Beschreibung
This book guides its audiencewhich can range from novice users to experts though a 55-chapter tour of Google Earth Engine. A sequenced and diverse set of lab materials, this is the product of more than a year of effort from more than a hundred individuals, collecting new exercises from professors, undergraduates, masters students, PhD students, postdocs, and independent consultants. Cloud Based Remote Sensing with Google Earth Engine is broadly organized into two halves. The first half, Fundamentals, is a set of 31 labs designed to take the reader from being a complete Earth Engine novice to being a quite advanced user. The second half, Applications, presents a tour of the world of Earth Engine across 24 chapters, showing how it is used in a very wide variety of settings that rely on remote-sensing data This is an open access book.
Autorenporträt
Jeffrey Cardille An Associate Professor at McGill University, Jeff is trained in landscape ecology, computer science, and remote sensing / GIS. A very early adopter of Earth Engine, his research focuses on ingesting multiple data sources to improve land-cover classifications, by smoothing inconsistencies through time and accumulating evidence of land cover change and stability. Through the years, one of the challenges in training new graduates and undergraduates has been finding training materials that both teach fundamentals and present interesting applications of using satellites for cutting-edge environmental science. Jeff hopes that this effort will appeal to individuals working on their own to understand this powerful technology. He has a Bachelors Degree in Applied Mathematics and Computer Science from Carnegie-Mellon University, a Master of Science in Operations Research from the Georgia Institute of Technology, and a PhD in Environmental Monitoring from the University of Wisconsin-Madison. Nick Clinton Nick is on the Earth Engine developer relations team. He received a bachelors, masters and PhD from the Department of Environmental Science, Policy and Management at UC Berkeley. From 2008-2011, Nick worked in the Airborne Sensor Facility of NASA Ames Research Center, producing science quality calibrated imagery and supporting sensor maintenance for thermal, multispectral and hyperspectral imagers. From 2012-2015, he was on the faculty of the Center for Earth System Science at Tsinghua University, in Beijing, China. He joined Google in 2015. Morgan A. Crowley Morgan is a Forest Fire Research Scientist with NRCan at the Great Lakes Forestry Centre - Canadian Forest Service and the Tier 2 scientific lead of the WildFireSat mission that will be launched in 2028. She recently completed her Ph.D. at McGill University, where her research explored novel applications of fire monitoring using Google Earth Engine and multi-sensor satellite data fusion. Morgan has been recognized for her research and outreach using Google Earth Engine, including as a 2022-2023 Google Developer Expert, a Geospatial Rising Star by Geospatial World Media, and a leader in ML4EO by Radiant Earth Foundation. She is also the co-director of the Ladies of Landsat organization, which works to increase support for underrepresented scientists in the field of remote sensing. David Saah A Professor at the University of San Francisco, David is an environmental scientist with expertise in a number of areas including: landscape ecology, ecosystem ecology, hydrology, geomorphology, ecosystem modeling, natural hazard modeling, remote sensing, geographic information systems (GIS) and geospatial analysis. His academic research uses integrated geospatial science for multi-scale mapping, monitoring and modeling of environmental spatial heterogeneity, particularly in riparian, savanna, and forest ecosystems. Dr. Saahs long-running work with the NASA SERVIR project has focused on outreach to developing nations, working to build knowledge infrastructure to solve pressing environmental problems.
Herstellerkennzeichnung:
Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE
E-Mail: juergen.hartmann@springer.com
