Multi-Source Remote Sensing for Digital Terrain Modeling and Analysis
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing for Geospatial Science".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 355
Editors
Interests: terrain modeling; terrain analysis; virtual geographic environment
Special Issues, Collections and Topics in MDPI journals
Interests: terrain modeling; terrain analysis; point cloud; GeoAI
Interests: multisource remote sensing data fusion; water environment monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Digital terrain models (DTMs) and digital elevation models (DEMs) are fundamental geospatial data that play a crucial role in geomorphology, hydrology, ecology, disaster management, and urban planning. With the rapid development of Earth observation technology, remote sensing has become the main means of obtaining high-resolution terrain data. However, terrain modeling that relies on a single data source often faces inherent limitations. Therefore, integrating multi-source remote sensing data, such as spaceborne/airborne LiDAR, synthetic aperture radar, multispectral images, and unmanned aerial vehicle photogrammetry, has become an important paradigm. Multi-source data fusion makes use of the complementary advantages of different sensors and significantly improves the accuracy, spatial resolution, and reliability of digital terrain modeling, especially in complex environments such as dense forests, steep terrain, and urban landscapes.
Meanwhile, DTM/DEM-based terrain analysis is a benchmark method in hydrological simulation, geological hazard warning, ecological protection, and infrastructure development. With the rapid development of geographic information systems (GISs) and spatial computing technology, automatic and fine-grained extraction of terrain features has become the norm. However, terrain analysis that relies on a single scale or traditional empirical algorithms often faces inherent limitations, becoming a major bottleneck in computational efficiency and error propagation when dealing with large amounts of high-resolution data or complex surface morphology. Therefore, combining multi-scale spatial algorithms with artificial intelligence, especially deep learning, has become an important paradigm.
This Special Issue aims to collect original research articles and comprehensive reviews, focusing on the latest advances in methods, innovative algorithms, and various applications related to multi-source remote sensing for digital terrain modeling and analysis.
Potential themes include, but are not limited to the following:
- Fusion of multi-source remote sensing data (LiDAR, SAR, optical, unmanned aerial vehicles, etc.) to generate high-precision DEM/DTM;
- Machine learning and deep learning algorithms in terrain modeling and feature extraction;
- Advanced point cloud processing and ground filtering algorithms in complex environments;
- Quality assessment, error modeling, and uncertainty analysis of multi-source terrain data;
- Digital terrain analysis and quantitative topographic measurement;
- Super-resolution reconstruction and spatial downscaling of elevation models;
- Application of multi-source terrain models in hydrology, glaciology, ecology, and natural disaster assessment.
Dr. Wen Dai
Prof. Dr. Chuanfa Chen
Dr. Linwei Yue
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- digital terrain models (DTMs)
- digital elevation models (DEMs)
- terrain analysis
- multi-source remote sensing data
- geomorphometry
- terrain modeling
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
