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Journal articles on the topic 'Digital elevation models'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

Nelson, E. James, and Norman L. Jones. "Reducing elevation roundoff errors in digital elevation models." Journal of Hydrology 169, no. 1-4 (1995): 37–49. http://dx.doi.org/10.1016/0022-1694(94)02671-w.

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2

Guth, Peter L., Adriaan Van Niekerk, Carlos H. Grohmann, et al. "Digital Elevation Models: Terminology and Definitions." Remote Sensing 13, no. 18 (2021): 3581. http://dx.doi.org/10.3390/rs13183581.

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Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.

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3

Krauß, T., and P. d'Angelo. "MORPHOLOGICAL FILLING OF DIGITAL ELEVATION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-4/W19 (September 7, 2012): 165–72. http://dx.doi.org/10.5194/isprsarchives-xxxviii-4-w19-165-2011.

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4

Zhou, Howard, Jie Sun, Greg Turk, and James M. Rehg. "Terrain Synthesis from Digital Elevation Models." IEEE Transactions on Visualization and Computer Graphics 13, no. 4 (2007): 834–48. http://dx.doi.org/10.1109/tvcg.2007.1027.

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5

Lohr, U. "Digital Elevation Models By Laser Scanning." Photogrammetric Record 16, no. 91 (1998): 105–9. http://dx.doi.org/10.1111/0031-868x.00117.

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6

Pfiffner, O. Adrian. "Digital elevation models in mountainous regions." Schriftenreihe der Deutschen Gesellschaft für Geowissenschaften 99 (June 12, 2024): 49–60. http://dx.doi.org/10.1127/sdgg/99/2024/49.

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7

Eakins, Barry W., Lisa A. Taylor, Kelly S. Carignan, and Maureen R. Kenny. "Advances in Coastal Digital Elevation Models." Eos, Transactions American Geophysical Union 92, no. 18 (2011): 149–50. http://dx.doi.org/10.1029/2011eo180001.

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8

Liu, XiaoHang, Hai Hu, and Peng Hu. "The “M” in digital elevation models." Cartography and Geographic Information Science 42, no. 3 (2015): 235–43. http://dx.doi.org/10.1080/15230406.2014.993711.

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9

Franklin, Steven E. "Geomorphometric processing of digital elevation models." Computers & Geosciences 13, no. 6 (1987): 603–9. http://dx.doi.org/10.1016/0098-3004(87)90030-6.

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10

Zhou, Qiming. "Relief shading using digital elevation models." Computers & Geosciences 18, no. 8 (1992): 1035–45. http://dx.doi.org/10.1016/0098-3004(92)90019-n.

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11

Pavlova, A. I. "Analysis of elevation interpolation methods for creating digital elevation models." Optoelectronics, Instrumentation and Data Processing 53, no. 2 (2017): 171–77. http://dx.doi.org/10.3103/s8756699017020108.

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12

Haala, Norbert, Heidi Hastedt, Kirsten Wolf, Camillo Ressl, and Sven Baltrusch. "Digital Photogrammetric Camera Evaluation – Generation of Digital Elevation Models." Photogrammetrie - Fernerkundung - Geoinformation 2010, no. 2 (2010): 99–115. http://dx.doi.org/10.1127/1432-8364/2010/0043.

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13

Erdede, Sevim Bilge, and Sebahattin Bektaş. "Examining The Interpolation Methods Used In Forming The Digital Elevation Models." Celal Bayar Üniversitesi Fen Bilimleri Dergisi 16, no. 2 (2020): 207–14. https://doi.org/10.18466/cbayarfbe.678176.

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Today, digital elevation models and digital terrain models are used in many areas, such as Geographical Information Systems (GIS), several engineering work and management of natural resources. Digital elevation models are a general source of data for terrain analysis and 3-D applications. The slope of the land, aspect, curvature, catchment area characteristics can be determined easily by the help of digital elevation models. Therefore, besides the accurate production of digital elevation models, its fast and economical production becomes a goal. Different accuracy digital elevation model can be produced by using different interpolation methods. In this study, the concepts of digital terrain model and digital elevation model are explained, interpolation methods used to generate digital elevation models are described in mathematical models. In practice, 3-D model was created from the point set whose x,y,z coordinates were known with different interpolation techniques. The results obtained were compared and tried to determine the best method that works.

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14

Volařík, D. "Application of digital elevation model for mapping vegetation tiers." Journal of Forest Science 56, No. 3 (2010): 112–20. http://dx.doi.org/10.17221/74/2009-jfs.

