LiDAR Principles, Processing and Applications in Forest Ecology
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience.
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| Main Authors | , , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier Science & Technology
2023
Academic Press |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780128238943 0128238941 |
| DOI | 10.1016/C2020-0-00694-8 |
Cover
Table of Contents:
- 9 - Forest Structural and Functional Attribute Upscaling Using Spaceborne LiDAR Data
- Front Cover -- LIDAR PRINCIPLES, PROCESSING AND APPLICATIONS IN FOREST ECOLOGY -- LIDAR PRINCIPLES, PROCESSING AND APPLICATIONS IN FOREST ECOLOGY -- Copyright -- Contents -- Preface -- 1 - The Origin and Development of LiDAR Techniques -- 1.1 Introduction and History of LiDAR -- 1.2 Classification of LiDAR Hardware -- 1.3 Commercial LiDAR Hardware -- 1.4 LiDAR Software -- 1.5 The Importance of LiDAR in Forest Ecology Applications -- 1.6 Chapter Summary -- References -- 2 - Working Principles of LiDAR -- 2.1 Ranging Principle of LiDAR -- 2.1.1 Pulse Laser Ranging -- 2.1.2 Phase Laser Ranging -- 2.1.3 Ranging Accuracy -- 2.2 Radiation Principle of LiDAR -- 2.2.1 Introduction to the LiDAR Equation -- 2.2.2 Methods of Recording the Return Signal -- 2.3 Working Principle of Terrestrial LiDAR -- 2.3.1 System Components of Terrestrial LiDAR -- 2.3.2 Laser Scanning Metrics for Terrestrial LiDAR Systems -- 2.4 Working Principle of Near-Surface LiDAR -- 2.4.1 System Components of Backpack LiDAR -- 2.4.2 System Components of Mobile LiDAR -- 2.4.3 System Components of Unmanned Aerial Vehicle LiDAR -- 2.4.4 System Metrics for Near-Surface LiDAR Systems -- 2.5 Working Principle of Airborne LiDAR -- 2.5.1 Systems Components of Airborne LiDAR -- 2.5.2 System Metrics for Airborne LiDAR Systems -- 2.6 Working Principle of Spaceborne LiDAR -- 2.6.1 System Components of Spaceborne LiDAR -- 2.6.2 System Metrics for Spaceborne LiDAR Systems -- 2.7 Chapter Summary -- References -- 3 - LiDAR Field Workflow and Systematic Error Sources -- 3.1 Basic Operation of Terrestrial LiDAR -- 3.1.1 Preparation for Terrestrial LiDAR Scanning -- 3.1.1.1 Information Collection for Areas of Interest -- 3.1.1.2 Initial Design of Scanning Route -- 3.1.1.3 Field Surveying of the Working Area -- 3.1.2 Scanning Operational Planning -- 3.1.3 Terrestrial LiDAR Data Collection
- 3.1.3.1 Preparing and Checking the Scanning Equipment -- 3.1.3.2 Detailed Scanning Process -- 3.1.3.3 Rough Inspection of Data Quality -- 3.1.4 Initial Data Inspection -- 3.2 Basic Operation of Mobile and Backpack LiDAR -- 3.2.1 Overview of Mobile and Backpack LiDAR Operation -- 3.2.2 Preparation for Mobile and Backpack LiDAR Scanning -- 3.2.3 Scanning Operational Planning -- 3.2.3.1 Mobile LiDAR System -- 3.2.3.1.1 Route Planning -- 3.2.3.1.2 Global Navigation Satellite System Base Station Setup -- 3.2.3.2 Backpack LiDAR System -- 3.2.3.2.1 Route Planning -- 3.2.3.2.2 Global Navigation Satellite System Base Station Setup -- 3.2.4 Mobile and Backpack LiDAR Data Collection -- 3.2.4.1 Mobile LiDAR System -- 3.2.4.2 Backpack LiDAR System -- 3.2.5 Initial Data Inspection -- 3.2.5.1 Mobile LiDAR System -- 3.2.5.2 Backpack LiDAR System -- 3.3 Basic Operation of Airborne and Unmanned