SRTM resample with short distance‐low nugget kriging

Grohmann, Carlos Henrique; Steiner, Santos

doi:10.1080/13658810701730152

Cited by 28 publications

(6 citation statements)

References 18 publications

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“…The major differences between the datasets occur in the 0-40 m range, where TanDEM-X data show a peak at ∼2 m, SRTM and ALOS AW3D30 at ∼5 m and ASTER GDEM peak at ∼10 m. This reflects the geomorphology of the area, with a large coastal plain and presence of aeolian dune fields, which can show radar shadowing effects and can be difficult to model via photogrammetry due the lack of contrast in homogeneous sand landforms. Maps of slope for all study areas and DEMs are presented in Supplemental Figures S10-S16; summary statistics are presented in Supplemental Tables S8-S14, the distribution of slope values can be seen as histograms in Supplemental Figure S17, while Supplemental Figure S18 shows plots of mean slope per elevation (Guth, 2006;Grohmann and Steiner, 2008), which provide an effective way of comparing the response of each DEM to local variations of the topographic surface.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Grohmann¹

2018

Remote Sensing of Environment

166

101

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A first assessment of the TanDEM-X DEMs over Brazilian territory is presented through a comparison with SRTM, ASTER GDEM and ALOS AW3D30 DEMs in seven study areas with distinct geomorphological contexts, vegetation coverage, and land use. Visual analysis and elevation histograms point to a finer effective spatial (i.e., horizontal) resolution of TanDEM-X compared to SRTM and ASTER GDEM. In areas of open vegetation, TanDEM-X lower elevations indicate a deeper penetration of the radar signal. DEMs of differences (DoDs) allowed the identification of issues inherent to the production methods of the analyzed DEMs, such as mast oscillations in SRTM data and mismatch between adjacent scenes in ASTER GDEM and ALOS AW3D30. A systematic difference in elevations between TanDEM-X 12 m, TanDEM-X 30 m, and SRTM was observed in the steep slopes of the coastal ranges, related to the moving-window process used to resample the 12 m data to a 30 m pixel size. It is strongly recommended to produce a DoD with SRTM before using ASTER GDEM or ALOS AW3D30 in any analysis, to evaluate if the area of interest is affected by these problems. The DoDs also highlighted changes in land use in the time span between the acquisition of SRTM (2000) and TanDEM-X (2013) data, whether by natural causes or by human interference in the environment. The results show a high level of detail and consistency for TanDEM-X data, indicate that the effective horizontal resolution of SRTM is coarser than the nominal 30 m, and highlight the errors in ASTER GDEM and ALOS AW3D30 due to mismatch between adjacent scenes in the photogrammetric process.

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Section: Descriptive Statisticsmentioning

confidence: 99%

Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Grohmann¹

2018

Remote Sensing of Environment

166

101

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“…Other Penninga and Van Oosterom (2008) and Grohmann and Steiner (2008) show a continuing strength of interest in it by readers.…”

Section: Trends Over the Past Yearmentioning

confidence: 94%

Recent Trends in IJGISc

Lees¹

2009

International Journal of Geographical Information Science

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“…Thus, the accuracy and grid DEM resolution can be increased using resampling and downscaling methods [19,20]. The common approaches for downscale resampling are bilinear, bi-cubic, and Kriging [21]. The main advantage of these methods is that they do not require prior DEM data at different resolutions for training and are computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

Minh,

Huong,

Khanh

et al. 2024

Remote Sensing

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The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.

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SRTM resample with short distance‐low nugget kriging

Cited by 28 publications

References 18 publications

Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Recent Trends in IJGISc

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

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