## 4 Discussion
This finding confirms the main hypothesis that increasing the number of off-nadir angular measurements from the newly processed high-resolution MISR MSMD data would yield improved canopy structural parameters estimates and lower estimation errors as compared to the nadir only measurements. The MISR-HR BRF 36 data channels estimation model performance was slightly better, but comparable to the MISR-HR RPV all bands (12 bands) models for both forest structure parameters. The results showed a small rRMSE (1-2%) difference between these two models and differences were significant. In addition, the use of a non-parametric machine learning model such as Random Forest may have assisted to extract the non-linear anisotropic information present in the BRF dataand synthesized by the RPV parameters. Hence the study supports the findings that RPV parameters contain vegetation structural information [26; 48; 55], which can be used in vegetation structural studies. These results could warrant making use of all the MISR-HR RPV all bands (12 bands) over MISR-HR BRF 36 data channels MISR-HR BRF 36 data channels which would mean data dimensionality reduction. Each vegetation types had important difference in the heterogeneity of cover and height, which influenced the per vegetation type model results. The savanna (a spatially heterogeneous and open canopy environment) modelling results, were the best of all the three forest types, with the lowest estimation errors and highest coefficient of determination for H\({}_{\text{mean}}\), rRMSE = 32.28%, and R\({}^{2}\) value of 0.67 (Figure 8a,b). A possible explanation for the better model results in the savanna could be due to its open and heterogeneous canopy structure, resulting in shadows cast by the trees on the grassy background providing a stronger anisotropy signal. These conditions would not be prevalent at the opposite side of the woody canopy cover range, either at closed or no woody canopy cover. The indigenous forest exhibited fair results for the CC parameter which had the lowest estimation error (rRMSE = 38.19%) and an R\({}^{2}\) value of 0.57. However, the generated map (Figure 9) shown that it is difficult to obtain high accuracy in extracting height classes in these savanna environments in South Africa. The limited high precision in the derived map (Figure 9), could simply be due to scale of estimation as a result of the spatial resolution of the data. Nevertheless, the overall findings support the potential use and further research of multi-angular multispectral data in the estimation of vegetation structural parameters at regional scales assuming data continuity (i.e., no data gaps) but might not be the best data to be used within low cover heterogeneous vegetation types like the savannas. However, the models for each vegetation type and both parameters resulted in considerably larger relative estimation errors than in the combined sample models. Amongst the two estimated structural parameters, the mean tree height was retrieved with the highest accuracy (lower error level and higher R\({}^{2}\)) regardless of the tested scenarios. Similar results have been reported in other studies, for instance [44]. These results could possibly be due to the three-dimension nature of tree height better captured by the multi-angular data. Hence, mean tree height maps were produced at 275 m spatial resolution for both study sites, using the Scenario 4 (MISR-HR BRF 36 data channel) predictive model due to its best performance. The map in Figure 9a (Lowveld savanna area) and 10a (iSimangaliso St-Lucia site (i.e., indigenous forest and forest plantations), illustrates the spatial distribution and pattern of the estimated mean tree height across the MISR image Blocks (Path 168 Block 110 and Path 167 Block 113). In both the maps, there are grey regions which denote missing data in the MISR data. These data gaps are as a result of when the MISR aerosol/ surface retrievals failed as a result of cloud or other atmospheric anomalies (i.e., topographic shading).