Aerial Vehicle LiDAR -- 3.3.1 Overview of Airborne and Unmanned Aerial Vehicle LiDAR Operation -- 3.3.2 Preparation for Aerial LiDAR Survey -- 3.3.2.1 Application for Airspace Authorization -- 3.3.2.2 Information Collection for Areas of Interest -- 3.3.2.3 Field Survey -- 3.3.3 Scanning Operational Planning -- 3.3.3.1 Route Planning -- 3.3.3.2 Setting Up Global Navigation Satellite System Base Stations -- 3.3.3.3 Selection of the Calibration Field -- 3.3.4 Airborne and Unmanned Aerial Vehicle LiDAR Data Collection -- 3.3.4.1 System Test and Setting Up Global Navigation Satellite System Base Station on the Ground -- 3.3.4.2 Preparation and Sensor Adjustment -- 3.3.4.3 LiDAR Field Data Acquisition -- 3.3.5 Initial Data Inspection -- 3.4 Error Sources in LiDAR Data Collection -- 3.4.1 Platform Vibration Error -- 3.4.2 Position and Orientation System Error -- 3.4.3 Scanner Error -- 3.4.4 System Integration Error -- 3.5 Chapter Summary -- References -- 4 - LiDAR Data Formats
- 4.1 Format, Composition, and Characteristics of Point Cloud Data -- 4.1.1 Format and Composition of Point Cloud Data -- 4.1.1.1 LAS Format -- 4.1.1.2 ASCII Format -- 4.1.2 Characteristics of Point Cloud Data -- 4.2 Indexing of Point Cloud Data -- 4.2.1 Regular Grid -- 4.2.2 k-d Tree -- 4.2.3 Octree -- 4.3 Reading Point Cloud Data -- 4.3.1 Why Python? -- 4.3.2 Basic Syntax of Python -- 4.3.2.1 Python Variable Types -- 4.3.2.2 Basic Operations and Flow Control of Python -- 4.3.2.3 Important Libraries Used in this Book -- 4.3.3 Reading LiDAR Point Cloud Data Python -- 4.4 Reading Full-Waveform Data -- 4.4.1 Introduction to Full-Waveform LiDAR Data -- 4.4.2 Common Full-Waveform LiDAR Data Formats -- 4.4.3 Reading Full-Waveform Data -- 4.5 Chapter Summary -- References -- 5 - Data Preprocessing and Feature Extraction -- 5.1 Point Cloud Resolving -- 5.1.1 Geometric Approach -- 5.1.2 Simultaneous Localization and Mapping Approach -- 5.1.3 Global Navigation Satellite System-Inertial Measurement Unit Navigation Approach -- 5.1.4 Integrated Approach -- 5.2 Point Cloud Registration -- 5.2.1 Multiscan Terrestrial LiDAR Data Registration -- 5.2.2 Mobile LiDAR Data Registration -- 5.2.3 Airborne LiDAR Data Registration -- 5.2.4 Multiplatform LiDAR Data Registration -- 5.3 LiDAR Point Cloud Denoising -- 5.3.1 Denoising Algorithms Based on the Spatial Distribution of Points -- 5.3.2 Cluster-Based Denoising Algorithm -- 5.3.3 Density-Based Denoising Algorithm -- 5.4 Point Cloud Feature Extraction -- 5.4.1 Geometric Features -- 5.4.2 Statistical Features -- 5.4.3 Topological Features -- 5.5 Point Cloud Classification -- 5.5.1 Point Cloud Classification Based on Model Fitting -- 5.5.2 Point Cloud Classification Based on Region Growing -- 5.5.3 Clustering-Based Point Cloud Classification -- 5.5.4 Hierarchical Point Cloud Classification
