Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites

Han, Haijiao; Zeng, Qiming; Jiao, Jiantao

doi:10.3390/rs13071304

Cited by 27 publications

(18 citation statements)

References 55 publications

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“…Given that SAR sensors collect data regardless of weather and day or night conditions, DEM generation from InSAR is essential for applications in remote areas where the photogrammetric approach to DEM generation using optical imagery may be affected by unfavorable weather conditions, e.g., cloud coverage [17]. For further details, the reader should refer to the overview of the technical properties of these DEMs provided in [19].…”

Section: Introductionmentioning

confidence: 99%

Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery

et al. 2021

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“Tells” are archaeological mounds formed by deposition of large amounts of anthropogenic material and sediments over thousands of years and are the most important and prominent features in Near and Middle Eastern archaeological landscapes. In the last decade, archaeologists have exploited free-access global digital elevation model (DEM) datasets at medium resolution (i.e., up to 30 m) to map tells on a supra-regional scale and pinpoint tentative tell sites. Instead, the potential of satellite DEMs at higher resolution for this task was yet to be demonstrated. To this purpose, the 3 m resolution imaging capability allowed by the Italian Space Agency’s COSMO-SkyMed Synthetic Aperture Radar (SAR) constellation in StripMap HIMAGE mode was used in this study to generate DEM products of enhanced resolution to undertake, for the first time, a systematic mapping of tells and archaeological deposits. The demonstration is run at regional scale in the Governorate of Wasit in central Iraq, where the literature suggested a high density of sites, despite knowledge gaps about their location and spatial distribution. Accuracy assessment of the COSMO-SkyMed DEM is provided with respect to the most commonly used SRTM and ALOS World 3D DEMs. Owing to the 10 m posting and the consequent enhanced observation capability, the COSMO-SkyMed DEM proves capable to detect both well preserved and levelled or disturbed tells, standing out for more than 4 m from the surrounding landscape. Through the integration with CORONA KH-4B tiles, 1950s Soviet maps and recent Sentinel-2 multispectral images, the expert-led visual identification and manual mapping in the GIS environment led to localization of tens of sites that were not previously mapped, alongside the computation of a figure as up-to-date as February 2019 of the survived tells, with those affected by looting. Finally, this evidence is used to recognize hot-spot areas of potential concern for the conservation of tells. To this purpose, we upgraded the spatial resolution of the observations up to 1 m by using the Enhanced Spotlight mode to collect a bespoke time series. The change detection tests undertaken on selected clusters of disturbed tells prove how a dedicated monitoring activity may allow a regular observation of the impacts due to anthropogenic disturbance (e.g., road and canal constructions or ploughing).

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Section: Introductionmentioning

confidence: 99%

Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery

et al. 2021

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“…15c). Therefore, the large errors remaining in these four DEMs should be due to systematic deviations (Han et al 2021), rather than the influence of misregistration.…”

Section: Influence Of Misregistrationmentioning

confidence: 96%

“…Besides, glacial regions were not considered in their studies, due to the variations of glaciers over time. Misregistration among DEMs, which may lead to evaluation bias (Han et al 2021;Hugonnet et al 2021;Van Niel et al 2008), was also neglected. Bearing these issues in mind, and considering the limitations of optics sensors in rugged terrain and the glacier accumulation area , it is clear that a further assessment of the performance of AW3D30 is required.…”

Section: Introductionmentioning

confidence: 99%

Towards ice thickness inversion: an evaluation of global DEMs by ICESat-2 in the glacierized Tibetan Plateau

