Earth Observation Using Python
Earth Observation Using Python

Author: Rebekah B. Esmaili

Publisher: John Wiley & Sons

Published: 2021-08-24

Total Pages: 308

ISBN-13: 1119606888

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Learn basic Python programming to create functional and effective visualizations from earth observation satellite data sets Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks. Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research. Gain Python fluency using real data and case studies Read and write common scientific data formats, like netCDF, HDF, and GRIB2 Create 3-dimensional maps of dust, fire, vegetation indices and more Learn to adjust satellite imagery resolution, apply quality control, and handle big files Develop useful workflows and learn to share code using version control Acquire skills using online interactive code available for all examples in the book The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more about this book from this Q&A with the Author

Earth Observation Using Python
Earth Observation Using Python

Author: Rebekah B. Esmaili

Publisher: John Wiley & Sons

Published: 2021-08-04

Total Pages: 308

ISBN-13: 1119606918

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Learn basic Python programming to create functional and effective visualizations from earth observation satellite data sets Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks. Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research. Gain Python fluency using real data and case studies Read and write common scientific data formats, like netCDF, HDF, and GRIB2 Create 3-dimensional maps of dust, fire, vegetation indices and more Learn to adjust satellite imagery resolution, apply quality control, and handle big files Develop useful workflows and learn to share code using version control Acquire skills using online interactive code available for all examples in the book The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more about this book from this Q&A with the Author

Open Source Geospatial Tools
Open Source Geospatial Tools

Author: Daniel McInerney

Publisher: Springer

Published: 2014-11-22

Total Pages: 370

ISBN-13: 3319018248

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This book focuses on the use of open source software for geospatial analysis. It demonstrates the effectiveness of the command line interface for handling both vector, raster and 3D geospatial data. Appropriate open-source tools for data processing are clearly explained and discusses how they can be used to solve everyday tasks. A series of fully worked case studies are presented including vector spatial analysis, remote sensing data analysis, landcover classification and LiDAR processing. A hands-on introduction to the application programming interface (API) of GDAL/OGR in Python/C++ is provided for readers who want to extend existing tools and/or develop their own software.

Earth Observation Open Science and Innovation
Earth Observation Open Science and Innovation

Author: Christoph Aubrecht

Publisher:

Published: 2020-10-08

Total Pages: 326

ISBN-13: 9781013269363

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Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites.This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Hyperspectral Remote Sensing
Hyperspectral Remote Sensing

Author: Prem Chandra Pandey

Publisher: Elsevier

Published: 2020-08-05

Total Pages: 508

ISBN-13: 0081028954

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Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies

Google Earth Engine Applications
Google Earth Engine Applications

Author: Lalit Kumar

Publisher: MDPI

Published: 2019-04-23

Total Pages: 420

ISBN-13: 3038978841

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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

ZeroMQ
ZeroMQ

Author: Pieter Hintjens

Publisher: "O'Reilly Media, Inc."

Published: 2013-03-15

Total Pages: 516

ISBN-13: 1449334067

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Discover why ZeroMQ is rapidly becoming the programming framework of choice for exchanging messages between systems. With this practical, fast-paced guide, you’ll learn how to use this lightweight and highly flexible networking tool for message passing in clusters, the cloud, and other multi-system environments. Created by ZeroMQ maintainer Pieter Hintjens and volunteers from the framework’s community, this book takes you on a tour of different real-world applications, with extended examples in C. You’ll learn how to use specific ZeroMQ programming techniques, build multithreaded applications, and create your own messaging architectures.

Remote Sensing Time Series
Remote Sensing Time Series

Author: Claudia Kuenzer

Publisher: Springer

Published: 2015-04-28

Total Pages: 458

ISBN-13: 3319159674

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This volume comprises an outstanding variety of chapters on Earth Observation based time series analyses, undertaken to reveal past and current land surface dynamics for large areas. What exactly are time series of Earth Observation data? Which sensors are available to generate real time series? How can they be processed to reveal their valuable hidden information? Which challenges are encountered on the way and which pre-processing is needed? And last but not least: which processes can be observed? How are large regions of our planet changing over time and which dynamics and trends are visible? These and many other questions are answered within this book “Remote Sensing Time Series Analyses – Revealing Land Surface Dynamics”. Internationally renowned experts from Europe, the USA and China present their exciting findings based on the exploitation of satellite data archives from well-known sensors such as AVHRR, MODIS, Landsat, ENVISAT, ERS and METOP amongst others. Selected review and methods chapters provide a good overview over time series processing and the recent advances in the optical and radar domain. A fine selection of application chapters addresses multi-class land cover and land use change at national to continental scale, the derivation of patterns of vegetation phenology, biomass assessments, investigations on snow cover duration and recent dynamics, as well as urban sprawl observed over time.

Remote Sensing and GIS for Ecologists
Remote Sensing and GIS for Ecologists

Author: Martin Wegmann

Publisher: Pelagic Publishing Ltd

Published: 2016-02-08

Total Pages: 410

ISBN-13: 1784270245

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This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning.

Deep Learning for the Earth Sciences
Deep Learning for the Earth Sciences

Author: Gustau Camps-Valls

Publisher: John Wiley & Sons

Published: 2021-08-18

Total Pages: 436

ISBN-13: 1119646162

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DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.