Comparative evaluation of vertical accuracy of elevated points with ground control points from ASTERDEM and SRTMDEM with respect to CARTOSAT-1DEM

Rawat, Kishan Singh; Singh, Sudhir Kumar; Singh, Mutum Ibomcha; Garg, Bharat

doi:10.1016/j.rsase.2018.11.005

Cited by 33 publications

(17 citation statements)

References 43 publications

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“…Traditional data collection methods such as mapping and ground surveying are time-consuming and costly 2 . Additionally, the information is not updated frequently and is difficult to access 3,4 . A powerful implementation of economic activity monitoring research is remotely-sensed image, which provide an up-to-date and realistic presentation of the Earth's surface.…”

Section: Analysis Of Regional Economic Development Based On Land Use mentioning

confidence: 99%

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Chen

Liu

et al. 2020

Sci Rep

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consistent means for monitoring economic activities 22-25. The initial purpose of the DMSP/OLS, however, was to observe the clouds illuminated by moonlight, and the night-time light imagery was a by-product of the data under cloud-free conditions 26-30. Consequently, there remain some limitations when using NTL data: (1) Underestimation of economic activities that emit less or no additional night-time light, and in the potentially serious measurement errors of gross domestic product (GDP) growth, particularly in developing and emerging economies, the growth is more likely to be underestimated 31-33. (2) As the most important NTL data, DMSP-OLS data are provided by multiple DMSP satellites, and the fact that NTL data from different satellites in different years cannot be directly compared due to lack of onboard calibration is likely to be the main obstacle to a time-series analysis 34-37. (3) The DMSP-OLS NTL data provided by the NGDC have a geographic grid resolution of 30 arc seconds and a grid cell size that is approximately 0.86 km 2 at the equator 16. Therefore, the probability of multiple ground objects belonging to the same pixel is large, which affects the accuracy of economic activity evaluation 38. Generally, satellite remote-sensing missions are originally designed to monitor the physical environment of the Earth, for which the mapping of LUCC information is one of the most important applications 39,40. LUCC information is usually associated with and mainly driven by socioeconomic factors and is also a direct reflection of economic activities 41-43. It is well documented that the relationship between economic growth and LUCC information is not a one-way effect, but rather a complex relationship of interactions 44,45. On the one hand, economic activities have profoundly changed the surface morphology of the Earth. Moreover, with economic development and population increase, land use changes have accelerated sharply, and the land cover pattern changes have become more and more significant. On the other hand, the variation process of land use and land cover has significant impacts on economy. As the foundation for economic activities, land is an indispensable production factor for economic development, and the input of land resources plays an important role in promoting economic growth 34,46,47. Thus, in the coordinated process of LUCC information and economic development, changes in economic activity intensity can be reflected through LUCC information. Remotely-sensed image can intuitively and comprehensively reflect the dynamics of land use and land cover, and the types, quantities and locations of LUCC information can be obtained via classification technology from remotely-sensed image. Normal multispectral optical satellite data with various spatial, temporal and spectral resolutions have been extensively applied in investigations of LUCC information and its driving mechanism of socioeconomic factors 44,48. However, economic indicators for assessing economic development have rarely been connected to LU...

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Section: Analysis Of Regional Economic Development Based On Land Use mentioning

confidence: 99%

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Chen

Liu

et al. 2020

Sci Rep

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“…Since we did not discuss the absolute geo coordinate accuracy by using GCPs, the corrected DEM would still inevitably contain errors. A true error assessment can typically be conducted with enough GCPs [9,10,15,38,52], which provides an actual error estimation. However, the error estimation using GCP in an insecure place, located in a remote area with high mountain and poor traffic accessibility, makes it nearly impossible to collect such data.…”

Section: Advantages and Shortcoming Of The Methodsmentioning

confidence: 99%

“…We also evaluated the model performance in decreasing the locational errors using the quantitative value of the root means square error (RMSE) to confirm the degree of the matching in both corrected and uncorrected data in the unaffected area (Figures 13c and 14c). This indicator has been frequently used to evaluate the prediction error quantitatively [38]. The RMSE of the corrected profile cross-section was obtained at 0.81 m, whereas the RMSE for the uncorrected profile cross-section was obtained at 3.81 m, which is more than four times larger than that of the corrected one.…”

Section: Comparison Of Cross-sections Of Dems In the Affected And Unamentioning

confidence: 99%

Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs

Atefi

Miura

2021

Remote Sensing

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On 2 May 2014, a large-scale landslide in Abe Barek, Badakhshan, Afghanistan, produced extensive damage to the buildings and killed hundreds of people. Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present the applicability of a nonlinear geometric correction technique for decreasing the undesired registration errors between pre- and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery, identifying landslide affected areas, and quantifying the landslide volume from DEMs of difference (DoD) analysis. The nonlinear mapping method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the method outperformed the simple DoD technique by eliminating a large-scale of geometric errors in an unaffected area. We estimated the volume of the landslide as 1.05 × 106 m3 from the DoD corrected by the nonlinear method, and discussed the relationship between the area and volume compared to those of the previous studies.

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“…where X i is the elevation of a DEM at point i, Y i is the reference elevation at point i, and n is the number of samples. According to Rawat et al (2019), RMSE varies with the variability within the distribution of error magnitudes, square root of the number of errors and the magnitude of MAE. MAE is a more natural measure of average error and, unlike RMSE, is unambiguous (Rawat et al, 2019).…”

Section: Vertical Accuracy Of the Demsmentioning

confidence: 99%

Assessing the influence of DEM source on derived streamline and catchment boundary accuracy

Mashimbye

Clercq

Niekerk

2019

WSA

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Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (<30 m) DEMs produce more accurate parameters and that DEM source and resampling techniques also play a role. However, if high resolution DEMs are not available, the 30 m SRTM DEM is recommended as its vertical accuracy was relatively high and the quality of the streamlines and catchment boundary was good. In addition, it was found that the novel Euclidean distance-based MAE and RMSE proposed in this study to compare reference and DEM-extracted raster datasets of different resolutions is a more reliable indicator of geometrical accuracy than the correctness and figure of merit indices.

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Comparative evaluation of vertical accuracy of elevated points with ground control points from ASTERDEM and SRTMDEM with respect to CARTOSAT-1DEM

Cited by 33 publications

References 43 publications

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs

Assessing the influence of DEM source on derived streamline and catchment boundary accuracy

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