*4.3. Land-Sea Coral Reefscape Mapping at the Class Scale*

As might be logically expected, the best multispectral combination relied on the full optical dataset, from Coastal to NIR2. Relatively to the BGR discrete prediction, the discrimination between grass, wood and road classes were significantly refined. These findings might easily be explained if the NIR enhancement can capture the higher reflectance of both the chlorophyll-laden and the tar/asphalt -made classes better than the VIS one [30].

The DSM fusion with the optical dataset strongly improved the roof and the soil detection. This boosting was correlated with the decrease in misclassification with topographically lower road and topographically higher dry vegetation, respectively. The knowledge of the elevation component within the landscape therefore helped separate human-made, on the one hand, and natural features, on the other hand, that are spectrally similar in the optical range [30].

The best superspectral combination relied on the merge of the optical dataset with the MIR8 (2295–2365 nm). The positive effect at the class scale was tangible with grass, wood, roof, road and soil classes. Compared to the optical dataset, the roof and soil classes were better isolated. The roof outcome might stem from the MIR8 spectral fitting with the higher reflectance of the Polynesian roof made of oxidized-galvanized steel metal (0.34 reflectance) than the optical one (0.19 reflectance) [30]. The soil gain might also be due to MIR8 matching more closely with the higher reflectance (0.44) than the optical one (0.28) (see "brown loam" in [28]). These explanations were also supported by the decline in both roof and soil misclassifications with road, that displays a low MIR8 reflectance of 0.08 [30].

The DSM influence showed a better OA with the combination of the optical dataset with the MIR1 (1195–1225 nm). This optimum simply reinforced its positive effect on the same previous classes, suggesting that the additional elevation information was relatively redundant to this coming from the MIR.

Concerning the coral reefs, the successive integration of the optical bands and the DSM to the BGR dataset, slightly but consistently, strengthened their detection. The addition of the Coastal and yellow bands favoured the coral reefs' separability among other benthic features given the refinement in spectral signatures [11]. The benthic terrain information was also profitable due to the robustness of the depth proxy for delineating benthos' ecophysiological belts [23]. In view of the neat classification of the coral reefs along the lagoon width (Figures 5–8), further research should divide the coral reefs' current class according to their landscape position (fringing, barrier and outer reefs), and their morphology (encrusting, branching, massive, tabular, columnar, etc.).
