Reprint
Earth Observation Data Cubes
Edited by
March 2020
302 pages
- ISBN978-3-03928-092-6 (Paperback)
- ISBN978-3-03928-093-3 (PDF)
This book is a reprint of the Special Issue Earth Observation Data Cubes that was published in
Biology & Life Sciences
Business & Economics
Computer Science & Mathematics
Environmental & Earth Sciences
Medicine & Pharmacology
Public Health & Healthcare
Summary
Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10× in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/spectral resolutions). Traditional approaches to the acquisition, management, distribution, and analysis of EO data have limitations (e.g., data size, heterogeneity, and complexity) that impede their true information potential to be realized. Addressing these big data challenges requires a change of paradigm and a move away from local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. To tackle these issues, EO data cubes (EODC) are a new paradigm revolutionizing the way users can store, organize, manage, and analyze EO data. This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations to broaden the use of EO data to larger communities of users, support decision-makers with timely and actionable information converted into meaningful geophysical variables, and ultimately unlock the information power of EO data.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
topology based map algebra; data cubes; big data; map algebra; earth oberservation; GRASS GIS; earth observations; satellite imagery; R; data cubes; Sentinel-2; Sentinel-1; SAR; analysis ready data; ARD; interoperability; data cube; Earth observation; pyroSAR; data cube; image cube; image data cube; imagery; Landsat; Sentinel; earth observation; GIS; web services; web application; analysis; GIS; Open Data Cube; Earth Observations; interoperability; visualization; Sentinel; Analysis Ready Data; Sentinel-1; Synthetic Aperture Radar; Data Cube; dual-polarimetric decomposition; interferometric coherence; Digital Earth Australia; remote sensing; big Earth data; big EO data; information extraction; semantic enrichment; time-series; Open Data Cube; remote sensing; geospatial standards; landsat; sentinel; analysis ready data; dynamic data citation; subset; data curation; persistent identifier; data provenance; metadata; versioning; query store; data sharing; FAIR principles; big earth data; sustainable development goals; swiss DC; Armenian DC; Landsat; sentinel; analysis ready data; data discovery; metadata; knowledge base; graph data; intelligent semantic agents; data cube; optical remote sensing; snow cover; Gran Paradiso National Park; climate change; land cover classification; change; Digital Earth Australia; open data cube; Landsat; Australia; Open Data Cube; UN 2030 Agenda for Sustainable Development; UN System of Environmental Economic Accounting; Earth observation data; open science; reproducibility; earth observations; data cube; analysis ready data; remote sensing; satellite imagery