ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

27 citations as recorded by crossref.

1. Integration of Structural Characteristics from GEDI Waveforms for Improved Forest Type Classification M. McClure et al. https://doi.org/10.3390/rs16244776
2. Optimizing GEDI Footprint Selection for Forest Canopy Height Estimation in the Western Himalaya Using Multi-sensor Data A. Paygude et al. https://doi.org/10.1007/s12524-025-02349-y
3. Hybrid model for estimating forest canopy heights using fused multimodal spaceborne LiDAR data and optical imagery S. Wang et al. https://doi.org/10.1016/j.jag.2023.103431
4. Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR Q. Yu et al. https://doi.org/10.1088/2752-664X/ad39f2
5. Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks Y. He et al. https://doi.org/10.1016/j.isprsjprs.2026.04.022
6. Role of LiDAR remote sensing in identifying physiognomic traits of alpine treeline: a global review J. Mathew et al. https://doi.org/10.1007/s42965-023-00317-6
7. Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions R. Chen et al. https://doi.org/10.3390/f15112024
8. Canopy height and biomass distribution across the forests of Iberian Peninsula Y. Su et al. https://doi.org/10.1038/s41597-025-05021-9
9. Evaluating the accuracy of ground elevation estimates from GEDI satellite LiDAR in forested terrain M. Cățeanu https://doi.org/10.1080/10095020.2025.2555385
10. Performance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia S. Dhargay et al. https://doi.org/10.3390/rs14153615
11. Large-scale monitoring of inland water surface levels with GEDI data: an operational cloud-based approach in Google Earth Engine A. Hamoudzadeh et al. https://doi.org/10.1080/15481603.2025.2483027
12. Mountains of error: UAV LiDAR benchmarking of GEDI and GEDI-derived global canopy height products in steep Himalayan forests N. Mishra & P. Singh https://doi.org/10.1016/j.jag.2026.105203
13. Evaluating Lorenz entropy for tropical forest discrimination using GEDI and supervised machine learning approach N. Mashhadi & A. Sanchez-Azofeifa https://doi.org/10.1016/j.ecolind.2025.113374
14. Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass R. Ceriani et al. https://doi.org/10.1016/j.jag.2025.104614
15. Latin Hypercube Sampling improves estimation of the GEDI-based forest canopy height: a case study in Jiangxi Province, China Y. Shu et al. https://doi.org/10.1080/01431161.2025.2557583
16. Spaceborne LiDAR for characterizing forest structure across scales in the European Alps L. Mandl et al. https://doi.org/10.1002/rse2.330
17. Quantification of GEDI Geolocation Error and Its Influence on Elevation and Canopy Height C. Yang et al. https://doi.org/10.1109/JSTARS.2024.3461843
18. Machine Learning-Based Analysis of Forest Vertical Structure Dynamics Using Multi-Temporal UAV Photogrammetry and Geomorphometric Indicators A. Yiğit https://doi.org/10.3390/f17020258
19. Application of Random Forest Method Based on Sensitivity Parameter Analysis in Height Inversion in Changbai Mountain Forest Area X. Wang et al. https://doi.org/10.3390/f15071161
20. Maximum tree height in European Mountains decreases above a climate-related elevation threshold P. Gelabert et al. https://doi.org/10.1038/s43247-024-01246-5
21. Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island K. Lahssini et al. https://doi.org/10.1080/07038992.2024.2351004
22. Accuracy assessment of GEDI terrain elevation, canopy height, and aboveground biomass density estimates in Japanese artificial forests H. Li et al. https://doi.org/10.1016/j.srs.2024.100144
23. Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture K. Lahssini et al. https://doi.org/10.3389/frsen.2024.1484900
24. Improving GEDI geolocation accuracy with ultra-high-resolution DTM data in Japan H. Li et al. https://doi.org/10.1080/15481603.2025.2601327
25. Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data X. Huang et al. https://doi.org/10.1109/TGRS.2023.3240848
26. Satellite-based mapping of canopy fuels at the pan-European scale E. Kutchartt et al. https://doi.org/10.1080/10095020.2024.2429376
27. How to get closer to actual forest stand height using GEDI? A case study in central European Scots pine stands W. Krawczyk & P. Wężyk https://doi.org/10.1080/22797254.2024.2399209