Special Issue "Geospatial Statistics and Spatial/Spatiotemporal Analysis in Remote Sensing"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 258
Interests: geostatistics; spatial statistics; spatial analysis and modeling; spatial data science and analytics; remote sensing; inverse problems; geographic information science
Interests: geographical information science; remote sensing; soil science; uncertainty analysis; geostatistics; spatial analysis; precision agriculture; spatial data quality
Interests: The main research interests of the research group reside in the development of stochastic methods that characterize the spatial and temporal variability inherent to natural systems, in particular related to the water cycle. We use numerical techniques using high-order, nonparametric statistics. These allow us to analyze complex datasets such as remote sensing data or the outputs of complex models (climate models or flow/transport models). The work pursued is at the frontier between Earth modeling and computer science, with a strong emphasis on stochastic models, training images and example-based modeling.
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In the last few decades, the availability of remotely sensed data has grown dramatically, spurring related products and services across multiple fields, including the atmospheric and environmental sciences, earth sciences, ecology, population, health and socio-economic studies, as well as archaeology and cultural heritage. In this data-rich epoch, there is a pressing need for methodological developments in the related fields of geospatial statistics and analysis, explicitly designed to account for spatiotemporal dependencies in multi-temporal data, to:
(a) enable processing, analysis and inference in extremely large datasets; so-called “big data”;
(b) support sense-making from spatiotemporal patterns;
(c) integrate data (directly or produced by inversion) from different sensors and in-situ measurements, possibly along with information increasingly furnished by people themselves, and
(d) accompany remotely sensed products with (spatially-distributed) measures of reliability or uncertainty to support risk-conscious decision-making in the face of uncertainty.
All the above can contribute to the better use of remote sensing for addressing global challenges, such as food shortages, climate change, infectious diseases, or vulnerability against natural and human-induced hazards, to name but a few.
This Special Issue aims to assemble the latest developments and best practices in geospatial statistics and the spatio-temporal analysis of remotely sensed data and relevant products for addressing some of the world’s greatest environmental and social challenges. The list of potential topics below is indicative of the research themes in which manuscripts are solicited; contributions on related topics are also welcome as long as they do not constitute mere applications of classical statistics to spatial and/or spatiotemporal data.
- Pattern Recognition/Understanding/Modelling
- Multi-temporal Remote Sensing
- Change Detection
- Data Integration/Fusion
- Spatial or Spatiotemporal Resolution Issues
- Big Geospatial Data Analytics
- Modern Classification Methods
- Machine Learning/Deep Learning
- Data assimilation
- Uncertainty Propagation
Prof. Phaedon Kyriakidis
Dr. Sytze De Bruin
Prof. Gregoire Mariethoz
Prof. Peter M. Atkinson
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Spatial statistics/geostatistics
- Spatial/spatiotemporal correlation
- Spatial support
- Change detection
- Uncertainty propagation
- Data integration/fusion
- Contextual classification
- Big geospatial data analytics
- Machine learning/deep learning