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.

Portable 3D laser scanners are a valuable tool for compiling elaborate digital collections of archaeological objects and analysing the shapes and dimensions of pieces. Although low-cost desktop 3D laser scanners have powerful capacities, it is important to know their limitations. This paper performs an analysis of the uncertainty and repeatability of the NextEngine^TM portable low-cost 3D laser scanner by scanning an object 20 times in two different resolution modes—Macro and Wide. Some dimensions of the object were measured using a digital calliper, and these results were used as the “true” or control data. In comparing the true and the scanned data, we verified that the mean uncertainty in the Macro Mode is approximately half that of the Wide Mode, at ±0.81 mm and ±1.66 mm, respectively. These experimental results are significantly higher than the accuracy specifications provided by the manufacturer. An analysis of repeatability shows that the successive replicates do not match in the same position. The results are better in Macro Mode than in Wide Mode; it is observed that the repeatability factor is slightly larger than the corresponding mode accuracy, with ±0.84 vs. ±0.81 mm in Macro Mode and ±1.82 vs. ±1.66 mm in Wide Mode. We suggest several improvements, such as adding an external reference scale or providing a calibrated object to allow for a self-calibration operation of the scanner.

Circular or directional data are used in disciplines such as meteorology, geomatics, biology, and geology. The analysis of angular data requires special methods that are available in some statistical packages. However, these tools analyze only the angular values and do not include the vector modules, assuming unit vectors in all cases. In this letter, an open-source graphic and statistical package, i.e., VecStatGraphs2D, is described. It works in the R environment and provides statistics and graphics for modules (linear) and azimuths (circular), as well as graphics for the joint analysis of modules and azimuths. QuikSCAT satellite wind data are used to demonstrate some features of the package. QuikSCAT data are non-unit-length vectors, where both azimuth and magnitude (speed) are derived from u and v vector components (vector projections over the x- and y-axes). The example is used to show the seasonal change of winds in the Intertropical Convergence Zone, a key area in the ocean bird migration from the North to South Atlantic oceans.