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The aim of this paper is to explore possibilities of application of digital elevation model for mapping vegetation tiers (altitudinal vegetation zones). Linear models were used to investigate the relationship between vegetation tiers and variables derived from a digital elevation model – elevation and potential global radiation. The model was based on a sample of 138 plots located from the 2<SUP>nd</SUP> to the 5<SUP>th</SUP> vegetation tier. Potential global radiation was computed in r.sun module in geographic information system GRASS. The final model explained 84% of data variability and employed variables were found to be sufficient for modelling vegetation tiers in the study area. Applied methodology could be used to increase the accuracy and efficiency of mapping vegetation tiers, especially in areas where such task is considered difficult (e.g. agricultural landscape).

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15

Mulsow, C., R. Kenner, Y. Bühler, A. Stoffel, and H. G. Maas. "SUBAQUATIC DIGITAL ELEVATION MODELS FROM UAV-IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 739–44. http://dx.doi.org/10.5194/isprs-archives-xlii-2-739-2018.

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The paper presents an approach for the generation of digital elevation models (DEMs) of underwater areas from aerial images. Standard software-products do not provide the possibility to measure correctly through refractive interfaces, such as water. Existing solutions for that problem are based on oriented images and known water levels with the DEM points determined by forward intersection based on reconstructed image ray paths (ray tracing). In this article we present an integrated procedure for image orientation as well as DEM mass point determination from aerial imagery containing both land and underwater areas. The proof of concept was done by capturing UAV imagery of shallow water areas of a high-alpine lake in the Swiss alps. In the paper the processed dataset will be presented. Furthermore, the extraction and matching of image-points observed through water are discussed. The accuracy potential as well as practical limitations of processing multimedia-data are analysed.

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16

Eakins, Barry W., and Pamela R. Grothe. "Challenges in Building Coastal Digital Elevation Models." Journal of Coastal Research 297 (September 2, 2014): 942–53. http://dx.doi.org/10.2112/jcoastres-d-13-00192.1.

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17

Berry, P. A. M. "Global digital elevation models -- fact or fiction?" Astronomy & Geophysics 40, no. 3 (1999): 3.10–3.13. http://dx.doi.org/10.1093/astrog/40.3.3.10.

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18

Kennelly, Patrick J., and A. Jon Kimerling. "Desktop Hachure Maps from Digital Elevation Models." Cartographic Perspectives, no. 37 (September 1, 2000): 78–81. http://dx.doi.org/10.14714/cp37.811.

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19

Fairfield, John, and Pierre Leymarie. "Drainage networks from grid digital elevation models." Water Resources Research 27, no. 5 (1991): 709–17. http://dx.doi.org/10.1029/90wr02658.

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20

Fenton, Gordon A., Amanda McLean, Farrokh Nadim, and D. V. Griffiths. "Landslide hazard assessment using digital elevation models." Canadian Geotechnical Journal 50, no. 6 (2013): 620–31. http://dx.doi.org/10.1139/cgj-2011-0342.

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Human beings are, in general, risk-averse and willing to go to great lengths to reduce failure consequences. However, if the underlying issues are not understood, effective action cannot properly be taken. A landslide hazard assessment framework capable of estimating regional probabilities of slope failure can be used to aid a vast number of communities currently living in landslide “danger zones”. Such a framework would provide a tool with which community resources can be optimized and ensure that appropriate preparedness and mitigation strategies are in place. Maximum slope angles, as estimated using digital elevation models (DEMs), are one of the most important indicators for landslide hazard assessment. This paper uses local averaging theory to determine how the resolution of DEMs affects regional landslide probability estimates. Emphasis is on a regional landslide hazard assessment, measured by the probability that one or more slopes of at least a critical minimum scale will fail within the region.

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21

Wecker, Lakin, Faramarz Samavati, and Marina Gavrilova. "Contextual void patching for digital elevation models." Visual Computer 23, no. 9-11 (2007): 881–90. http://dx.doi.org/10.1007/s00371-007-0148-1.

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22

Mawardi, Mawardi, Makmun R. Razali, and Cyntia Cyntia. "LAND SLIDE ANALYSIS USING DIGITAL ELEVATION MODELS." Inersia, Jurnal Teknik Sipil 10, no. 2 (2019): 21–28. http://dx.doi.org/10.33369/ijts.10.2.21-28.

