Remote Sensing Modeling and Applications to Wildland Fires

John J. Qu · William Sommers · Ruixin Yang · Allen Riebau · Menas Kafatos

12/2014 · Springer

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Scientists and managers alike need timely, cost-effective, and technically appropriate fire-related information to develop functional strategies for the diverse fire communities. "Remote Sensing Modeling and Applications to Wildland Fires" addresses wildland fire management needs by presenting discussions that link ecology and the physical sciences from local to regional levels, views on integrated decision support data for policy and decision makers, new technologies and techniques, and future challenges and how remote sensing might help to address them. While creating awareness of wildland fire management and rehabilitation issues, hands-on experience in applying remote sensing and simulation modeling is also shared. This book will be a useful reference work for researchers, practitioners and graduate students in the fields of fire science, remote sensing and modeling applications.

Professor John J. Qu works at the Department of Geography and GeoInformation Science at George Mason University (GMU), USA. He is the Founder and Director of the Environmental Science and Technology Center (ESTC) and EastFIRE Lab at GMU.

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Dr. John J. Qu is a faculty member of the ESGS program at the school of Computational Sciences and is Technical Director of EastFIRE Lab at George Mason University. He is also with NASA Goddard Space Flight Center to support the NPOESS Preparatory Project (NPP) mission. His major research areas are satellite remote sensing, Earth systems sciences, fire science and GIS applications.

Dr. Menas Kafatos is Dean of the school of Computational Sciences (SCS), Director of the Center for Earth Observing and Space Research (CEOSR) and Professor of Interdisciplinary Science at George Mason University. He has published numerous books, and articles on computational science, astrophysics, Earth systems science, general relativity and the foundations of quantum theory.