scholarly journals A geomorphology based approach for digital elevation model fusion – case study in Danang City, Vietnam

2014 ◽  
Vol 2 (1) ◽  
pp. 255-296 ◽  
Author(s):  
T. A. Tran ◽  
V. Raghavan ◽  
S. Masumoto ◽  
P. Vinayaraj ◽  
G. Yonezawa

Abstract. Global Digital Elevation Model (DEM) is considered as vital spatial information and finds wide use in several applications. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) and Shuttle Radar Topographic Mission (SRTM) DEM offer almost global coverage and provide elevation data for geospatial analysis. However, GDEM and SRTM still contain some height errors that affect the quality of elevation data significantly. This study aims to examine methods to improve the resolution as well as accuracy of available free DEMs by data fusion technique and evaluating the results with high quality reference DEM. The DEM fusion method is based on the accuracy assessment of each global DEM and geomorphological characteristics of the study area. Land cover units were also considered to correct the elevation of GDEM and SRTM with respect to the bare earth surface. Weighted averaging method was used to fuse the input DEMs based on landform classification map. According to the landform types, the different weights were used for GDEM and SRTM. Finally, a denoising algorithm (Sun et al., 2007) was applied to filter the output fused DEM. This fused DEM shows excellent correlation to the reference DEM having correlation coefficient R2 = 0.9986 and the accuracy was also improved from Root Mean Square Error (RMSE) 14.9 m in GDEM and 14.8 m in SRTM into 11.6 m in fused DEM.

2014 ◽  
Vol 2 (2) ◽  
pp. 403-417 ◽  
Author(s):  
T. A. Tran ◽  
V. Raghavan ◽  
S. Masumoto ◽  
P. Vinayaraj ◽  
G. Yonezawa

Abstract. Global digital elevation models (DEM) are considered a source of vital spatial information and find wide use in several applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) and Shuttle Radar Topographic Mission (SRTM) DEM offer almost global coverage and provide elevation data for geospatial analysis. However, GDEM and SRTM still contain some height errors that affect the quality of elevation data significantly. This study aims to examine methods to improve the resolution as well as accuracy of available free DEMs by data fusion techniques and evaluating the results with a high-quality reference DEM. The DEM fusion method is based on the accuracy assessment of each global DEM and geomorphological characteristics of the study area. Land cover units were also considered to correct the elevation of GDEM and SRTM with respect to the bare-earth surface. The weighted averaging method was used to fuse the input DEMs based on a landform classification map. According to the landform types, the different weights were used for GDEM and SRTM. Finally, a denoising algorithm (Sun et al., 2007) was applied to filter the output-fused DEM. This fused DEM shows excellent correlation to the reference DEM, having a correlation coefficient R2 = 0.9986, and the accuracy was also improved from a root mean square error (RMSE) of 14.9 m in GDEM and 14.8 m in SRTM to 11.6 m in the fused DEM. The results of terrain-related parameters extracted from this fused DEM such as slope, curvature, terrain roughness index and normal vector of topographic surface are also very comparable to reference data.


Author(s):  
S. D. Jawak ◽  
A. J. Luis

Available digital elevation models (DEMs) of Antarctic region generated by using radar altimetry and the Antarctic digital database (ADD) indicate elevation variations of up to hundreds of meters, which necessitates the generation of local DEM and its validation by using ground reference. An enhanced digital elevation model (eDEM) of the Schirmacher oasis region, east Antarctica, is generated synergistically by using Cartosat-1 stereo pair-derived photogrammetric DEM (CartoDEM)-based point elevation dataset and multitemporal radarsat Antarctic mapping project version 2 (RAMPv2) DEM-based point elevation dataset. In this study, we analyzed suite of interpolation techniques for constructing a DEM from RAMPv2 and CartoDEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improves the elevation accuracy of DEM from synergistically fused RAMPv2 and CartoDEM point elevation datasets. RAMPv2 points and CartoDEM points were used as primary data for various interpolation techniques such as ordinary kriging (OK), simple kriging (SK), universal kriging (UK), disjunctive kriging (DK) techniques, inverse distance weighted (IDW), global polynomial (GP) with power 1 and 2, local polynomial (LP) and radial basis functions (RBF). Cokriging of 2 variables with second dataset was used for ordinary cokriging (OCoK), simple cokriging (SCoK), universal cokriging (UCoK) and disjunctive cokriging (DCoK). The IDW, GP, LP, RBF, and kriging methods were applied to one variable, while Cokriging experiments were employed on two variables. The experiment of dataset and its combination produced two types of point elevation map categorized as (1) one variable (RAMPv2 Point maps and CartoDEM Point maps) and (2) two variables (RAMPv2 Point maps + CartoDEM Point maps). Interpolated surfaces were evaluated with the help of differential global positioning system (DGPS) points collected from study area during the Indian Scientific Expedition to Antarctic (ISEA). Accuracy assessment of the RAMPv2 DEM, CartoDEM, and combined eDEM (RAMPv2 + CartoDEM) by using DGPS as ground reference data shows that eDEM achieves much better accuracy (average elevation error 8.44 m) than that of existing DEM constructed by using only CartoDEM (13.57 m) or RAMPv2 (41.44 m) alone. The newly constructed eDEM achieves a vertical accuracy of about 7 times better than RAMPv2 DEM and 1.5 times better than CartoDEM. After using accurate DGPS data for accuracy assessment, the approximation to the actual surface of the eDEM extracted here is much more accurate with least mean root mean square error (RMSE) of 9.22 m than that constructed by using only CartoDEM (RMSE = 14.15 m) point elevation data and RAMPv2 (RMSE = 69.48 m) point elevation data. Our results indicate that, the overall trend of accuracy for the interpolation methods for generating continuous elevation surface from CartoDEM + RAMPv2 point elevation data, based on RMSE, is as follows: GP1 > IDW > GP2 > OK > LP2 > DK > LP1 > RBF > SK > UK. In case of cokriging interpolation methods, OCoK yields more accurate eDEM with the least RMSE of 8.16 m, which can be utilized to generate a highly accurate DEM of the research area.. Based on this work, it is inferred that GP2 and OCok interpolation methods and synergistic use of RAMPv2 and CartoDEM-based point elevation datasets lead to a highly accurate DEM of the study region. This research experiment demonstrates the stability (w.r.t multi-temporal datasets), performance (w.r.t best interpolation technique) and consistency (w.r.t all the experimented interpolation techniques) of synergistically fused eDEM. On the basis of average elevation difference and RMSE mentioned in present research, the newly constructed eDEM may serve as a benchmark for future elevation models such as from the ICESAT-II mission to spatially monitor ice sheet elevation.


Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 31-39 ◽  
Author(s):  
W. G. Rees

ABSTRACTA new source of digital elevation data, the advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), has been freely available since 2009. It provides enormously greater coverage of the Arctic than previous satellite derived ‘global’ digital elevation models, extending to a latitude of 83 °N in contrast to 60 °N. The GDEM is described as a preliminary, research grade product. This paper investigates its accuracy in a number of specifically Arctic landscapes, including ice and snow, boreal forest, tundra and unvegetated terrain, using test sites in Svalbard, Iceland, Norway and Russia. Semivariogram analysis is used to characterise the magnitude and spatial correlation of errors in the GDEM products from the test sites. The analysis suggests that the horizontal resolution of the GDEM data is around 130 m, somewhat coarser than the sampling interval of 1 second of latitude and longitude. The vertical accuracy is variable, and the factors influencing it have not been systematically explored. However, it appears that the likely accuracy can be estimated from ‘stacking number’ data supplied with the elevation data. The stacking number is the number of independent digital elevation models averaged to generate the supplied product. Provided that this number is greater than around 6 the data have an rms accuracy of typically 5–10 m.


2019 ◽  
Vol 24 (2) ◽  
pp. 105
Author(s):  
Ade Suhendar Sutisna ◽  
Haryono Putro

Availability of Digital Elevation Model (DEM) dataset and Geographic Information System (GIS), makes the watershed properties can be extracted automatically. There are two DEM providers which are freely accessible for research purposes and commonly use that is the Shuttle Radar Topographic Mission (SRTM) - DEM (30m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2). Based on the result of modeling conducted at Ciliwung River Basin with Qgis application, area generated from SRTM data is 5% smaller than Ciliwung River Basin which obtained from BPDAS Ciliwung-Citarum as a reference, while the result of ASTER-GDEM data is 87% larger than reference. Linear Regression Test and t-Test performed on three segments of the watershed shows that the upstream of both samples gives a good accuracy result that is R2 = 0,999; P = 0,499 (SRTM) and R2 = 0,999; P = 0,481 (ASTER-GDEM), while in the middle and downstream segments respectively for both samples are SRTM with R2 = 0,993; P = 0,413 and R2 = 0,734; P = 0,088; and then ASTER-GDEM with R2 = 0,784; P = 0,00038 and R2 = 0,376; P = 1,27209 x10-22.


2018 ◽  
Vol 2 (1) ◽  
pp. 88-97
Author(s):  
Totok Wahyu Wibowo

A contour map is one of many layers that composed Informasi Geospasial Dasar (IGD), which according to Act. No 4 2011 serves as a reference for any thematic map. The provision of contour map at a different level of scale is needed since mapping activities will always refer to map scale based on the mapping area. This research aims to analyze automated contour generation quality to produce 1:50.000 contour map, by means of using open access Digital Elevation Model (DEM) data, such as Shuttle Radar Topographic Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). The automated contour generation was done by using contour interpolation in Quantum GIS software. Furthermore, simplification and smoothing algorithm was applied to both data, in order to improve their visual appearance. In this case, there are four algorithms used in the study, namely Douglas-Peucker, Visvalingam, Chaikin, and McMaster. Quality assessment, both qualitative and quantitative assessment, was done to each derived contour map to ensure the applicability of the procedure. The result shows that contour map generated from SRTM has a better quality than contour map generated from ASTER GDEM. Nevertheless, both data has a similar pattern on each topographical classes, which tends to produce bad quality contour line in the flat area. The more mountainous the area, the better the contour line. Meanwhile, of all generalization algorithm applied in this study, Chaikin’s algorithm is the best algorithm in terms of smoothing the contour line and improving visual quality, but still doesn’t significantly improved the metric accuracy. The contour line can be either directly added to the Digital Cartographic Model of Topographic Map (Rupabumi Map), or used as compliance data in a thematic map.


2021 ◽  
Author(s):  
Shizhou Ma ◽  
Karen Beazley ◽  
Patrick Nussey ◽  
Chris Greene

Abstract The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality and methodology, ARA studies to date have not achieved effective computer-based ARA-component delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To achieve framework refinement and determine the optimal input elevation data for future ARA studies, this study tested a novel Digital Elevation Model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-meter spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions.


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