Thermal Infrared Remote Sensing: Principles and Applications

This book systematically and comprehensively introduces concepts, theories, principles, and applications of Thermal Infrared (TIR) imaging spectral techniques and TIR remote sensing. It introduces state-of the-art techniques and methods applied hydrology, biomass burning, urban thermal environment and other areas of interest.

Ruiliang Pu was a Professor at the School of Geosciences at the University of South Florida (USF). He received his MSc in Forest Management from Nanjing Forestry University, China in 1985, and earned a Ph.D in Cartography & Geographic Information System in 2000, at the University of California at Berkeley. He has published more than 138 journal papers, book chapters, in English and more than 26 journal articles in Chinese. In addition, he is the author of the book ＂Hyperspectral Remote Sensing: Fundamentals and Practices＂ (2017, CRC Press, ), which was translated into Chinese and published in 2020 by Higher Education Press, Beijing, China. He has also co-written two books in Chinese with his Chinese colleagues. His publications have appeared in top- or higher-tier journals such as Remote Sensing of Environment, IEEE Transactions on Geoscience and Remote Sensing, Journal of Geophysical Research, Photogrammetric Engineering and Remote Sensing, and International Journal of Remote Sensing.

Qiuyan Yu is a Senior Scientist at Exponent Inc. in the Biological and Ecological Sciences practice. Before joining Exponent, she served as a Research Assistant Professor at New Mexico State University. She earned her Ph.D. in Geography and Environmental Science & Policy from the University of South Florida in 2018 under the mentorship of Dr. Ruiliang Pu and also holds an M.Sc. in Cartography and Geographic Information Systems from Beijing Normal University, China. Dr. Yu has led NASA-funded research projects, has served on NASA and NSF review panels, and has published more than 45 journal articles. She has contributed to over 100 industry-focused projects spanning from environmental science to machine learning and artificial intelligence-based modelling.