## 4 Discussion
### DEMs Evaluation  
Evaluation of nine global DEMs over the study area using LVIS surveys and the levelling dataset is summarised in Tables 4 and 5 and Figure 15. If the \(1^{\prime\prime}\) DSMs are compared in terms of the uncertainty represented by MAD (STD* values point towards similar conclusions, though they are less applicable to bi-modal distributions). NASADEM with MAD/STD* \(5.4\)/\(9.3\) m has the best performance according to the LVIS2019 validation, followed by SRTMGL1 and AW3D30 with \(5.5\)/\(9.4\) m and \(5.6\)/\(9.5\) m and ASTER with \(7.9\)/\(13.5\) m (Table 4). However, the mean/median error is much smaller for NASADEM, \(-5.6\)/ \(-3.6\) m, compared to \(-8.6\)/ \(-6.8\) m and \(-8.6\)/ \(-6.4\) m for SRTMGL1 and AW3D30. The mean/median error for ASTER is slightly better than NASADEM, \(-4.8\)/ \(-2.4\) m; however, due to its large MAD, overall, the results are inferior to NASADEM. Validation with all datasets showed NASADEM's superior performance in terms of the percentage of points with absolute differences below \(5\), \(10\), \(15\) and \(20\) m (e.g., \(50\), \(69\), \(82\) and \(9\%\) for LVIS2019). All \(3^{\prime\prime}\) DSMs, SRTMv4, SRTM v3, and TanDEM-X, provided inferior results to \(1^{\prime\prime}\) DSMs. Validation with the four datasets over bare-earth areas in Table 5 shows that for the studied area, NASADEM is superior to all DSMs with mean/MED/MAD/STD\({}^{\bullet}\) of \(-0.4/0.6/2.1/5.4\) m, \(-0.4/0.2/0.8/3.0\) m, and \(-0.5/-0.4/1.3/2.4\) m with the LVIS datasets for 2019, 2005, and 1998, respectively, \(-2.6/-2.0/2.7/7.3\) m with the levelling dataset. For that reason, NASADEM was chosen as the 1\({}^{\prime\prime}\) base DSM for CRDTM2020. Nevertheless, other 1\({}^{\prime\prime}\) DSMs, SRTMGL1 and AW3D30, though providing slightly inferior results in this study, still exhibited good performance in Costa Rica with all four validation datasets. We reviewed the literature for reported accuracy for NASADEM to further assess the quality of our results. The accuracy of ASTER, AW3D30v3.1, MERIT, TanDEM-X, SRTM, and NASADEM DEMs examined in [PERSON] et al., 2020 [41] in four geographic regions was 100-200 km\({}^{2}\) each, with respect to 1 m mesh size LiDAR DEMs. Vertical accuracy as a function of the slope, slope aspect, and land cover were investigated. AW3D30 provided the most robust and accurate DEM for all regions, while NASADEM showed only slightly better accuracy than SRTM, with the overall RMS found to be 6.4, 8.5 and 12.1 m in the three study areas. [PERSON] 2021 [37] evaluated ALOS AW3D30, ASTER GDEM, SRTM, and NASADEM over Mexico with respect to levelling benchmark elevations and a LiDAR-derived DEM. For the second validation method, NASADEM showed a mean absolute difference of 3.1 m, a normalised median absolute deviation 2.7 m, and an RMS 5.2 m. Comparison with benchmark elevations resulted in a mean absolute difference of 3.6 m. Furthermore, ref. [37] performed an analysis of the MAE with respect to slope, land cover, and aspect. [PERSON] et al., 2022 [42] evaluated AW3D30, SRTM-GL1, NASADEM, TanDEM-X, SRTM4, and MERIT DEMs with ICESat-2 laser altimetry data, considered the effects of glacier dynamics, terrain, and DEM misregistration, and found that NASADEM performed the best with a mean error of \(-1.0\) m and an RMS of 12.6 m. [PERSON] et al., 2021 [43] analysed NASADEM and ALOS AW3D30 for the Brazilian Cerrado region using elevations of the 1695 reference stations of the Brazilian Geodetic System. They reported better performance for AW3D30. The mean/STD/RMS/min/max values reported for NASADEM were \(-2.9/8.4/8.9/-109.7/96.8\) m and for AW3D30 0.7/6.1/6.1/ \(-108.7/97.8\) m. Furthermore, they showed that the difference between two DEMs is statistically insignificant according to the Cohen effect size and the correlation coefficient data.