Special Issue "Geospatial Statistics and Spatiotemporal Analysis Based on Remote Sensing Imagery"
Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 6050
Interests: spatial analysis; geostatistics; remote sensing processing methods; GIS; interoperability; high-performance computing
Interests: satellite image time series analysis; machine learning; semantics in remote sensing
Special Issues, Collections and Topics in MDPI journals
The scientific community has become increasingly interested in using earth observation system (EOS) satellites. Remotely sensed data is also increasingly more accessible across multiple scientific domains. The Earth's surface and the corresponding variables captured by remotely sensed images have distinct spatial properties. Geospatial statistics provide methods for quantification and analysis of these spatial properties and their spatial dependencies. A long historical archive of remote sensing data is within the reach of scientists providing huge temporal datasets for monitoring, estimating, modeling, and understanding the dynamics of many of the Earth's surface phenomena. With the aim of increasing the knowledge of spatiotemporal properties and methodologies in remote sensing disciplines, the list of potential topics below is indicative of the research themes in which manuscripts are solicited:
- Methods of scaling geospatial remote sensing data
- Methods for coherent multisensor time series of remote sensing data
- Uncertainty spatialization of remote sensing data
- Analysis of geospatial properties: anisotropy, heterogeneity, fragmentation, autocorrelation, etc. of large remote sensing time series
- Innovative analysis of cycle and phenology spatiotemporal patterns of remote sensing time series
- Changes on autocorrelation patterns of large time series
- Remote sensing imagery time series harmonization in geostatistical analysis
- Statistical and spatial quality indicators for remote sensing imagery
- Products composite (i.e., vegetation indexes) and multitemporal data fusion methods with preserving geospatial properties.
- Geostatistical methodologies for filling time/spatial gaps or artifacts in remote sensing imagery
- New approaches for spatial, statistical and spatiotemporal resolution issues on remote sensing imagery
- Optimal sampling of in-situ measurements for calibration or validation of remote sensing variables
Dr. Lluís Pesquer Mayos
Dr. Mariana Belgiu
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.
- Spatiotemporal patterns
- Geostatistical remote sensing
- Spatialized uncertainty
- Multisource data fusion
- Time series coherence
- Optimal sampling
- Analysis of geospatial properties