
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Crown ◽  
Individual Tree ◽  
Branch Diameter ◽  
Reference Tree ◽  
Crown Volume ◽  
Tree Branch

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models.   

scholarly journals Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features

2020 ◽  
Vol 12 (7) ◽  
pp. 1078 ◽  
Author(s):  
Zhenyu Ma ◽  
Yong Pang ◽  
Di Wang ◽  
Xiaojun Liang ◽  
Bowei Chen ◽  
...  
Keyword(s):  
Region Growing ◽  
Point Clouds ◽  
Tree Canopy ◽  
Segmentation Method ◽  
Tree Crown ◽  
Two Stage ◽  
Individual Tree ◽  
Tree Structures ◽  
Larch Plantation

The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.

scholarly journals Automatic Delineation and Height Measurement of Regenerating Conifer Crowns under Leaf-Off Conditions Using UAV Imagery

2020 ◽  
Vol 12 (24) ◽  
pp. 4104
Author(s):  
Andrew J. Chadwick ◽  
Tristan R. H. Goodbody ◽  
Nicholas C. Coops ◽  
Anne Hervieux ◽  
Christopher W. Bater ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Picea Glauca ◽  
Pinus Contorta ◽  
Tree Height ◽  
Point Clouds ◽  
Strongly Correlated ◽  
Tree Crown ◽  
Height Measurement ◽  
Individual Tree ◽  
Fine Spatial Resolution

The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (r2 = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was −0.37 m and −0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height.

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