The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground Regions
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 1284
Special Issue Editors
Interests: permafrost; interactions in snow–vegetation–frozen ground
Interests: permafrost; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: permafrost; periglacial geomorphology; landscape change detection; modeling ground temperature
Interests: remote sensing; permafrost; deep learning for computer vision
Special Issue Information
Dear Colleagues,
Frozen ground is an important component of the cryosphere. Permafrost regions underlie approximately 24% of the exposed land surface of the Northern Hemisphere, and seasonally frozen ground (SFG) regions occupy 57%. Such a vast area extent of frozen ground plays a significant role in the local to global atmospheric circulation, climate, hydrology, and terrestrial ecosystems by affecting the energy, water, and carbon cycles. Due to significant global warming, frozen ground and its environment have experienced great changes, e.g., the freeze–thaw process, the area extent, ground temperature, landform, vegetation, and others. Thus, it is necessary to study this topic. Excluding classical field observations, remote sensing, machine learning, and deep learning methods are popular in the field of frozen ground research.
This Special Issue is aimed at studies covering different applications of remote sensing, machine learning, and deep learning in the frozen ground, including seasonally frozen ground and permafrost. Topics may cover anything from the related frozen ground in the point-regional-hemisphere scales. Hence, algorithms, applications, and simulations in frozen ground studies, among other issues, are welcome. Articles may address, but are not limited to, the following topics:
- Remote sensing in the freeze/thaw status;
- Seasonally frozen ground changes;
- Permafrost changes;
- Landform;
- The application or development of algorithms in the frozen ground study;
- Environment changes in the frozen ground regions.
Prof. Dr. Xiaoqing Peng
Prof. Dr. Dongliang Luo
Dr. Raul-David Șerban
Dr. Lingcao Huang
Prof. Dr. Yonghong Yi
Guest Editors
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.
Keywords
- frozen ground
- permafrost
- remote sensing
- machining learning
- deep learning
- InSAR
- carbon