- 5.5.5 Machine Learning-Based Point Cloud Classification -- 5.5.6 Deep Learning-Based Point Cloud Classification -- 5.6 Chapter Summary -- References -- 6 - LiDAR Data Filtering and Digital Elevation Model Generation -- 6.1 Introduction to LiDAR Data Filtering -- 6.1.1 Basic Concepts -- 6.1.2 Challenges in LiDAR Data Filtering -- 6.2 Introduction to Filtering Methods -- 6.2.1 Slope-Based Filtering Methods -- 6.2.2 Morphological Filtering Methods -- 6.2.3 Interpolation-Based Filtering Algorithms -- 6.2.4 Progressive Densification Filtering Methods -- 6.2.5 Filtering Methods Based on Segmentation -- 6.2.6 Filtering Methods Combining Other Information -- 6.2.6.1 Fusion with Intensity Information -- 6.2.6.2 Fusion with Full-Waveform Information -- 6.2.6.3 Fusion With Optical Images -- 6.2.7 Deep Learning-Based Filtering Methods -- 6.3 Accuracy Evaluation Methods -- 6.3.1 Quantitative Evaluation -- 6.3.2 Qualitative Evaluation -- 6.4 Digital Elevation Model Generation -- 6.4.1 Digital Elevation Model Interpolation Methods -- 6.4.1.1 Inverse Distance Weighting Interpolation -- 6.4.1.2 Natural Neighbor Interpolation -- 6.4.1.3 Kriging Interpolation -- 6.4.1.4 Radial Basis Function Interpolation -- 6.4.2 Digital Elevation Model Error Sources -- 6.4.2.1 Errors from the LiDAR System -- 6.4.2.2 Errors from Point Cloud Processing -- 6.4.2.3 Errors from Geographical Factors -- 6.4.2.4 Errors from the Digital Elevation Model Resolution -- 6.4.3 Digital Elevation Model Accuracy Analysis -- 6.5 Chapter Summary -- References -- 7 - Forest Structural Attribute Extraction -- 7.1 Stand-Level Structural Attribute Extraction -- 7.1.1 Canopy Height and Canopy Gap Detection -- 7.1.2 Canopy Height Profile -- 7.1.3 Intensity Profile -- 7.1.4 Regression-Based Structural Attribute Extraction -- 7.2 Individual Tree Segmentation
- 7.2.1 Canopy Height Model-Based Individual Tree Segmentation Based on Airborne LiDAR Data -- 7.2.2 Point Cloud-Based Individual Tree Segmentation Based on Airborne LiDAR Data -- 7.2.3 Comparison Between Point Cloud-Based and Canopy Height Model-Based Individual Tree Segmentation Algorithms -- 7.2.4 Individual Tree Segmentation Based on Mobile and Terrestrial LiDAR Data -- 7.2.5 Individual Tree Segmentation Using Deep Learning -- 7.3 Wood-Leaf Separation -- 7.3.1 Threshold-Based Methods -- 7.3.2 Geometry-Based Methods -- 7.3.3 Machine Learning-Based Methods -- 7.4 Individual Tree- and Organ-Level Structural Attribute Extraction -- 7.4.1 Basic Forest Inventory Attributes -- 7.4.1.1 Tree Height -- 7.4.1.2 Diameter at Breast Height -- 7.4.1.3 Crown Base Height -- 7.4.2 Crown Size Attributes -- 7.4.3 Branch Architecture Attributes -- 7.4.3.1 Branch Angle -- 7.4.3.2 Branch Diameter and Length -- 7.4.3.3 Branch Volume -- 7.4.4 Timber-Related Attributes -- 7.4.5 Leaf-Related Attributes -- 7.4.5.1 Voxelization -- 7.4.5.2 Leaf Segmentation and Filtration -- 7.4.5.3 Leaf Inclination and Azimuthal Angle Estimation -- 7.5 Structural Attributes Extracted Through the Fusion of Multiplatform LiDAR Data -- 7.6 Chapter Summary -- References -- 8 - Estimation of Forest Functional Attributes -- 8.1 Canopy Cover and Closure -- 8.2 Leaf Area Index -- 8.2.1 Theoretical Basis -- 8.2.2 Leaf Area Index Extraction from Terrestrial LiDAR Data -- 8.2.3 Leaf Area Index Extraction from Airborne LiDAR Data -- 8.3 Growing Stock and Biomass -- 8.3.1 Growing Stock and Biomass Estimation at the Individual Tree Level -- 8.3.2 Growing Stock and Biomass Estimation at the Community Level -- 8.4 Tree Species Classification -- 8.4.1 Tree Species Classification at the Individual Tree Level -- 8.4.2 Tree Species Classification at the Community Level -- 8.5 Chapter Summary -- References