Individual Tree Crown Modeling and Change Detection From Airborne Lidar Data

2016 ◽  
Vol 9 (8) ◽  
pp. 3467-3477 ◽  
Author(s):  
Wen Xiao ◽  
Sudan Xu ◽  
Sander Oude Elberink ◽  
George Vosselman
Keyword(s):  
Change Detection ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Airborne Lidar Data

scholarly journals AUTOMATIC BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 19-24
Author(s):  
R. C. dos Santos ◽  
M. Galo ◽  
A. C. Carrilho ◽  
G. G. Pessoa ◽  
R. A. R. de Oliveira
Keyword(s):  
Change Detection ◽  
Average Density ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Qualitative And Quantitative ◽  
Multi Temporal ◽  
Quantitative Analyses ◽  
3D Information ◽  
Airborne Lidar Data ◽  
Area Monitoring

Abstract. The automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, 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 Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data

2019 ◽  
Vol 11 (1) ◽  
pp. 97 ◽  
Author(s):  
Lin Cao ◽  
Zhengnan Zhang ◽  
Ting Yun ◽  
Guibin Wang ◽  
Honghua Ruan ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Stem Density ◽  
Shape Parameters ◽  
Individual Tree ◽  
Subtropical Forests ◽  
Weibull Parameters ◽  
Error Index ◽  
Airborne Lidar Data ◽  
Parameter Prediction

Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.

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