scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343 ◽  
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

scholarly journals Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 571 ◽  
Author(s):  
Chen ◽  
Xiang ◽  
Moriya
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Lidar Data ◽  
Individual Tree ◽  
Matching Algorithm ◽  
Crown Width ◽  
Individual Trees ◽  
Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

scholarly journals Transformation of even-aged spruce stands at the School Forest Enterprise Kostelec nad Černými lesy: Structure and final cutting of mature stand

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 158-171 ◽  
Author(s):  
J. Remeš
Keyword(s):  
Forest Stand ◽  
Growth Potential ◽  
Main Criterion ◽  
Individual Tree ◽  
Main Method ◽  
Single Tree ◽  
Mature Stand ◽  
Spruce Stands ◽  
Individual Trees ◽  
Very High

This paper deals with the transformation of pure even-aged forest stands to mixed and more uneven-aged stands on an example of selected even-aged Norway spruce stands in the School Forest Enterprise (SFE) in Kostelec nad Černými lesy. A forest stand where individual tree felling was used as the main method of forest stand regeneration was chosen as a conversion example. The main criterion of tree maturity is the culmination of mean volume increment of a single tree. The analyses confirmed a very high variability in the growth potential of individual trees. The potential and actual increment was strongly influenced by the stand position of tree and by crown release. These results show a high potential level of tree growth even at the age of 120 years. From 30% to 9% of all trees on particular experimental plots achieved felling maturity.

scholarly journals ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 585-588
Author(s):  
K. T Chang ◽  
C. Lin ◽  
Y. C. Lin ◽  
J. K. Liu
Keyword(s):  
Forest Canopy ◽  
Accuracy Assessment ◽  
Tree Height ◽  
Experimental Results ◽  
Estimation Methods ◽  
Surface Model ◽  
Individual Tree ◽  
Free Process ◽  
Crown Shape ◽  
Individual Trees

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

scholarly journals Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

2019 ◽  
Vol 11 (13) ◽  
pp. 1551 ◽  
Author(s):  
Heejoon Choi ◽  
Youngkeun Song ◽  
Youngwoon Jang
Keyword(s):  
Change Detection ◽  
Leaf Area ◽  
Growth Rates ◽  
Urban Forest ◽  
Urban Forests ◽  
Forest Growth ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Area Index

Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests.

scholarly journals SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM

2017 ◽  
Vol IV-1/W1 ◽  
pp. 141-148 ◽  
Author(s):  
C. Dechesne ◽  
C. Mallet ◽  
A. Le Bris ◽  
V. Gouet-Brunet
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Airborne Lidar ◽  
Forest Stands ◽  
Pure Species ◽  
Individual Tree ◽  
Very High Spatial Resolution ◽  
Very High

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

scholarly journals Predicting forest stand variables from airborne LiDAR data using a tree detection method in Central European forests

2019 ◽  
Vol 65 (3-4) ◽  
pp. 191-197 ◽  
Author(s):  
Ivan Sačkov ◽  
Ľubomír Scheer ◽  
Tomáš Bucha
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Individual Tree ◽  
Tree Detection ◽  
Detection Approach ◽  
Mean Diameter ◽  
Priori Information ◽  
The Individual

Abstract In this study, the individual tree detection approach (ITD) was used to estimate forest stand variables, such as mean height, mean diameter, and total volume. Specifically, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia) which uses all the information contained in the original point cloud and a priori information. For the accuracy assessment, four reference forest stands with different types of species mixture and the area of 7.5 ha were selected and measured. Furthermore, independent measurements of 1 372 trees were made for the construction of allometric models. The author’s ITD-based method provided slightly more accurate estimations for stands with substantial or moderate dominance of coniferous trees. However, no statistically significant effect of species mix on the overall accuracy was confirmed (p < 0.05). The root mean square error did not exceed 1.9 m for mean height, 3.0 cm for mean diameter, and 12.88 m3 ha−1 for total volume.

scholarly journals A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

2021 ◽  
Vol 13 (2) ◽  
pp. 322
Author(s):  
Melissa Latella ◽  
Fabio Sola ◽  
Carlo Camporeale
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Tree Distribution ◽  
Tree Detection ◽  
Forest Modeling ◽  
Point Density ◽  
Potential Applications ◽  
Field Campaigns ◽  
The Individual ◽  
Individual Trees

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

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