Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data
ABSTRACTOpen source Global Digital Elevation Models (GDEMs) serve as an important base for studies in geosciences. However, these models contain vertical errors due to various reasons. In this study, data from two Satellite LiDAR altimetry systems, GEDI and ICESat-2, were used to improve the vertica...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2316113 |
| _version_ | 1797302951408041984 |
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| author | Omer Gokberk Narin Saygin Abdikan Mevlut Gullu Roderik Lindenbergh Fusun Balik Sanli Ibrahim Yilmaz |
| author_facet | Omer Gokberk Narin Saygin Abdikan Mevlut Gullu Roderik Lindenbergh Fusun Balik Sanli Ibrahim Yilmaz |
| author_sort | Omer Gokberk Narin |
| collection | DOAJ |
| description | ABSTRACTOpen source Global Digital Elevation Models (GDEMs) serve as an important base for studies in geosciences. However, these models contain vertical errors due to various reasons. In this study, data from two Satellite LiDAR altimetry systems, GEDI and ICESat-2, were used to improve the vertical accuracy of GDEMs. Three different machine learning methods, namely an Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN), were employed to improve existing DEM data with satellite LiDAR data. The methodology was tested in five areas with varying characteristics. Ground control data were selected from high accuracy DEMs generated from Airborne LiDAR and GNSS data. The use of ANN method improved the vertical accuracy of SRTM data from 6.45 to 3.72 m in Test area-4. Similarly, the CNN method demonstrated an improvement in the vertical accuracy of bare ground SRTM data increasing from 3.4 to 0.6 m in Test area-4. In Test area-5, the ANN method improved the vertical accuracy of SRTM data with slopes between 30 and 60%, increasing from 3.8 to 0.5 m. Notably, the results underscore the successful improvement of GDEMs across all test areas. |
| first_indexed | 2024-03-07T23:45:19Z |
| format | Article |
| id | doaj.art-8eaaabe9308545f790867e36f421eba0 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-07T23:45:19Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-8eaaabe9308545f790867e36f421eba02024-02-19T13:14:17ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2316113Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry dataOmer Gokberk Narin0Saygin Abdikan1Mevlut Gullu2Roderik Lindenbergh3Fusun Balik Sanli4Ibrahim Yilmaz5Department of Geomatics Engineering, Afyon Kocatepe University, Afyonkarahisar, TurkeyDepartment of Geomatics Engineering, Hacettepe University, Ankara, TurkeyDepartment of Geomatics Engineering, Afyon Kocatepe University, Afyonkarahisar, TurkeyDepartment of Geoscience and Remote Sensing, Delft University of Technology, Delft, The NetherlandsDepartment of Geomatics Engineering, Yildiz Technical University, Istanbul, TurkeyDepartment of Geomatics Engineering, Afyon Kocatepe University, Afyonkarahisar, TurkeyABSTRACTOpen source Global Digital Elevation Models (GDEMs) serve as an important base for studies in geosciences. However, these models contain vertical errors due to various reasons. In this study, data from two Satellite LiDAR altimetry systems, GEDI and ICESat-2, were used to improve the vertical accuracy of GDEMs. Three different machine learning methods, namely an Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN), were employed to improve existing DEM data with satellite LiDAR data. The methodology was tested in five areas with varying characteristics. Ground control data were selected from high accuracy DEMs generated from Airborne LiDAR and GNSS data. The use of ANN method improved the vertical accuracy of SRTM data from 6.45 to 3.72 m in Test area-4. Similarly, the CNN method demonstrated an improvement in the vertical accuracy of bare ground SRTM data increasing from 3.4 to 0.6 m in Test area-4. In Test area-5, the ANN method improved the vertical accuracy of SRTM data with slopes between 30 and 60%, increasing from 3.8 to 0.5 m. Notably, the results underscore the successful improvement of GDEMs across all test areas.https://www.tandfonline.com/doi/10.1080/17538947.2024.2316113Global digital elevation modelsGEDIICESat-2machine learning |
| spellingShingle | Omer Gokberk Narin Saygin Abdikan Mevlut Gullu Roderik Lindenbergh Fusun Balik Sanli Ibrahim Yilmaz Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data International Journal of Digital Earth Global digital elevation models GEDI ICESat-2 machine learning |
| title | Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data |
| title_full | Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data |
| title_fullStr | Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data |
| title_full_unstemmed | Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data |
| title_short | Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data |
| title_sort | improving global digital elevation models using space borne gedi and icesat 2 lidar altimetry data |
| topic | Global digital elevation models GEDI ICESat-2 machine learning |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2316113 |
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