LiDAR Principles, Processing and Applications in Forest Ecology

LiDAR Principles, Processing and Applications in Forest Ecology

1st Edition - March 1, 2023

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  • Authors: Qinghua Guo, Yanjun Su, Tianyu Hu
  • Paperback ISBN: 9780128238943

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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. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world.

Key Features

  • Presents LiDAR applications for forest ecology based in real-world experience
  • Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way
  • Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR
  • Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data
  • Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world


Researchers, teachers, students, related technicians, policymakers, and field officers in forest ecology

Table of Contents

  • 1. The Origin and Development of LiDAR Active Remote Sensing Technology
    1.1 LiDAR techniques
    1.2 Different types of LiDAR
    1.3 Introduction of commercial LiDAR equipment
    1.3.1 Introduction of foreign commercial LiDAR equipment
    1.3.2 Introduction of domestic commercial LiDAR equipment
    1.4 Introduction of LiDAR processing software
    1.5 The application value of LiDAR in forest ecology
    1.6 Chapter summary References

    2. Working principle of LiDAR
    2.1 Ranging principle of LiDAR
    2.1.1 Pulse ranging
    2.1.2 Phase ranging
    2.1.3 Ranging accuracy
    2.2 Radiation principle of LiDAR
    2.2.1 LiDAR equation
    2.2.2 Radiation echo signal
    2.3 Working principle of ground-based LiDAR
    2.3.1 Composition of ground-based LiDAR system
    2.3.2 Foundation main parameters of the laser radar system
    2.4 Working principle of airborne LiDAR
    2.4.1 Composition of airborne LiDAR system
    2.4.2 Main parameters of airborne LiDAR system
    2.5 The working principle of spaceborne LiDAR
    2.5.1 Space - borne LiDAR system composition
    2.5.2 main parameters of the spaceborne LiDAR system
    2.6 Chapter summary

    3. Field work flow and system error source of LiDAR
    3.1 Basic operation process of terrestrial LiDAR system
    3.1.1 Preparation for scanning
    3.1.2 Scanning operation planning
    3.1.3 Data collection
    3.1.4 Data initial inspection
    3.2 Basic operation procedure of airborne LiDAR scanning system
    3.2.1 Overview of airborne LiDAR operation process
    3.2.2 Preparation for aerial survey
    3.2.3 Plan and design of aerial survey scheme
    3.2.4 Collection of aerial measurement data
    3.2.5 Preliminary decoding and supplementary measurement of data
    3.3 Error sources of terrestrial LiDAR scanning system
    3.4 Error sources of airborne LiDAR scanning system
    3.4.1 Flight platform error
    3.4.2 POS system error
    3.4.3 Laser sensor error
    3.4.4 System integration error
    3.5 Summary

    4. LiDAR data format
    4.1 Format, composition, and characteristics of point cloud data
    4.1.1 Format and composition of point cloud data
    4.1.2 Characteristics of point cloud data
    4.2 Indexing of point cloud data
    4.3 Reading of point cloud data
    4.3.1 Why python?
    4.3.2 Syntax of Python
    4.3.3 Reading LiDAR data in Python
    4.4 Summary

    5. LiDAR data filtering and digital elevation model generation
    5.1 Introduction to filtering
    5.1.1 Basic concepts
    5.1.2 Difficulties in filtering
    5.2 Introduction of filtering methods
    5.2.1 Slope-based filtering method
    5.2.2 Morphological filtering method
    5.2.3 Interpolation-based filtering algorithm
    5.2.4 Progressive densification filtering method
    5.2.5 Filtering method based on segmentation
    5.2.6 Filtering method combining other information
    5.3 Filtering accuracy evaluation comparison
    5.4 Generation of digital elevation model
    5.4.1 DEM interpolation method and comparison
    5.4.2 DEM Error Analysis 5.4.3 DEM accuracy analysis
    5.5 Summary