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Landslides almost every year occur in Indonesia, this rare landslide that can be detected early. because so far the prediction of slope slope is difficult. To predict the calculation requires the analysis and stability of the slope manually. This manual calculation process is quite long and long process. The calculation data and slope stability analysis are in the form of slope measurement, the work is quite tiring and risky for the researcher, and also the scope of the slope that can be measured is only narrow. In addition to slope inclination data, for slope analysis also requires soil data to be sampled and testing in a geotechnical laboratory. This study investigated slope stability by creating landslide models using Digital Elevation Models (DEM), and Geoslope programs. Slope model was analyzed from DEM and landslide stability analysis using Geoslope. From this concept we are expected to analyze landslide / stability slope quickly and accurately without risk for researcher. The results of lab tests were obtained:water content (wN), wN1 = 39.47%, wN1 = 40.54%, wN1 = 38.89%. Specific Soil Weight (Gs) ranged from 2.60 to 2.62, wet soil volume weight ranged from 14.59 to 16.16 kN / m3, the weight of saturated soil volume ranged from 15.59 to 16.82 kN / m3, the weight of soil volume dried ranged from 09.99 to 16.82 kN / m3, soil liquid limit ranged 61.26-66.06%, plastic limit of land ranged from 39.58 to 44.88%, soil plastic index ranged from 21.18 to 21.66, so that the soil is categorized as organic clay soil, the face of the soil at a depth of -0.5m, the cohesion value (c) ranges from 29.10 to 34.90 kPa, and the frictional angle values in the range 19.51 21.100, the slope of the slope ranges from 24 to 420 and slope safety figures (FK), on slopes 1 FK = 1.87 (slope safe against landslide hazard), on slope 2 FK = 1.20 (slope unsafe against landslide hazard), on slope 3 FK = 1.52 (the slope is safe from landslide hazards).

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23

Gavriil, Konstantinos, Georg Muntingh, and Oliver J. D. Barrowclough. "Void Filling of Digital Elevation Models With Deep Generative Models." IEEE Geoscience and Remote Sensing Letters 16, no. 10 (2019): 1645–49. http://dx.doi.org/10.1109/lgrs.2019.2902222.

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24

Byalyi, M. O., and P. A. Savkov. "APPLICATION OF RADAR INTERFEROMETRY FOR CONSRTUCTION OF DIGITAL RELIEF MODELS." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 79 (2023): 76–93. http://dx.doi.org/10.17721/2519-481x/2023/79-08.

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Advanced experience in preparing and conducting military operations has shown that terrain is one of the most crucial elements in any map, as it determines the characteristics of the landscape of a specific area. Digital Elevation Models are used in updating digital topographic maps and plans of various scales, conducting various types of engineering surveys, and geological, biological, and geographical research. This article examines existing models and methods for constructing digital elevation models for the purpose of their comparative analysis based on the integration of open, publicly available sources of information. The approaches to building digital elevation models are described, and the information support for their creation is considered. The possibility of using different data sources intended for creating digital elevation model is demonstrated: for land management tasks modeling, hydrological network modeling, analysis of radio tower coverage zones, construction task modeling, and modeling flood and inundation zones. By improving digital elevation model, it is possible to develop new approaches for their creation or integrate existing ones. At the current stage of Ukraine's Armed Forces development, it is advisable to use radar interferometry as a relatively fast and accurate method for constructing digital elevation model. Interferometry is a technology for extracting elevation information from the phase of two images. Interferometric processing is carried out based on the use of radar data. The method assumes that the same area should be imaged with a displacement in space of the radar antenna receiver. Thus, by applying radar interferometry, it is possible to rapidly develop new approaches for creating digital elevation model or combining multiple ones.

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25

Kamiński, Mirosław. "The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence." Geosciences 10, no. 12 (2020): 488. http://dx.doi.org/10.3390/geosciences10120488.

