LiDAR Principles, Processing and Applications in Forest Ecology
Auteurs : Guo Qinghua, Su Yanjun, Hu Tianyu
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.
2. Working principle of LiDAR 2.1 Ranging principle of LiDAR
3. Field work flow and system error source of LiDAR
4. LiDAR data format
5. LiDAR data filtering and digital elevation model generation
6. Data Analysis and Feature Extraction of Terrestrial LiDAR
7. Data Analysis and Feature Extraction of Airborne LiDAR
8. Data Analysis and Feature Extraction of Spaceborne LiDAR
9. Forest Structural Parameters Extraction
10. Ecosystem Function Parameters Inversion and Large-scale Simulation
11. Applications of LiDAR in dynamic monitoring of forest ecosystem
12. Applications of LiDAR technology in forest biodiversity, hydrology, and ecological models
13. 3D visualization and reconstruction of vegetation based on LiDAR technology
14. Emerging and ecological application of the near-surface LiDAR platform
15. Challenges and applications of LiDAR
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.
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 stru
- 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
Date de parution : 03-2023
Ouvrage de 510 p.
15.2x22.8 cm
Thème de LiDAR Principles, Processing and Applications in Forest... :
Mots-clés :
Aboveground biomass; Accuracy; Airborne LiDAR; Applications in forest ecology; Backpack LiDAR; Canopy closure; Canopy cover; Classification of LiDAR; Data format; Data processing software; Data storage; Denoising; Digital elevation model; Error sources; Feature extraction; Fieldwork preparation; Filtering; Forest community; GEDI; Ground point; ICESat GLAS; ICESat-2 ATLAS; Interpolation; Leaf area index; LiDAR data acquisition; LiDAR equation; LiDAR manufacturers; Light detection and ranging (LiDAR); Mobile LiDAR; Multiplatform LiDAR systems; Point cloud; Point cloud classification; Point cloud preprocessing; Ranging principle; Registration; Resolving; Spaceborne LiDAR; Structural attribute; Terrestrial laser scanning; Terrestrial LiDAR; Tree segmentation; Tree species classification; Unmanned aerial vehicle LiDAR; Upscaling; Waveform; Waveform data