Evaluation of the Vertical Accuracy of Open Global DEMs over Steep Terrain Regions Using ICESat Data: A Case Study over Hunan Province, China

Liu, Zhiwei; Zhu, Jianjun; Fu, Haiqiang; Zhou, Cui; Zuo, Tingying

doi:10.3390/s20174865

Cited by 44 publications

(27 citation statements)

References 63 publications

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“…It means that downsampling will reduce the quality of DEM, especially in mountainous areas [35]. It also demonstrates the advantages of high-resolution DEM in describing terrain details [58]. In the other two study areas with the reference points mainly distributed in flat areas, the overall quality of the 12-and 30-m were very close.…”

Section: Overall Qualitymentioning

confidence: 70%

“…Positive values of ME mean that the elevation of these DEMs is generally higher than that of the reference. Part of the reason is the influence of the vegetation canopy, where short-wavelength SAR or optical satellite sensors cannot penetrate [58]. This overestimation was noted in previous studies [32,35,59,60].…”

Section: Overall Qualitymentioning

confidence: 88%

“…However, the results of this study are the opposite of the theory. Part of the reason is that the fusion of multi-baseline and multi-aspect TanDEM-X DEMs improves the quality of the final TanDEM-X DEM product, which was also discussed by Liu et al [58]. Because the acquisition time of the images used to generate the DEMs is different, we should also consider the effects of changes in vegetation and terrain over a period of time on experimental results.…”

Section: Overall Qualitymentioning

confidence: 92%

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

2021

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Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented accuracy. These global datasets are often used for local applications and the quality of DEMs affects the results of applications. Many researchers have assessed and compared the quality of global DEMs on a local scale. To provide some additional insights on quality assessment of 12- and 30-m resolution TanDEM-X DEMs, 30-m resolution ASTER GDEM and 30-m resolution SRTM, this study assessed differences’ performance in relation to not only geographical features but also the ways in which DEMs have been created on selected Chinese sites, taking ICESat/GLAS points with 14-cm absolute vertical accuracy but size of 70-m diameter and 12-m resolution TanDEM-X DEM with less than 10-m absolute vertical accuracy as the reference data for comprehensive quality evaluation. When comparing the three 30-m DEMs with the reference DEM, an improved Least Z-Difference (LZD) method was applied for co-registration between models, and Quantile–Quantile (Q-Q) plot was used to identify if the DEM errors follow a normal distribution to help choose proper statistical indicators accordingly. The results show that: (1) TanDEM-X DEMs have the best overall quality, followed by SRTM. ASTER GDEM has the worst quality. The 12-m TanDEM-X DEM has significant advantages in describing terrain details. (2) The quality of DEM has a strong relationship with slope, aspect and land cover. However, the relationship between aspect and vertical quality weakens after data co-registration. The quality of DEMs gets higher with the increasing number of images used in the fusion process. The quality in where slopes opposite to the radar beam is the worst for SRTM, which could provide a new perspective for quality assessment of SRTM and other DEMs whose incidence angle files are available. (3) Systematic deviations can reduce the vertical quality of DEM. The differences have non-normal distribution even after co-registration. For researchers who want to know the quality of a DEM in order to use it in further applications, they should pay more attention to the terrain factors and land cover in their study areas and the ways in which the DEM has been created.

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Section: Overall Qualitymentioning

confidence: 70%

Section: Overall Qualitymentioning

confidence: 88%

Section: Overall Qualitymentioning

confidence: 92%

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

2021

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“…How to effectively evaluate DSM accuracy is a basic topic of DSM research when there are multiple DSMs coexisting. To reveal and verify the horizontal and vertical accuracies of DSM, many studies used high-precision ground observations as reference or compared the DSM with higher precision DSM or DEM [25][26][27][28][29][30][31]. Now, GFDM satellite data and GF-7 satellite data are gradually released and applied, and their accuracy needs to be verified and evaluated urgently.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery

et al. 2021

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Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel.

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“…However, field GCP collection is highly expensive and time-consuming, thereby being suited for small-scale areas. Some researchers suggested the use of Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data as GCPs [23][24][25][26][27][28][29][30], because ICESat/GLAS data are freely available and have a near-global coverage. More importantly, these data are highly accurate under good conditions.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration

Chen

Yang

2020

Remote Sensing

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Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.

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Evaluation of the Vertical Accuracy of Open Global DEMs over Steep Terrain Regions Using ICESat Data: A Case Study over Hunan Province, China

Cited by 44 publications

References 63 publications

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

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

Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery

Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration

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