Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shutt...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-02-01
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| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2022-0455 |
| _version_ | 1797848722454872064 |
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| author | Saberi Azim Kabolizadeh Mostafa Rangzan Kazem Abrehdary Majid |
| author_facet | Saberi Azim Kabolizadeh Mostafa Rangzan Kazem Abrehdary Majid |
| author_sort | Saberi Azim |
| collection | DOAJ |
| description | Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively. |
| first_indexed | 2024-04-09T18:32:13Z |
| format | Article |
| id | doaj.art-b6d1eadf7ffb480d9e1654c5755447b8 |
| institution | Directory Open Access Journal |
| issn | 2391-5447 |
| language | English |
| last_indexed | 2024-04-09T18:32:13Z |
| publishDate | 2023-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj.art-b6d1eadf7ffb480d9e1654c5755447b82023-04-11T17:07:16ZengDe GruyterOpen Geosciences2391-54472023-02-011511031310.1515/geo-2022-0455Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)Saberi Azim0Kabolizadeh Mostafa1Rangzan Kazem2Abrehdary Majid3Department of RS and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IranDepartment of RS and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IranDepartment of RS and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, IranUniversity West, Trollhättan, SwedenSatellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.https://doi.org/10.1515/geo-2022-0455srtmaster demdem accuracy assessmentoptimization algorithmdem accuracy improvement |
| spellingShingle | Saberi Azim Kabolizadeh Mostafa Rangzan Kazem Abrehdary Majid Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) Open Geosciences srtm aster dem dem accuracy assessment optimization algorithm dem accuracy improvement |
| title | Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) |
| title_full | Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) |
| title_fullStr | Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) |
| title_full_unstemmed | Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) |
| title_short | Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran) |
| title_sort | accuracy assessment and improvement of srtm aster fabdem and merit dems by polynomial and optimization algorithm a case study khuzestan province iran |
| topic | srtm aster dem dem accuracy assessment optimization algorithm dem accuracy improvement |
| url | https://doi.org/10.1515/geo-2022-0455 |
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