Big Earth Observation Data Analysis for Environment Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 9050
Special Issue Editors
2. Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Austria
Interests: time series analysis; pattern recognition; global resource extraction; environmental monitoring
Special Issue Information
Dear Colleagues,
Satellite Earth observation (EO) is the most comprehensive and timely source of data to address global environmental challenges. Despite the increasing availability of free and open EO data, environmental information on the continental or global scale has not yet been produced at the same speed. Several computational challenges related to big EO data handling and processing have been tackled recently. For example, today’s cloud processing infrastructures allow scaling data analysis more efficiently, and EO data cube technologies provide support for spatiotemporal data analysis, which was highly challenging to perform just a few years ago.
Big EO data analytics provide a unique opportunity to generate new information about and insights into the global environment. However, deriving environmental information with appropriate semantics from big EO data is still a challenge. This Special Issue aims at featuring innovative research that advances big EO data analysis for environmental monitoring. Applications may be related to the whole human Earth system, for example, biodiversity, forestry, agriculture, land-use changes, burning dynamics, and soil degradation. We invite papers including, but not limited to, the following research lines:
- Scalable methods for environmental monitoring;
- New insights about global human Earth system dynamics;
- Tools and systems that facilitate large-scale environmental analysis;
- Statistical/machine learning approaches to modeling global environmental phenomena;
- Large-scale environmental change modeling;
- Socioeconomic drivers of global environmental changes.
Dr. Victor Maus
Dr. Marius Appel
Guest Editors
Manuscript Submission Information
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Keywords
- Environmental impacts
- Satellite image time series
- Data cube
- Statistical learning
- Artificial intelligence
- Data mining
- Earth system dynamics
- Environmental monitoring
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