Abstract
Information about forest structures is becoming crucial to earth's global carbon cycle, forest habitats, and biodiversity. The Global Ecosystem Dynamics Investigation (GEDI) provides 25-m diameter footprints of the surface for 3-D structure measurements. The main goal of this study is to compare 12 031 footprints of GEDI data with other airborne and spaceborne digital elevation models (DEMs) for Southwest Spain. Ground elevation differences [elevation of the lowest mode (ELM)] are analyzed by comparing GEDI measurements with airborne laser scanning (ALS) LiDAR- and TanDEM-X-derived DEMs. The vertical structure (RH100) is compared to the ALS LiDAR measurement. Ten zones are analyzed, considering different degrees of coverage and slopes. We achieved a root mean square error (RMSE) of 6.13 m for the ELM when comparing GEDI and LiDAR data and an RMSE of 7.14 m when comparing GEDI and TanDEM-X data. For some of the studied areas, these values were considerably smaller, with RMSE values even lower than 1 m. For the RH100 metric, an RMSE of 3.56 m was achieved when comparing GEDI and LiDAR data, but again with a minimum value of 2.09 m for one zone. The results show a clear relation to coverage and slope, especially for the latter. This work also evaluates the positional uncertainty of GEDI footprints, shifting them ±10 and ±5 m along and across the track of the satellite orbit and their intermediate angular positions. The outcomes reveal a strong tendency to obtain better results in the ELM when setting the footprint to 270° and displacing it within 10 m of its positional uncertainty in comparison with the LiDAR and TanDEM-X data.
Highlights
I N RECENT years, LiDAR technology has become a powerful tool for the study of the earth’s surface from a 3-D perspective
From the analysis of the elevation of the lowest mode (ELM), we find that the percentage of best shifts located at 10-270° is notably larger in both comparisons with LiDAR and TDX data
If we focus on the percentages of the root mean square error (RMSE) with respect to the RH100 value, our values are better than those of Healey et al [40] since our maximum RMSE% (44%) is their minimum and we have zones with errors up to five times better than this error
Summary
I N RECENT years, LiDAR technology has become a powerful tool for the study of the earth’s surface from a 3-D perspective. LiDAR remote sensing from three platforms— ground, airborne, and spaceborne—has the potential to acquire direct 3-D measurements of the forest canopy that are useful for estimating a variety of forest inventory parameters [1]. Manuscript received November 11, 2020; revised February 16, 2021; accepted May 12, 2021.
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