Articles | Volume 2, issue 2
https://doi.org/10.5194/esurf-2-403-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/esurf-2-403-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 Jul 2014
Research article |
| 25 Jul 2014
A geomorphology-based approach for digital elevation model fusion – case study in Danang city, Vietnam
T. A. Tran
Graduate School for Creative Cities, Osaka City University, Osaka, Japan
V. Raghavan
Graduate School for Creative Cities, Osaka City University, Osaka, Japan
S. Masumoto
Graduate School of Science, Osaka City University, Osaka, Japan
P. Vinayaraj
Graduate School for Creative Cities, Osaka City University, Osaka, Japan
G. Yonezawa
Graduate School for Creative Cities, Osaka City University, Osaka, Japan
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Cited
22 citations as recorded by crossref.
- Mekong delta much lower than previously assumed in sea-level rise impact assessments P. Minderhoud et al. https://doi.org/10.1038/s41467-019-11602-1
- Comparison of ASTER GDEM3, SRTM3, NASADEM, TanDEM-X90, AW3D30, and ALOS PALSAR data with TanDEM-X12: a case study of Tagragra of Akka inlier, Moroccan Anti-Atlas Z. Adiri et al. https://doi.org/10.1007/s12517-022-10885-x
- Exposure of manufacturing firms to future sea level rise in Ho Chi Minh City, Vietnam R. Leitold & J. Diez https://doi.org/10.1080/17445647.2018.1548385
- Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features R. Gui et al. https://doi.org/10.3390/rs16193567
- Fusion of public DEMs based on sparse representation and adaptive regularization variation model L. Guan et al. https://doi.org/10.1016/j.isprsjprs.2020.09.005
- A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data H. Pham et al. https://doi.org/10.1016/j.rse.2018.03.026
- A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications C. Okolie & J. Smit https://doi.org/10.1016/j.isprsjprs.2022.03.016
- Fusion of multiple photogrammetric DEMs for large-scale lunar topographic mapping R. Huang et al. https://doi.org/10.1016/j.asr.2025.01.001
- Adaptive variational fusion of multi-source lunar digital elevation models based on curvature regularization S. Qiu et al. https://doi.org/10.1016/j.cageo.2026.106107
- A new strategy combined HASM and classical interpolation methods for DEM construction in areas without sufficient terrain data J. Wang et al. https://doi.org/10.1007/s11629-020-6655-5
- МЕТОДИКА СОЗДАНИЯ ЦИФРОВОЙ МОДЕЛИ РЕЛЬЕФА УДМУРТИИ . Караваев https://doi.org/10.35634/2412-9518-2026-36-1-55-69
- Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China X. Yang et al. https://doi.org/10.3390/ijgi7040145
- A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs L. Jiang et al. https://doi.org/10.3390/w12051369
- Global Digital Elevation Model Comparison Criteria: An Evident Need to Consider Their Application C. López-Vázquez & F. Ariza-López https://doi.org/10.3390/ijgi12080337
- Improvement of Digital Elevation Model (DEM) using data fusion technique for oil palm replanting phase N. Muhadi et al. https://doi.org/10.1080/19479832.2018.1504826
- A GIS-based Approach for Flood Vulnerability Assessment in Hoa Vang District, Danang City, Vietnam T. An et al. https://doi.org/10.1088/1755-1315/652/1/012003
- Enhancing digital elevation models for hydraulic modelling using flood frequency detection G. Ettritch et al. https://doi.org/10.1016/j.rse.2018.08.029
- Harnessing geoinformatics and AHP techniques to assess the groundwater potential zones in Uttar Pradesh, India S. Chandra & A. Singh https://doi.org/10.1007/s12518-025-00640-8
- Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30 S. Zhao et al. https://doi.org/10.3390/ijgi11030207
- A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges N. Mölg et al. https://doi.org/10.5194/essd-10-1807-2018
- Fusing Digital Elevation Models to Improve Hydrological Interpretations S. Furze et al. https://doi.org/10.4236/jgis.2017.95035
- Rapid enhanced-DEM using Google Earth Engine, machine learning, weighted and spatial interpolation techniques W. Kandil et al. https://doi.org/10.1108/WJE-05-2024-0315
22 citations as recorded by crossref.
- Mekong delta much lower than previously assumed in sea-level rise impact assessments P. Minderhoud et al. https://doi.org/10.1038/s41467-019-11602-1
- Comparison of ASTER GDEM3, SRTM3, NASADEM, TanDEM-X90, AW3D30, and ALOS PALSAR data with TanDEM-X12: a case study of Tagragra of Akka inlier, Moroccan Anti-Atlas Z. Adiri et al. https://doi.org/10.1007/s12517-022-10885-x
- Exposure of manufacturing firms to future sea level rise in Ho Chi Minh City, Vietnam R. Leitold & J. Diez https://doi.org/10.1080/17445647.2018.1548385
- Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features R. Gui et al. https://doi.org/10.3390/rs16193567
- Fusion of public DEMs based on sparse representation and adaptive regularization variation model L. Guan et al. https://doi.org/10.1016/j.isprsjprs.2020.09.005
- A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data H. Pham et al. https://doi.org/10.1016/j.rse.2018.03.026
- A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications C. Okolie & J. Smit https://doi.org/10.1016/j.isprsjprs.2022.03.016
- Fusion of multiple photogrammetric DEMs for large-scale lunar topographic mapping R. Huang et al. https://doi.org/10.1016/j.asr.2025.01.001
- Adaptive variational fusion of multi-source lunar digital elevation models based on curvature regularization S. Qiu et al. https://doi.org/10.1016/j.cageo.2026.106107
- A new strategy combined HASM and classical interpolation methods for DEM construction in areas without sufficient terrain data J. Wang et al. https://doi.org/10.1007/s11629-020-6655-5
- МЕТОДИКА СОЗДАНИЯ ЦИФРОВОЙ МОДЕЛИ РЕЛЬЕФА УДМУРТИИ . Караваев https://doi.org/10.35634/2412-9518-2026-36-1-55-69
- Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China X. Yang et al. https://doi.org/10.3390/ijgi7040145
- A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs L. Jiang et al. https://doi.org/10.3390/w12051369
- Global Digital Elevation Model Comparison Criteria: An Evident Need to Consider Their Application C. López-Vázquez & F. Ariza-López https://doi.org/10.3390/ijgi12080337
- Improvement of Digital Elevation Model (DEM) using data fusion technique for oil palm replanting phase N. Muhadi et al. https://doi.org/10.1080/19479832.2018.1504826
- A GIS-based Approach for Flood Vulnerability Assessment in Hoa Vang District, Danang City, Vietnam T. An et al. https://doi.org/10.1088/1755-1315/652/1/012003
- Enhancing digital elevation models for hydraulic modelling using flood frequency detection G. Ettritch et al. https://doi.org/10.1016/j.rse.2018.08.029
- Harnessing geoinformatics and AHP techniques to assess the groundwater potential zones in Uttar Pradesh, India S. Chandra & A. Singh https://doi.org/10.1007/s12518-025-00640-8
- Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30 S. Zhao et al. https://doi.org/10.3390/ijgi11030207
- A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges N. Mölg et al. https://doi.org/10.5194/essd-10-1807-2018
- Fusing Digital Elevation Models to Improve Hydrological Interpretations S. Furze et al. https://doi.org/10.4236/jgis.2017.95035
- Rapid enhanced-DEM using Google Earth Engine, machine learning, weighted and spatial interpolation techniques W. Kandil et al. https://doi.org/10.1108/WJE-05-2024-0315
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