scholarly journals A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

2019 ◽  
Vol 11 (18) ◽  
pp. 2109 ◽  
Author(s):  
Xiao-Hu Wang ◽  
Yi-Zhuo Zhang ◽  
Miao-Miao Xu
Keyword(s):  
Parameter Extraction ◽  
Airborne Lidar ◽  
Tree Level ◽  
Surface Model ◽  
Individual Tree ◽  
Threshold Segmentation ◽  
Percentage Points ◽  
Segmentation Approach ◽  
Small Footprint ◽  
Understory Trees

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

scholarly journals Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment

2015 ◽  
Vol 63 (3) ◽  
pp. 793-801 ◽  
Author(s):  
Tomáš Mikita ◽  
Petr Balogh
Keyword(s):  
Risk Assessment ◽  
Airborne Lidar ◽  
Forest Recovery ◽  
Surface Model ◽  
Spatial Decision Support System ◽  
Individual Tree ◽  
Wood Volume ◽  
Volume Calculation ◽  
Growing Stock ◽  
Precision Forestry

This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the first step, the growing stock on the planned clearcut determined by its location and area in feature class is calculated (by the method of individual tree detection). Subsequently tree heights from canopy height model (CHM) are extracted and then diameters at breast height (DBH) and wood volume using the regressions are calculated. Information about wood volume of each tree in the clearcut is exported and summarized in a table. In the next step, all trees in the clearcut are removed and a new DSM without trees in the clearcut is generated. This canopy model subsequently serves as an input for evaluation of wind risk damage by the MAXTOPEX tool (Mikita et al., 2012). In the final raster, predisposition of uncovered forest stand edges (around the clearcut) to wind risk is calculated based on this analysis. The entire tool works in the background of ArcGIS server as a spatial decision support system for foresters.

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 SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1001-1007 ◽  
Author(s):  
C. Yao ◽  
X. Zhang ◽  
H. Liu
Keyword(s):  
Species Identification ◽  
Tree Species ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Forest Community ◽  
Surface Model ◽  
Lidar Data ◽  
Palm Tree ◽  
Crown Height ◽  
Individual Tree

The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65&amp;thinsp;%. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

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.

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. 

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