    6. Data Analysis and Feature Extraction of Terrestrial LiDAR
    6.1 Point Cloud Resolving
    6.2 Point Cloud Registration
    6.3 Noisy and Outlier Point Removal
    6.3.1 Denoising algorithm based on spatial distribution
    6.3.2 Cluster-based denoising algorithm
    6.3.3 Density-based denoising algorithm
    6.4 Point Cloud Feature Extraction
    6.4.1 Color characteristics
    6.4.2 Local geometric features
    6.5 Point Cloud Classification
    6.5.1 Point cloud classification based on model fitting
    6.5.2 Point cloud classification based on regional growth
    6.5.3 Cluster-based point cloud classification
    6.6 Summary

    7. Data Analysis and Feature Extraction of Airborne LiDAR
    7.1 Data Processing Flow of Airborne LiDAR
    7.2 Data Resolving of Airborne LiDAR
    7.3 Strip Alignment and Adjustment
    7.4 Gross Error Detection of Point Cloud Data
    7.4.1 Definitions and Sources of Gross Errors
    7.4.2 Gross Error Detection and Elimination
    7.5 Point Cloud Classification and Target Extraction of Airborne LiDAR
    7.5.1 Overview
    7.5.2 The supervised classification process and supervised accuracy analysis of airborne LiDAR point cloud
    7.5.3 Feature extraction in classification process
    7.6 Summary

    8. Data Analysis and Feature Extraction of Spaceborne LiDAR
    8.1 Introduction of GLAS
    8.1.1 Data introduction
    8.1.2 GLAS introduction
    8.1.3 Data Acquisition and Data Types
    8.2 Processing of Waveform Data and Parameter Extraction of GLAS
    8.2.1 Processing of waveform data
    8.2.2 Waveform parameter extraction
    8.3 Data Application of GLAS
    8.3.1 Estimation of tree height with GLAS data at large regional scale
    8.3.2 Estimation of biomass with GLAS data at large regional scale
    8.4 Summary

    9. Forest Structural Parameters Extraction
    9.1 community-level structural parameters
    9.1.1 Communities’ vertical structural profiles
    9.1.2 Communities’ structural parameters and layered structure
    9.2 The retrieval of forest structure parameters at the individual tree level
    9.2.1 Individual tree segmentation
    9.2.2 Batch extraction of forest parameters
    9.3 Summary

    10. Ecosystem Function Parameters Inversion and Large-scale Simulation
    10.1 Canopy cover and canopy closure
    10.2 Leaf area index
    10.2.1 Theoretical basis
    10.2.2 LAI extraction from TLS data
    10.2.3 LAI extraction from ALS data
    10.2.4 LAI extraction from spaceborne LiDAR data
    10.3 Growing stock and biomass
    10.3.1 Growing stock and biomass estimation from large spot LiDAR data
    10.3.2 Growing stock and biomass estimation from small spot LiDAR data
    10.4 Summary

    11. Applications of LiDAR in dynamic monitoring of forest ecosystem
    11.1 forest dynamic monitoring
    11.1.1 forest dynamic monitoring based on LiDAR
    11.1.2 Construction of forest growth model based on LiDAR
    11.2 Forest fire monitoring and fire severity assessment
    11.2.1 Real-time monitoring of forest fires
    11.2.2 forest fire severity assessment based on LiDAR
    11.2.3 Forest fuel loads estimation based on LiDAR
    11.3 Forest management measures implication scope detection
    11.4 Chapter summary

    12. Applications of LiDAR technology in forest biodiversity, hydrology, and ecological models
    12.1 Applications of LiDAR in biodiversity research
    12.1.1 Research progress of remote sensing on biodiversity
    12.1.2 Case studies of applications of LiDAR in research of biodiversity
    12.1.3 Future development prospects of applications of LiDAR in biodiversity monitoring network
    12.2 Applications of LiDAR in ecohydrology research
    12.3 Applications of LiDAR in the ecological model
    12.4 Chapter summary

    13. 3D visualization and reconstruction of vegetation based on LiDAR technology
    13.1 3D reconstruction of individual tree
    13.2 3D visual simulation of landscape
    13.3 Application of three-dimensional vegetation reconstruction
    13.4 Chapter summary