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The paper discusses the impact that the quality of the digital elevation model (DEM) has on the final result of landslide susceptibility modeling (LSM). The landslide map was developed on the basis of the analysis of archival geological maps and the Light Detection and Ranging (LiDAR) digital elevation model. In addition, complementary field studies were conducted. In total, 92 landslides were inventoried and their degree of activity was assessed. An inventory of the landslides was prepared using a 1-m-LiDAR DEM and field research. Two digital photogrammetric elevation models with an elevation pixel resolution of 20 m were used for landslide susceptibility modeling. The first digital elevation model was obtained from a LiDAR point cloud (DEM–airborne laser scanning (ALS)), while the second model was developed based on archival digital stereo-pair aerial images (DEM–Land Parcel Identification System (LPIS)). Both models were subjected to filtration using a Gaussian low-pass filter to reduce errors in their elevation relief. Then, using ArcGIS software, a differential model was generated to illustrate the differences in morphology between the models. The maximum differences in topographic elevations between the DEM–ALS and DEM–LPIS models were calculated. The Weights-of-Evidence model is a geostatistical method used for the landslide susceptibility modeling. Six passive factors were employed in the process of susceptibility generation: elevation, slope gradient, exposure, topographic roughness index (TRI), distance from tectonic lines, and distance from streams. As a result, two landslide susceptibility maps (LSM) were obtained. The accuracy of the landslide susceptibility models was assessed based on the Receiver Operating Characteristic (ROC) curve index. The area under curve (AUC) values obtained from the ROC curve indicate that the accuracy of classification for the LSM–DEM–ALS model was 78%, and for the LSM–LPIS–DEM model was 73%.

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26

Zorina, Victoria V., and Andrey L. Entin. "Inaccuracy of relative elevations on uavbased digital elevation models without precise reference information." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 17, no. 2 (2024): 26–35. http://dx.doi.org/10.24057/2071-9388-2024-3123.

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Imagery obtained from unmanned aerial vehicle (UAV) is widely used for land surface modelling. Recent research prove that digital elevation models (DEMs) created from UAV imagery are characterized by a high rate of accuracy and reliability. Most of these studies are focused on assessing absolute elevation accuracy of the UAV DEMs, but the accuracy of relative elevations (i.e., accuracy of reproducing of local elevation differences within DEM) also should be considered. In this paper, we focus on the precision of replicating relative elevations in DEMs derived from imagery captured via UAVs without precise coordinate reference. To evaluate this accuracy, we use datasets of aerial images processed in two different methods: one with on-board coordinates obtained from a GNSS receiver, and the other based on precise coordinates calculated with the Post-Processing Kinematic (PPK) method. The sites selected for assessment are not look like each other in terms of terrain and forest cover characteristics to track the difference of modelling in the divergent areas. Constructed DEMs were compared with reference fragments of global DEMs by the statistical indices for the difference fields. The findings indicate that the absence of an accurate coordinate reference does not have a substantial impact on the precision of reproducing relative elevations in the DEM. This makes it possible to use UAV materials without precise coordinate reference for modelling in most geographical studies, where the error of terrain steepness values of 0.9° can be considered acceptable.

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27

Hepburn, Adam J., Tom Holt, Bryn Hubbard, and Felix Ng. "Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline." Geoscientific Instrumentation, Methods and Data Systems 8, no. 2 (2019): 293–313. http://dx.doi.org/10.5194/gi-8-293-2019.

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Abstract. The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to low-resolution global datasets, such models enable the study of landforms <10 km in size, which is the primary scale at which geomorphic processes have been active on Mars over the last 10–20 Myr . Stereogrammetry is a means of producing digital elevation models from stereo pairs of images. The HiRISE camera on board the Mars Reconnaissance Orbiter has captured >3000 stereo pairs at 0.25 m pixel−1 resolution, enabling the creation of high-resolution digital elevation models (1–2 m pixel−1). Hitherto, only ∼500 of these pairs have been processed and made publicly available. Existing pipelines for the production of digital elevation models from stereo pairs, however, are built upon commercial software, rely upon sparsely available intermediate data, or are reliant on proprietary algorithms. In this paper, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open-source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is designed for simple application by researchers interested in the use of high-resolution digital elevation models. Implemented here on a research computing cluster, this pipeline can also be used on consumer-grade UNIX computers. We produce and evaluate four digital elevation models using the pipeline presented here. Each are globally well registered, with accuracy similar to those of digital elevation models produced elsewhere.

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28

Hassan, Aqeel Abboud Abdul. "Accuracy Assessment of Open Source Digital Elevation Models." Journal of University of Babylon for Engineering Sciences 26, no. 3 (2018): 23–33. http://dx.doi.org/10.29196/jub.v26i3.601.

