Special Issue "Spatial and Spatio-Temporal Statistics: Methods and Applications in Remote Sensing"
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13504
Interests: statistical methods for remote sensing; uncertainty quantification; spatial and spatio-temporal statistical modeling; analysis of massive data sets; climate model diagnosis using remote sensing observations
Interests: spatial and spatio-temporal statistics; Bayesian hierarchical modeling; statistical computing; uncertainty quantification; statistical learning; statistical modeling for remote sensing; climate; environmental sciences
Interests: environmental statistics; spatial statistics; remote sensing; environmental epidemiology
Special Issues, Collections and Topics in MDPI journals
Over the last 20 years, a trove of new statistical methods for remote sensing data have been developed and used to reveal new insights about the Earth’s geophysical processes, the impact of human activities on them, and their impacts on people’s lives. These processes exhibit spatial and spatio-temporal dependence, and consequently, so do the corresponding observed radiances and retrieved geophysical data products. However, multiple uncertainties affect acquisition and processing, and they must be accounted for both in the products themselves and in subsequent analyses. Exploiting inherent spatial and temporal dependence can mitigate the impacts of these uncertainties and lead to more accurate products, science conclusions, and more informed decisions.
This Special Issue serves as a compendium of recent work bringing modern spatial and spatio-temporal statistical methods to bear on the collection, generation, and analysis of remote sensing data products. We explore two broad themes here: methods and applications. In methods, we showcase spatial and spatio-temporal statistical tools created specifically for massive remote sensing data sets. All the modern methods we are aware of were developed in response to practical problems, and we ask that authors include the motivation for their work and show examples. Under applications, we come at the problem from the other direction: we seek contributions from remote sensing scientists and users who have incorporated spatial and spatio-temporal statistical methods into their work and have realized benefits. We are especially interested in applications where new scientific or societal insights are enabled by these techniques. Our goal is to bring the remote sensing community up to date on what modern statistical methods have to offer and to facilitate more collaboration between the remote sensing and statistics communities going forward.
Dr. Amy Braverman
Dr. Emily Kang
Dr. Meredith Franklin
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 2700 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
- spatio-temporal statistics
- data fusion
- uncertainty quantification
- spatial/temporal dependence
- gradients and trends
- bias and variability
- health effects