Rectification methods comparison for the ASTER GDEM V2 data using the ICESat/GLA14 data in the Lvliang mountains, China

Zhao, Shengchuan; Wang, Li; Cheng, Weiming; Liu, Haijiang; He, Weican

doi:10.1007/s12665-015-4614-1

Cited by 9 publications

(6 citation statements)

References 61 publications

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“…GDEM-v2 was released in October 2011, which can be downloaded from USGS Global Data Explorer. GDEM-v2 is an upgraded version of GDEM-v1, which is developed using an advanced algorithm and more data sources (Zhao et al, 2015). GDEM-v2 may replace GDEM-v1 in researches and applications in the future.…”

Section: Dem Datasetsmentioning

confidence: 99%

Performance comparison among typical open global DEM datasets in the Fenhe River Basin of China

Zhao

et al. 2021

European Journal of Remote Sensing

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This paper aims to compare the performance of typical open global DEM datasets by using the indexes of elevation error, relative error and hydrologic network. Taking Fenhe River Basin of China as the study area, this research made quantitative performance comparison among four typical open global DEM datasets including SRTM data with 1" (SRTM1) and 3" (SRTM3) resolutions, ASTER Global DEM data at the 2nd version (GDEM-v2) and ALOS World 3D-30 m (AW3D) data. Through process and selection, more than 80,000 ICESat/GLA14 points were used as the reference data, and the elevation error was computed and compared accordingly. Furthermore, relative error was analyzed using slope values, and false slope ratio index was computed and categorically compared. Finally, the hydrologic networks extracted from the four DEM datasets were compared to the reference hydrologic network acquired by visual interpretation from remote sensing images. The research results show that the AW3D has the best performance, which is approximate to but a little better than SRTM1. The performance of SRTM3 and GDEM-v2 is similar, which are much worse than that of AW3D and SRTM1, and the performance of GDEM-v2 is the worst of all.

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Section: Dem Datasetsmentioning

confidence: 99%

Performance comparison among typical open global DEM datasets in the Fenhe River Basin of China

Zhao

et al. 2021

European Journal of Remote Sensing

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“…Due to the limited number of weather stations in the study area, data from the NCDC was used in this study (Table S1). A spatial interpolation was performed to obtain the data for the study area by using the Kriging interpolation method, as in other case studies [5,[50][51][52]. The spatial distribution of the 5-day continuous rainfall is illustrated in Figure 4A.…”

Section: Indicators and Criteriamentioning

confidence: 99%

Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

Cabrera

Lee

2019

Water

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Flooding is one of the major destructive natural disasters in Davao Oriental, Philippines, and results primarily from a high incidence of typhoons and heavy rainfalls. The main objective of this study was to identify flood-prone risk areas by mapping them based on the integration of multiple indicators, including rainfall, slope, elevation, drainage density, soil type, distance to the main channel and population density. For this purpose, a GIS-based flood risk spatial assessment was conducted by using analytic hierarchy process (AHP), weights by rank (WR) and ratio weighting (RW) frameworks to determine the relative importance of each indicator against another in the province of Davao Oriental. The resulting flood-prone areas by the three methods are validated by comparing with the estimated flood map based on ground truthing points from a field survey. The comparison results show that AHP is the most appropriate method among them to assess flood hazard. The result of the AHP flood risk map shows that 95.99% (5451.27 km2) of Davao Oriental is under low and moderate flood risk. The high and very high flood risk area covers approximately 3.39% (192.52 km2) of the province, primarily in the coastal areas. Thirty-one out of the one hundred eighty-three (31/183) barangays (towns) are at a high to very high risk of flooding at current climate, calling for the immediate attention of decision-makers to develop mitigation strategies for the future occurrence of flooding in Davao Oriental.