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Digital Elevation Model is a three-dimensional representation of the earth's surface, which is essential for Geoscience and hydrological implementations. DEM can be created utilizing Photogrammetry techniques, radar interferometry, laser scanning and land surveying. There are some world agencies provide open source digital elevation models which are freely available for all users, such as the National Aeronautics and Space Administration (NASA), Japan Aerospace Exploration Agency’s (JAXA) and others. ALOS, SRTM and ASTER are satellite based DEMs which are open source products. The technologies that are used for obtaining raw data and the methods used for its processing and on the other hand the characteristics of natural land and land cover type, these and other factors are the cause of implied errors produced in the digital elevation model which can't be avoided. In this paper, ground control points observed by the differential global positioning system DGPS were used to compare the validation and performance of different satellite based digital elevation models. For validation, standard statistical tests were applied such as Mean Error (ME) and Root Mean Square Error (RMSE) which showed ALOS DEM had ME and RMSE are -1.262m and 1.988m, while SRTM DEM had ME of -0.782m with RMSE of 2.276m and ASTER DEM had 4.437m and 6.241m, respectively. These outcomes can be very helpful for analysts utilizing such models in different areas of work.

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29

Amante, Christopher J., and Barry W. Eakins. "Accuracy of Interpolated Bathymetry in Digital Elevation Models." Journal of Coastal Research 76 (December 2016): 123–33. http://dx.doi.org/10.2112/si76-011.

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Klikunova, A., and A. Khoperskov. "Creation of digital elevation models for river floodplains." Information Technology and Nanotechnology, no. 2391 (2019): 275–84. http://dx.doi.org/10.18287/1613-0073-2019-2391-275-284.

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A procedure for constructing a digital elevation model (DEM) of the northern part of the Volga-Akhtuba interfluve is described. The basis of our DEM is the elevation matrix of Shuttle Radar Topography Mission (SRTM) for which we carried out the refinement and updating of spatial data using satellite imagery, GPS data, depth measurements of the River Volga and River Akhtuba stream beds. The most important source of high-altitude data for the Volga-Akhtuba floodplain (VAF) can be the results of observations of the coastlines dynamics of small reservoirs (lakes, eriks, small channels) arising in the process of spring flooding and disappearing during lowflow periods. A set of digitized coastlines at different times of flooding can significantly improve the quality of the DEM. The method of constructing a digital elevation model includes an iterative procedure that uses the results of morphostructural analysis of the DEM and the numerical hydrodynamic simulations of the VAF flooding based on the shallow water model.

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31

Jenness, Jeff S. "Calculating landscape surface area from digital elevation models." Wildlife Society Bulletin 32, no. 3 (2004): 829–39. http://dx.doi.org/10.2193/0091-7648(2004)032[0829:clsafd]2.0.co;2.

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32

Furze, Shane, Jae Ogilvie, and Paul A. Arp. "Fusing Digital Elevation Models to Improve Hydrological Interpretations." Journal of Geographic Information System 09, no. 05 (2017): 558–75. http://dx.doi.org/10.4236/jgis.2017.95035.

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33

Miandad, Javed, Margaret M. Darrow, Michael D. Hendricks, and Ronald P. Daanen. "Landslide Mapping Using Multiscale LiDAR Digital Elevation Models." Environmental and Engineering Geoscience 26, no. 4 (2020): 405–25. http://dx.doi.org/10.2113/eeg-2268.

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ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

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34

Farah, Ashraf, Ashraf Talaat, and Farrag Farrag. "Accuracy Assessment of Digital Elevation Models Using GPS." Artificial Satellites 43, no. 4 (2008): 151–61. http://dx.doi.org/10.2478/v10018-009-0014-7.

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Accuracy Assessment of Digital Elevation Models Using GPSA Digital Elevation Model (DEM) is a digital representation of ground surface topography or terrain with different accuracies for different application fields. DEM have been applied to a wide range of civil engineering and military planning tasks. DEM is obtained using a number of techniques such as photogrammetry, digitizing, laser scanning, radar interferometry, classical survey and GPS techniques. This paper presents an assessment study of DEM using GPS (Stop&Go) and kinematic techniques comparing with classical survey. The results show that a DEM generated from (Stop&Go) GPS technique has the highest accuracy with a RMS error of 9.70 cm. The RMS error of DEM derived by kinematic GPS is 12.00 cm.

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35

Louhaichi, M., M. M. Borman, A. L. Johnson, and D. E. Johnson. "Creating Low-Cost High-Resolution Digital Elevation Models." Journal of Range Management 56, no. 1 (2003): 92. http://dx.doi.org/10.2307/4003887.

