Special Issue "Geospatial Statistics and Spatial/Spatiotemporal Analysis in Remote Sensing"

Published on
January 7, 2019

A special issue on will be published in Remote Sensing, Deadline for manuscript submissions: 30 December 2019.

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:

  1. enable processing, analysis and inference in extremely large datasets; so-called “big data”;
  2. support sense-making from spatiotemporal patterns;
  3. integrate data (directly or produced by inversion) from different sensors and in-situ measurements, possibly along with information increasingly furnished by people themselves, and
  4. 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