Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation
To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2023.2203953 |
| _version_ | 1797678468670947328 |
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| author | Yanyan Li Linye Li Chuanfa Chen Yan Liu |
| author_facet | Yanyan Li Linye Li Chuanfa Chen Yan Liu |
| author_sort | Yanyan Li |
| collection | DOAJ |
| description | To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs, including SRTM1, AW3D30, and COPDEM30. Taking LiDAR DTM as the ground truth, the accuracy of the GDEMs before and after VB correction is assessed, as well as two existing GDEMs including MERIT and FABDEM. Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types, with the largest biases of 21.5 m for SRTM1, 26.3 m for AW3D30, and 27.18 m for COPDEM30. Taking data randomly sampled from the corrected area as the training points, the proposed model reduces the mean errors (root mean square errors) of the three GDEMs by 98.8%−99.9% (55.1%−75.8%) in the three forests. When training data have the same forest type as the corrected GDEM but under different local situations, the proposed model lowers the GDEM errors by at least 76.9% (44.1%). Furthermore, our corrected GDEMs consistently outperform the existing GDEMs for the two cases. |
| first_indexed | 2024-03-11T23:00:12Z |
| format | Article |
| id | doaj.art-12639b9c756048fe81a9acc9b9cf4820 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:12Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-12639b9c756048fe81a9acc9b9cf48202023-09-21T14:57:13ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-011611568158810.1080/17538947.2023.22039532203953Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelationYanyan Li0Linye Li1Chuanfa Chen2Yan Liu3Shandong University of Science and TechnologyShandong University of Science and TechnologyShandong University of Science and TechnologyShandong University of Science and TechnologyTo remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs, including SRTM1, AW3D30, and COPDEM30. Taking LiDAR DTM as the ground truth, the accuracy of the GDEMs before and after VB correction is assessed, as well as two existing GDEMs including MERIT and FABDEM. Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types, with the largest biases of 21.5 m for SRTM1, 26.3 m for AW3D30, and 27.18 m for COPDEM30. Taking data randomly sampled from the corrected area as the training points, the proposed model reduces the mean errors (root mean square errors) of the three GDEMs by 98.8%−99.9% (55.1%−75.8%) in the three forests. When training data have the same forest type as the corrected GDEM but under different local situations, the proposed model lowers the GDEM errors by at least 76.9% (44.1%). Furthermore, our corrected GDEMs consistently outperform the existing GDEMs for the two cases.http://dx.doi.org/10.1080/17538947.2023.2203953vegetation biasterrain parameterelevation correctionmachine learning |
| spellingShingle | Yanyan Li Linye Li Chuanfa Chen Yan Liu Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation International Journal of Digital Earth vegetation bias terrain parameter elevation correction machine learning |
| title | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation |
| title_full | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation |
| title_fullStr | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation |
| title_full_unstemmed | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation |
| title_short | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation |
| title_sort | correction of global digital elevation models in forested areas using an artificial neural network based method with the consideration of spatial autocorrelation |
| topic | vegetation bias terrain parameter elevation correction machine learning |
| url | http://dx.doi.org/10.1080/17538947.2023.2203953 |
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