Parameterization of Aerodynamic Roughness Length and Zero Plane Displacement Over Tropical Region Using Airborne LiDAR Data

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Muhammad Zulkarnain Abdul Rahman ◽  
Faiznor Farok ◽  
Abd Wahid Rasib ◽  
Wan Hazli Wan Kadir
Keyword(s):  
Airborne Lidar ◽  
Frontal Area ◽  
Tropical Region ◽  
Surface Model ◽  
Lidar Data ◽  
Individual Tree ◽  
Crown Delineation ◽  
Aerodynamic Roughness ◽  
Plane Displacement ◽  
Airborne Lidar Data

Airborne LiDAR data has been one of the reliable data for individual tree properties estimation. High density airborne LiDAR data has been used previously for detailed reconstruction of tree geometry. The aim of this study is to estimate aerodynamic roughness over specific height (Zo/H) and zero plane displacement (do) over forest area using airborne LiDAR data. The results of this study will be very useful as a main guideline for related applications to understand the role of carbon and hydrological cycles, land cover and land use change, habitat fragmentation, and biogeographical modeling. The airborne LiDAR data is first classified into ground and non-ground classes. The ground points are interpolated for digital terrain model (DTM) generation and the non-ground points are used to generate digital surface model (DSM). Canopy height model (CHM) is then generated by subtracting DTM from DSM. Individual tree delineation is carried out on the CHM and individual tree height is used together with allometric equation in estimating height to crown base (HCB) and diameter at breast height (DBH). Tree crown delineation is carried out using the Inverse Watershed segmentation approach. Crown diameter, HBC and DBH are used to estimate individual tree frontal area and the total frontal area over a specific ground surface is further calculated by subtracting the intersected crowns and trunks from the total area of tree crowns and trunks. The considered ground area i.e. plants area determined the final spatial resolution of the Zo/H and do. Both parameters are calculated for different wind directions that were assumed to be originated from North/South and East/West. The results show that the estimated Zo/H and do have similar pattern and values with previous studies over vegetated area. 

scholarly journals ESTIMATION OF AERODYNAMIC ROUGHNESS AND ZERO PLANE DISPLACEMENT USING MEDIUM DENSITY OF AIRBORNE LIDAR DATA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 217-224
Author(s):  
M. R. Mohd Salleh ◽  
M. Z. Abdul Rahman ◽  
M. A. Abu Bakar ◽  
A. W. Rasib ◽  
H. Omar
Keyword(s):  
Structural Parameters ◽  
Ground Surface ◽  
Airborne Lidar ◽  
Frontal Area ◽  
Surface Model ◽  
Lidar Data ◽  
Individual Tree ◽  
Aerodynamic Roughness ◽  
Plane Displacement ◽  
Airborne Lidar Data

This paper presents a framework to estimate aerodynamic roughness over specific height (<i>zo/H</i>) and zero plane displacement (<i>d/H</i>) over various landscapes in Kelantan State using airborne LiDAR data. The study begins with the filtering of airborne LiDAR, which produced ground and non-ground points. The ground points were used to generate digital terrain model (DTM) while the non-ground points were used for digital surface model (DSM) generation. Canopy height model (CHM) was generated by subtracting DTM from DSM. Individual trees in the study area were delineated by applying the Inverse Watershed segmentation method on the CHM. Forest structural parameters including tree height, height to crown base (HCB) and diameter at breast height (DBH) were estimated using existing allometric equations. The airborne LiDAR data was divided into smaller areas, which correspond to the size of the <i>zo/H</i> and <i>d/H</i> maps i.e. 50 m and 100 m. For each area individual tree were reconstructed based on the tree properties, which accounts overlapping between crowns and trunks. The individual tree models were used to estimate individual tree frontal area and the total frontal area over a specific ground surface. Finally, three roughness models were used to estimate <i>zo/H</i> and <i>d/H</i> for different wind directions, which were assumed from North/South and East/West directions. The results were shows good agreements with previous studies that based on the wind tunnel experiments.

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.

scholarly journals ROAD NETWORK MAPPING USING AIRBORNE LiDAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 707-711 ◽  
Author(s):  
S. Upadhayay ◽  
M. Yadav ◽  
D. P. Singh
Keyword(s):  
Road Network ◽  
Spatial Databases ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Classification Approach ◽  
Modified Maximum Likelihood ◽  
Road Network Extraction ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this research is to develop an efficient algorithm for road network extraction from airborne LiDAR data using supervised classification approach. The proposed algorithm first classifies the input data into the road and non-road features using modified maximum likelihood classification approach. Then Digital Terrain Model (DTM) mask is generated by removing non-ground features from Digital Surface Model using hierarchical morphology and road candidate image if obtained. The parking lots are removed and road network is extracted successfully.</p>

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