Characterizing Savanna Tree Canopy Heights Using GEDI and Spatially Continuous Multi-Source Data at a Landscape Level.
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| Title: | Characterizing Savanna Tree Canopy Heights Using GEDI and Spatially Continuous Multi-Source Data at a Landscape Level. |
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| Authors: | Ma, Xiao1 (AUTHOR), Qu, Yajie2,3 (AUTHOR), Chen, Meiyuan2,3 (AUTHOR), Zheng, Guang2,4 (AUTHOR) zhengguang@nju.edu.cn, Xu, Chi1 (AUTHOR), Li, Xiaoxuan2,4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1523. 23p. |
| Subjects: | Tree height, LIDAR, Remote sensing, Carbon cycle, National parks & reserves, Terrain mapping, Biomass estimation, Metadata |
| Geographic Terms: | Kruger National Park (South Africa) |
| Abstract: | Highlights: What are the main findings? Develop an optimized workflow for the filtering of high-quality GEDI footprints. Establish a mapping framework for accurately estimating savanna tree canopy height. What are the implications of the main findings? The proposed method enables the filtering of accurate footprints from GEDI L2A products without relying on high-precision reference data. Savanna tree canopy height prediction requires modeling distinct from that for forests. Accurately mapping tree canopy heights of savanna ecosystems, which account for around 20% of the terrestrial land surface, is of great importance for global biomass estimation, carbon cycling, and biodiversity. The spaceborne lidar of Global Ecosystem Dynamics Investigation (GEDI) has great potential for measuring tree canopy heights in sparse savanna ecosystems due to its implicit three-dimensional structural information. However, the accuracy of the GEDI system may be affected by the random geolocation errors. In this study, we aim to develop a reliable method to mitigate the impact of low-quality and position-biased GEDI footprints. Then we generated 30-m resolution wall-to-wall mapping of tree canopy heights for 2020 by combining GEDI L2A footprints with spatially continuous multi-source information in the Kruger National Park, South Africa. Moreover, we explored the explanatory ability of multi-dimensional features derived from optical, radar, topographic, and artificial intelligence-based images and conducted a comparative analysis of relevant products. Validation results confirmed that integrating quality indicators, incorrect ground elevation estimation assessment, and optical and radar features could significantly improve the accuracy of GEDI-based tree canopy height estimation in savannas (i.e., Pearson's r = 0.51, RMSE = 3.88 m, N = 6276). Compared to existing products, the model trained on comprehensively filtered footprints exhibited higher agreement with reference canopy height model data and lower estimation errors (i.e., Pearson's r = 0.66, RMSE = 4.09 m, N = 10,469). We also found that features incorporating red-edge bands exhibited higher explanatory ability. This study showcases GEDI-based mapping of savanna tree canopy heights and provides a foundation for future large-scale research on savanna ecosystems. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Develop an optimized workflow for the filtering of high-quality GEDI footprints. Establish a mapping framework for accurately estimating savanna tree canopy height. What are the implications of the main findings? The proposed method enables the filtering of accurate footprints from GEDI L2A products without relying on high-precision reference data. Savanna tree canopy height prediction requires modeling distinct from that for forests. Accurately mapping tree canopy heights of savanna ecosystems, which account for around 20% of the terrestrial land surface, is of great importance for global biomass estimation, carbon cycling, and biodiversity. The spaceborne lidar of Global Ecosystem Dynamics Investigation (GEDI) has great potential for measuring tree canopy heights in sparse savanna ecosystems due to its implicit three-dimensional structural information. However, the accuracy of the GEDI system may be affected by the random geolocation errors. In this study, we aim to develop a reliable method to mitigate the impact of low-quality and position-biased GEDI footprints. Then we generated 30-m resolution wall-to-wall mapping of tree canopy heights for 2020 by combining GEDI L2A footprints with spatially continuous multi-source information in the Kruger National Park, South Africa. Moreover, we explored the explanatory ability of multi-dimensional features derived from optical, radar, topographic, and artificial intelligence-based images and conducted a comparative analysis of relevant products. Validation results confirmed that integrating quality indicators, incorrect ground elevation estimation assessment, and optical and radar features could significantly improve the accuracy of GEDI-based tree canopy height estimation in savannas (i.e., Pearson's r = 0.51, RMSE = 3.88 m, N = 6276). Compared to existing products, the model trained on comprehensively filtered footprints exhibited higher agreement with reference canopy height model data and lower estimation errors (i.e., Pearson's r = 0.66, RMSE = 4.09 m, N = 10,469). We also found that features incorporating red-edge bands exhibited higher explanatory ability. This study showcases GEDI-based mapping of savanna tree canopy heights and provides a foundation for future large-scale research on savanna ecosystems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101523 |