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36

Moretti, Giovanni, and Stefano Orlandini. "Hydrography‐Driven Coarsening of Grid Digital Elevation Models." Water Resources Research 54, no. 5 (2018): 3654–72. http://dx.doi.org/10.1029/2017wr021206.

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37

Montgomery, David R., and Efi Foufoula-Georgiou. "Channel network source representation using digital elevation models." Water Resources Research 29, no. 12 (1993): 3925–34. http://dx.doi.org/10.1029/93wr02463.

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38

Band, Lawrence E. "Topographic Partition of Watersheds with Digital Elevation Models." Water Resources Research 22, no. 1 (1986): 15–24. http://dx.doi.org/10.1029/wr022i001p00015.

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39

Ravibabu, Mandla V., and Kamal Jain. "A Web-based Survey on Digital Elevation Models." Annals of GIS 12, no. 1 (2006): 34–37. http://dx.doi.org/10.1080/10824000609480615.

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40

Lakshmi, Subbu Esakkipandian, and Kiran Yarrakula. "Review and critical analysis on digital elevation models." Geofizika 35, no. 2 (2019): 129–57. http://dx.doi.org/10.15233/gfz.2018.35.7.

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41

Gens, Rüdiger. "Quality assessment of interferometrically derived digital elevation models." International Journal of Applied Earth Observation and Geoinformation 1, no. 2 (1999): 102–8. http://dx.doi.org/10.1016/s0303-2434(99)85003-x.

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42

Gooch, M. J., and J. H. Chandler. "Failure prediction in automatically generated digital elevation models." Computers & Geosciences 27, no. 8 (2001): 913–20. http://dx.doi.org/10.1016/s0098-3004(00)00129-1.

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ÖStman, A. "QUALITY CONTROL OF PHOTOGRAMMETRICALLY SAMPLED DIGITAL ELEVATION MODELS." Photogrammetric Record 12, no. 69 (2006): 333–41. http://dx.doi.org/10.1111/j.1477-9730.1987.tb00579.x.

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Aguilar, Fernando J., and Jon P. Mills. "Accuracy assessment of lidar-derived digital elevation models." Photogrammetric Record 23, no. 122 (2008): 148–69. http://dx.doi.org/10.1111/j.1477-9730.2008.00476.x.

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Dixon, A. R., G. H. Kirby, and D. P. M. Wills. "Towards Context- Dependent Interpolation of Digital Elevation Models." Computer Graphics Forum 13, no. 3 (1994): 23–32. http://dx.doi.org/10.1111/1467-8659.1330023.

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Guo-an, Tang, Josef Strobl, Gong Jian-ya, Zhao Mu-dan, and Chen Zhen-jiang. "Evaluation on the accuracy of digital elevation models." Journal of Geographical Sciences 11, no. 2 (2001): 209–16. http://dx.doi.org/10.1007/bf02888692.

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Cogbill, Allen H. "Gravity terrain corrections calculated using digital elevation models." GEOPHYSICS 55, no. 1 (1990): 102–6. http://dx.doi.org/10.1190/1.1442762.

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Abstract:

Corrections for terrain effects are required for virtually all gravity measurements acquired in mountainous areas, as well as for high‐precision surveys, even in areas of low relief. Terrain corrections are normally divided into two parts, one part being the correction for terrain relatively close to the gravity station (the “inner‐zone” correction) and the other part being the correction for more distant, say, >2 km, terrain. The latter correction is normally calculated using a machine procedure that accesses a digital‐terrain data set. The corrections for terrain very close to the gravity station are done manually using Hammer’s (1939) procedures or a similar method, are guessed in the field, or simply are neglected. Occasionally, special correction procedures are used for the inner‐zone terrain corrections (e.g., LaFehr et al., 1988); but such instances are uncommon.

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Kidner, David B., and Derek H. Smith. "Compression of digital elevation models by Huffman coding." Computers & Geosciences 18, no. 8 (1992): 1013–34. http://dx.doi.org/10.1016/0098-3004(92)90018-m.

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Smith, Derek H., and Michael Lewis. "Optimal predictors for compression of digital elevation models." Computers & Geosciences 20, no. 7-8 (1994): 1137–41. http://dx.doi.org/10.1016/0098-3004(94)90067-1.

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Tejaswini, Vallu, and Sathian KK. "Comparison of digital elevation models for hydrological modeling." International Journal of Agriculture and Food Science 7, no. 4 (2025): 104–6. https://doi.org/10.33545/2664844x.2025.v7.i4b.340.

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