Chen

Zhang

et al. 2021

Preprint

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Abstract. Accurate estimates of regional ice thickness, which are generally produced by ice-thickness inversion models, are crucial for assessments of available freshwater resources and sea level rise. Digital elevation model (DEM) derived surface topography of glaciers is a primary data source for such models. However, the scarce in-situ measurements of glacier surface elevation limit the evaluation of DEM uncertainty, and hence its influence on ice-thickness modelling over the glacierized area of the Tibetan Plateau (TP). Here, we examine the performance over the glacierized TP of six widely used and mainly global-scale DEMs: AW3D30 (30 m), SRTM-GL1 (30 m), NASADEM (30 m), TanDEM-X (90 m), SRTM v4.1 (90 m) and MERIT (90 m) by using ICESat-2 laser altimetry data while considering the effects of glacier dynamics, terrain, and DEM misregistration. The results reveal NASADEM as the best performer, with a small mean error (ME) of −1.0 and a root mean squared error (RMSE) of 12.6 m. A systematic vertical offset existed in AW3D30 (−35.3 ME and 34.9 m RMSE), although it had a similar relative accuracy to NASADEM (~ 13 m STD). TanDEM-X also performs well (−0.1 ME and 15.1 m RMSE), but suffers from serious errors and outliers on steep slopes. SRTM-based DEMs (SRTM-GL1, SRTM v4.1, and MERIT) (all ~ 36 m RMSE) had an inferior performance to NASADEM. However, their errors were reduced in the ablation zone when glacier variations were excluded. Errors in the six DEMs increased from the south-facing to the north-facing aspect and become larger with increasing slope. Misregistration of DEMs relative to ICESat-2 footprint in most glacier areas is small (less than one pixel). An intercomparison of four ice-thickness models: GlabTop2, Open Global Glacier Model (OGGM), Huss-Farinotti (HF), Ice Thickness Inversion Based on Velocity (ITIBOV), show that GlabTop2 is sensitive to the accuracy of both elevation and slope, while OGGM and HF are less sensitive to DEM quality, and ITIBOV is the most sensitive to slope accuracy. Considering the inconsistency of DEMs acquisition dates, NASADEM would be a best choice for ice-thickness estimates over the TP, followed by AW3D30, and TanDEM-X (if steep and high elevation terrain can be avoided). Our assessment figures out the performances of mainly global DEMs over the glacierized TP. This study not only avails the glacier thickness estimation with ice thickness inversion models, but also offered references for other cryosphere studies using DEM.

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“…The digital elevation model (DEM) is one of the sources of national geospatial infrastructure [1,2] and has been widely used in many fields, such as topographic mapping [3,4], ocean monitoring [5,6], glacial evolution detection [7], smart cities [8], and global land change [9]. The quality of a DEM is a basis for the applications and is checked during every step of producing the DEM, from the collection of elevations to the interpolation implemented for resampling [10].…”

Section: Introductionmentioning

confidence: 99%

Voids Filling of DEM with Multiattention Generative Adversarial Network Model

et al. 2022

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The digital elevation model (DEM) acquired through photogrammetry or LiDAR usually exposes voids due to phenomena such as instrumentation artifact, ground occlusion, etc. For this reason, this paper proposes a multiattention generative adversarial network model to fill the voids. In this model, a multiscale feature fusion generation network is proposed to initially fill the voids, and then a multiattention filling network is proposed to recover the detailed features of the terrain surrounding the void area, and the channel-spatial cropping attention mechanism module is proposed as an enhancement of the network. Spectral normalization is added to each convolution layer in the discriminator network. Finally, the training of the model by a combined loss function, including reconstruction loss and adversarial loss, is optimized. Three groups of experiments with four different types of terrains, hillsides, valleys, ridges and hills, are conducted for validation of the proposed model. The experimental results show that (1) the structural similarity surrounding terrestrial voids in the three types of terrains (i.e., hillside, valley, and ridge) can reach 80–90%, which implies that the DEM accuracy can be improved by at least 10% relative to the traditional interpolation methods (i.e., Kriging, IDW, and Spline), and can reach 57.4%, while other deep learning models (i.e., CE, GL and CR) only reach 43.2%, 17.1% and 11.4% in the hilly areas, respectively. Therefore, it can be concluded that the structural similarity surrounding the terrestrial voids filled using the model proposed in this paper can reach 60–90% upon the types of terrain, such as hillside, valley, ridge, and hill.

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Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites

Cited by 27 publications

References 55 publications

Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery

Regional-Scale Systematic Mapping of Archaeological Mounds and Detection of Looting Using COSMO-SkyMed High Resolution DEM and Satellite Imagery

Towards ice thickness inversion: an evaluation of global DEMs by ICESat-2 in the glacierized Tibetan Plateau

Voids Filling of DEM with Multiattention Generative Adversarial Network Model

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