    14. Emerging and ecological application of the near-surface LiDAR platform
    14.1 Backpack LiDAR system
    14.1.1 Hardware compositions of backpack LiDAR system
    14.1.2 Working principles of backpack LiDAR system
    14.1.3 Data examples of backpack LiDAR system
    14.2 Mobile LiDAR system
    14.2.1 Hardware composition of the mobile LiDAR system
    14.2.2 Working principles of mobile LiDAR scanner
    14.2.3 Data examples of mobile LiDAR system
    14.3 UAV LiDAR system 14.3.1 Hardware compositions of UAV LiDAR system
    4.3.2 Working principles of UAV LiDAR system
    14.3.3 Data examples of UAV LiDAR system
    14.4 Ecological applications of near-surface LiDAR platform
    14.4.1 Applications in urban ecosystem
    14.4.2 Applications in wetland ecosystems
    14.4.3 Applications in grassland ecosystem
    14.4.4 Applications in farmland ecosystem
    14.5 Chapter summary

    15. Challenges and applications of LiDAR
    15.1 Technical prospect of LiDAR
    15.1.1 Sensor development and innovation
    15.1.2 Multi-source heterogeneous data fusion
    15.1.3 Opportunities and challenges of LiDAR in big data era
    15.2 Application prospect of LiDAR in forest ecosystem
    15.3 Chapter summary

Product details

  • No. of pages: 450
  • Language: English
  • Copyright: © Academic Press 2023
  • Published: March 1, 2023
  • Imprint: Academic Press
  • Paperback ISBN: 9780128238943

About the Authors

Qinghua Guo

Dr. Qinghua Guo is currently a professor in Peking University, and serves as the director of the Institute of Remote Sensing & Geographical Information System, Peking University. He received the B.S. and M.S. degrees in Peking University, and the Ph.D degrees in University of California Berkeley. His recent research interests lie in developing near-surface (e.g., backpack, UAV and mobile) Lidar hardware and data processing software systems and combining them with airborne and spaceborne remote sensing data to map vegetation attributes (e.g., tree height, LAI, AGB, vegetation type) from individual plant scale to national and global scales. So far, he has published over 160 peer-reviewed papers.

Affiliations and Expertise

Professor in Peking University, and serves as the director of the Institute of Remote Sensing & Geographical Information System, Peking University

Yanjun Su

Dr. Yanjun Su is a professor in the Institute of Botany, Chinese Academy of Sciences. He received a B.E. degree from the China University of Geosciences (Beijing) in 2009, a M.S. degree from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and a Ph.D. degree from the University of California Merced in 2017. His research interests lie in using lidar to quantify vegetation structures and combining lidar-derived vegetation structures with other remote sensing techniques to understand how human activities and global climate change influence terrestrial ecosystems. So far, he has published over 70 peer-reviewed papers, and has received several academic awards, such as the “William A. Fisher Memorial Scholarship” from the American Society of Photogrammetry and Remote Sensing.

Affiliations and Expertise

Professor in the Institute of Botany, Chinese Academy of Sciences, Beijing, China

Tianyu Hu

Dr. Tianyu Hu is an associate professor in the Institute of Botany, Chinese Academy of Sciences. He received a B.S. degree in ecology from China Agriculture University, Beijing, China, in 2008, and a Ph.D. degree from the Institute of Botany, Chinese Academy of Sciences, Beijing, in 2014. His research focuses on using light detection and ranging (LiDAR) technology and dynamic global vegetation model to understand forest ecosystem, especially in forest structure, function, and biodiversity. Currently, He has published more than 30 peer-reviewed journal papers in the ecology and remote sensing, including Global Biogeochemical Cycles, Forest Ecology and Management, Remote Sensing of Environment, International Journal of Applied Earth Observations and Geoinformation and Remote Sensing etc

Affiliations and Expertise

Associate professor in the Institute of Botany, Chinese Academy of Sciences, Beijing, China

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