Geospatial Statistics and Spatiotemporal Analysis Based on Remote Sensing Imagery

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

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