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“…Both acquired by global ASTER stereo images, GDEM-v2 is an upgraded version of GDEM-v1 data. It is developed using an advanced algorithm and more data sources (Zhao et al, 2015).…”

Section: Data Sourcesmentioning

confidence: 99%

“…Traditional accuracy assessment to DEM data mainly adopts vertical error based on large accurate ground control points (GCPs), that is, the difference of the elevation values between GCPs and DEMs (Avtar et al, 2015;Hirano et al, 2003). Vertical error can be used to correct the values and improve the quality of the DEM data (Yue et al, 2010;Zhao et al, 2015). However, vertical error mainly focuses on elevation accuracy and seldom considers the errors of the neighbouring pixels, which is called as relative error (Satge et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Relative Error Evaluation to Typical Open Global Dem Datasets in Shanxi Plateau of China

Zhao

Zhang

Cheng

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Produced by radar data or stereo remote sensing image pairs, global DEM datasets are one of the most important types for DEM data. Relative error relates to surface quality created by DEM data, so it relates to geomorphology and hydrologic applications using DEM data. Taking Shanxi Plateau of China as the study area, this research evaluated the relative error to typical open global DEM datasets including Shuttle Radar Terrain Mission (SRTM) data with 1 arc second resolution (SRTM1), SRTM data with 3 arc second resolution (SRTM3), ASTER global DEM data in the second version (GDEM-v2) and ALOS world 3D-30m (AW3D) data. Through process and selection, more than 300,000 ICESat/GLA14 points were used as the GCP data, and the vertical error was computed and compared among four typical global DEM datasets. Then, more than 2,600,000 ICESat/GLA14 point pairs were acquired using the distance threshold between 100&thinsp;m and 500&thinsp;m. Meanwhile, the horizontal distance between every point pair was computed, so the relative error was achieved using slope values based on vertical error difference and the horizontal distance of the point pairs. Finally, false slope ratio (FSR) index was computed through analyzing the difference between DEM and ICESat/GLA14 values for every point pair. Both relative error and FSR index were categorically compared for the four DEM datasets under different slope classes. Research results show: Overall, AW3D has the lowest relative error values in mean error, mean absolute error, root mean square error and standard deviation error; then the SRTM1 data, its values are a little higher than AW3D data; the SRTM3 and GDEM-v2 data have the highest relative error values, and the values for the two datasets are similar. Considering different slope conditions, all the four DEM data have better performance in flat areas but worse performance in sloping regions; AW3D has the best performance in all the slope classes, a litter better than SRTM1; with slope increasing, the relative error for the SRTM3 data increases faster than other DEM datasets; so SRTM3 is better than GDEM-v2 in flat regions but worse in sloping regions. As to FSR value, AW3D has the lowest value, 4.37&thinsp;%; then SRTM1 data, 5.80&thinsp;%, similar to AW3D data; SRTM3 has higher value, about 8.27&thinsp;%; GDEM-v2 data has the highest FSR value, about 12.15&thinsp;%. FSR can represent the performance of correctly creating the earth surface based on DEM data. Hence, AW3D has the best performance, which is approximate to but a little better than SRTM1. The performance of SRTM3 and GDEM-v2 is similar, which is much worse than AW3D and SRTM1, and the performance of GDEM-v2 is the worst of all. Originated from the DEM dataset with 5m resolution, AW3D is regarded as the most precise global DEM datasets up to now, so it may exerts more effect in topographic analysis and geographic research. Through analysis and comparison of the relative error for the four open global DEM datasets, this research will provide reference in open global DEM datasets selection and applications in geosciences and other relevant fields.

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Rectification methods comparison for the ASTER GDEM V2 data using the ICESat/GLA14 data in the Lvliang mountains, China

Cited by 9 publications

References 61 publications

Performance comparison among typical open global DEM datasets in the Fenhe River Basin of China

Performance comparison among typical open global DEM datasets in the Fenhe River Basin of China

Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines

Relative Error Evaluation to Typical Open Global Dem Datasets in Shanxi Plateau of China

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