Special Issue "Applying Earth Surface Monitoring to Investigate Climate and Land Change Interactions"
Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 19101
Interests: land use; land cover; remote sensing; image classification; change detection; cloud computing
Monitoring change across the Earth’s surface has evolved considerably over the past five decades since civilian Earth Observation (EO) satellites were first launched into orbit. Continual technological advances and access to free satellite imagery have increased our ability to map and monitor both abrupt and subtle land surface changes over large collections of images. Extracting information on the surface changes with high spatial and temporal resolution is now being applied at national and global scales like never before. In many cases, large-scale efforts are aided by cloud-based EO processing that circumvents storage and processing constraints and new methodological approaches (including machine learning) that produce higher confidence land change estimates.
The promise of higher-quality mapping provides innumerable benefits to applications reliant on such data, from analyses of driving forces to empirically-driven projections of land change. Increasingly, land change monitoring efforts are being coupled with climate records in an effort to better understand how climate changes and land changes interact over short and long time frames. Investigating interactions between land change and climate is critical for reconstructing past landscapes under different climate conditions and projecting future land changes under different climate scenarios.
In this Special Issue, we welcome contributions that further advance EOS land change monitoring but have a greater interest in contributions that investigate cause–effect interactions between land change (detected by EOS) and climate. We request submissions on the following topics:
- New machine/deep learning algorithms for multi-temporal EOS analysis;
- Monthly-to-annual scale monitoring using cloud computing;
- Innovative applications in land change topics, including drought monitoring, vegetation phenology, post-fire vegetation recovery, etc.;
- Improvements in detecting and analyzing subtle changes using EOS;
- Disentangling the role of climate on land change in complex systems;
- Forcings and feedbacks between climate and land change over space and time;
- Novel trend analyses across dense time series of climate and land cover change information;
- Surface change hindcasting or forecasting informed by established climate-land change relationships.
Mr. Christopher Soulard
Dr. Miguel Villarreal
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.
- landscape change detection
- time series
- climate sensitivity
- climate projections
- machine learning
- cloud computing