About the Book
Ideal for both undergraduate and graduate students in the fields of geography, forestry, ecology, geographic information science, remote sensing, and photogrammetric engineering, LiDAR Remote Sensing and Applications expertly joins LiDAR principles, data processing basics, applications, and hands-on practices in one comprehensive source.
The LiDAR data within this book is collected from 27 areas in the United States, Brazil, Canada, Ghana, and Haiti and includes 183 figures created to introduce the concepts, methods, and applications in a clear context. It provides 11 step-by-step projects predominately based on Esri’s ArcGIS software to support seamless integration of LiDAR products and other GIS data. The first six projects are for basic LiDAR data visualization and processing and the other five cover more advanced topics: from mapping gaps in mangrove forests in Everglades National Park, Florida to generating trend surfaces for rock layers in Raplee Ridge, Utah.
Features
Offers a comprehensive overview of LiDAR technology with numerous applications in geography, forestry and earth science
Gives necessary theoretical foundations from all pertinent subject matter areas
Uses case studies and best practices to point readers to tools and resources
Provides a synthesis of ongoing research in the area of LiDAR remote sensing technology
Includes carefully selected illustrations and data from the authors' research projects
Before every project in the book, a link is provided for users to download data
Table of Contents:
Brief Overview of Remote Sensing. Principles of LiDAR Remote Sensing. Basics of LiDAR Data Processing. LiDAR for Forest Applications. LiDAR for Urban Applications. LiDAR for Geoscience Applications.
About the Author :
Dr. Pinliang Dong is a Professor in the Department of Geography and the Environment, University of North Texas (UNT), Denton, TX, USA. He received his B.Sc. in geology from Peking University, China in 1987, M.Sc. in cartography and remote sensing from the Institute of Remote Sensing Applications, Chinese Academy of Sciences in 1990, and Ph.D. in geology from the University of New Brunswick, Canada in 2003. Before joining UNT in 2004, he worked as a Senior GIS Analyst/Programmer at Titan Corporation in California (USA) and Okinawa (Japan), and a Staff Associate/GIS Specialist at the Center for International Earth Science Information Network (CIESIN), Columbia University. His research interests include remote sensing, geographic information systems (GIS), digital image analysis, and LiDAR applications in forestry, urban studies, and geosciences. He has taught Intermediate GIS, Advanced GIS, Advanced GIS Programming, Remote Sensing, Special Topics in GIS: LiDAR Applications, and China Field School; mentored over 30 Master’s and doctoral students and two post-doctoral fellows; and hosted over 15 international visiting scholars. He is a member of the American Association of Geographers (AAG), American Geophysical Union (AGU), and International Society for Digital Earth (ISDE).
Dr. Qi Chen is a Professor in the Department of Geography at the University of Hawaii at Mānoa, Honolulu, Hawaii, USA. He received his B.Sc. and M.Sc. in Geography in 1998 and 2001, respectively, from Nanjing University, China, and Ph.D. in Environmental Sciences, Policy, and Management from the University of California, Berkeley, USA in 2007. He joined the University of Hawaii at Mānoa as a tenure-track Assistant Professor in 2007, was tenured and promoted to Associate Professor in 2012, and was promoted to Professor in 2017. His early interest and research in LiDAR remote sensing during his Ph.D. study were to develop effective methods for LiDAR data processing and information extraction, including airborne LiDAR point cloud filtering, digital terrain model generation, and individual tree mapping. His research in recent years has expanded to satellite LiDAR, terrestrial LiDAR, and the use of LiDAR for extracting various vegetation attributes for applications such as wildfire mapping and hazard analysis, biodiversity and habitat analysis, and biomass mapping and estimation. His overall interest in LiDAR remote sensing is to improve the methods of information extraction from LiDAR and to promote the use of LiDAR for assisting environmental management and decision making. He is an advisor to many Master’s and doctoral students, postdocs, and has hosted many international students and professors for studying LiDAR in his research lab. He has also given multiple tutorial workshops on LiDAR remote sensing in international conferences organized by professional societies.
Review :
"LiDAR is an incredibly useful data and technology that has been making positive impacts in many areas of society, including natural hazard mitigation, city planning, and many other sectors. Teaching with and learning about LiDAR technology has been challenging for instructors and GIS practitioners because of the lack of useful and practical activities, data sets, and projects. Dong and Chen’s LiDAR Remote Sensing and Applications book will help fill this gap because their book includes a set of hands-on projects with step-by-step instructions, with accompanying very useful data sets on a variety of themes and scales."
— Joseph J. Kerski, Esri, Broomfield, Colorado, USA
"LiDAR Remote Sensing and Applications is an indispensable reference for those working with LiDAR data. Combining detailed background information, a comprehensive review of the literature, and a clear explanation of typical analytical approaches, the book focuses in particular on forestry, urban and geoscience applications. A key strength of the volume is the inclusion of tutorials and the associated data for 11 projects, allowing the reader to explore the applications presented and thus truly get to grips with the subject matter."
—Timothy Warner, West Virginia University, USA
"This is a good book for those who would like to understand the applications of Lidar without going into the technical details of Lidar technology, data pre-processing and processing." —Arup Dasgupta in Geospatial World
