Mustafa K. Alemam, Bin YONG, Abubakar Sani-Mohammed
ABSTRACT The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.
{"title":"A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy","authors":"Mustafa K. Alemam, Bin YONG, Abubakar Sani-Mohammed","doi":"10.2478/arsa-2023-0009","DOIUrl":"https://doi.org/10.2478/arsa-2023-0009","url":null,"abstract":"ABSTRACT The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.","PeriodicalId":8435,"journal":{"name":"Artificial Satellites","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aleksander Partyka, Jolanta Nastula, Justyna Śliwińska, Tomasz Kur, Malgorzata Wińska
ABSTRACT The main aim of this study is to evaluate the usefulness of Institute of Geodesy at Graz University of Technology (ITSG) daily gravity field models in the determination of hydrological angular momentum (HAM) at nonseasonal time scales. We compared the equatorial components (χ 1 and χ 2 ) of HAM calculated with the ITSG daily gravity field models (ITSG-Gravity Recovery and Climate Experiment [ITSG-GRACE] 2016 and ITSG-GRACE 2018) with HAM and sea-level angular momentum (SLAM) from hydrological land surface discharge model (LSDM) and the hydrological signal in the polar motion excitation (known as geodetic residuals [GAO]). Data from ITSG have a daily temporal resolution and allow us to determine oscillations with higher frequencies than the more commonly used monthly data. We limited our study to the period between 2004 and 2011 because of the gaps in GRACE observations before and after this period. We evaluated HAM obtained from ITSG GRACE models in spectral and time domains and determined the amplitude spectra of the analyzed series in the spectral range from 2 to 120 days. Our analyses confirm the existence of a sub-monthly signal in the HAM series determined from ITSG daily data. We observed a similar signal in LSDM-based HAM, but with notably weaker amplitudes. We also observed common peaks around 14 days in the amplitude spectra for the GAO- and ITSG-based series, which may be related to the Earth’s tides. ITSG daily gravity field models can be useful to determine the equatorial components of HAM at nonseasonal time scales.
{"title":"Analysis of The Itsg-Grace Daily Models in The Determination of Polar Motion Excitation Function","authors":"Aleksander Partyka, Jolanta Nastula, Justyna Śliwińska, Tomasz Kur, Malgorzata Wińska","doi":"10.2478/arsa-2023-0008","DOIUrl":"https://doi.org/10.2478/arsa-2023-0008","url":null,"abstract":"ABSTRACT The main aim of this study is to evaluate the usefulness of Institute of Geodesy at Graz University of Technology (ITSG) daily gravity field models in the determination of hydrological angular momentum (HAM) at nonseasonal time scales. We compared the equatorial components (χ 1 and χ 2 ) of HAM calculated with the ITSG daily gravity field models (ITSG-Gravity Recovery and Climate Experiment [ITSG-GRACE] 2016 and ITSG-GRACE 2018) with HAM and sea-level angular momentum (SLAM) from hydrological land surface discharge model (LSDM) and the hydrological signal in the polar motion excitation (known as geodetic residuals [GAO]). Data from ITSG have a daily temporal resolution and allow us to determine oscillations with higher frequencies than the more commonly used monthly data. We limited our study to the period between 2004 and 2011 because of the gaps in GRACE observations before and after this period. We evaluated HAM obtained from ITSG GRACE models in spectral and time domains and determined the amplitude spectra of the analyzed series in the spectral range from 2 to 120 days. Our analyses confirm the existence of a sub-monthly signal in the HAM series determined from ITSG daily data. We observed a similar signal in LSDM-based HAM, but with notably weaker amplitudes. We also observed common peaks around 14 days in the amplitude spectra for the GAO- and ITSG-based series, which may be related to the Earth’s tides. ITSG daily gravity field models can be useful to determine the equatorial components of HAM at nonseasonal time scales.","PeriodicalId":8435,"journal":{"name":"Artificial Satellites","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：