Integrating optimal terrain representations from public DEMs using spaceborne LiDAR
Due to differences in data sources and processing methods, the accuracy of Digital Elevation Models (DEMs) varies greatly in different regions, which poses challenges for users in data selection. To address this issue, this study proposes the Optimal Terrain Retrieval (OTR) method, which integrates...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2505631 |
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| Summary: | Due to differences in data sources and processing methods, the accuracy of Digital Elevation Models (DEMs) varies greatly in different regions, which poses challenges for users in data selection. To address this issue, this study proposes the Optimal Terrain Retrieval (OTR) method, which integrates the optimal segments from public DEMs by using spaceborne Light Detection And Ranging (LiDAR). The OTR method involves selecting LiDAR photons, assigning weights, ranking DEMs by error statistics relative to LiDAR benchmarks, extracting and merging the most accurate segments of each DEM. OTR ensures integrating the highest quality data from each DEM while preserving the original data’s integrity. The experiments were conducted in the Loess Plateau, and results show that the OTR-derived DEM (OTRDEM) has a 25.71% lower Mean Absolute Error (MAE) than the leading COP30 and a 25.40% lower RMSE than the FABDEM. Additionally, OTRDEM demonstrates advantages in rough terrain and densely vegetated areas. The proposed method provides a scalable and adaptable approach for DEM optimization for various regions and datasets. It allows for the incorporation of new DEMs without requiring degradation assumptions or extensive training processes, enhancing terrain representation as more LiDAR observation data becomes available. |
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| ISSN: | 1753-8947 1753-8955 |