# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 4 Discussion
Conversely, in the Alps (see panel c of Figure 6), the high contribution of vegetation surveys on forest consortia dominated by native tree species of Firs, Larches, and Pines, the low impact of forestation/afforestation activities, give a more accurate classification. Moreover, the evident discrepancy of data that are available for the semi-automatic classification (i.e., response variable) can also be seen in Table 3, where, out of a total of 2281 plots of coniferous forest, only the 24% are within the Mediterranean biogeographical regions (i.e., most of all the Apennine mountain chain). The classification of the 24 forests habitat-types revealed different results for each of the EUNIS II level classes. The overall accuracy for the detection of tree species distribution with spectral indices analysis, obtained within the T2 (91.5%) and T3 (76.2%), is in line with similar studies [55]. The low (67.6%) overall accuracy within T1 EUNIS III classes, is confirmed by previous studies, where the accuracy of the broadleaved deciduous species was always below of the broadleaved evergreen and coniferous forests [55]. The performance of habitat detection using EUNIS classification based on the stepwise approach of pre-defined hierarchical habitat schemes demonstrates that the mismatching between categories depends solely on ecological and biogeographic factors that reflects the real altitudinal gradient and coexistence in natural woodlands in southern Europe, thus revealing a good predicting capability of the method [116]. The misclassification in the confusion matrix of broadleaved deciduous (T1, Table 4) and evergreen (T2) forests (at the EUNIS II level) is mainly due to the similar ecological drivers and partial overlapping pattern at the edge of their own distribution, determining mixed forests along the elevation belt. The same driver on altitudinal contacts acts for the needleleaved forests that partially mismatch the broadleaved deciduous forests on the Alpine chain. Natural forests clearly reflected this condition. In the Apennines, the driver (ecological filtering on biogeographical pattern, see \"Species Pool\" theory) is different, but the results in detection capability are similar. Coniferous forests are relics [114], because of historical and biogeographic transformation, and the needleleaved trees are envelopedin broadleaved forests (_F. sylvatica_), again mismatching the net detection between the two categories. The natural elevation pattern also explains the highest misclassification found between the broadleaved deciduous and broadleaved evergreen habitat-types at the III level of EUNIS classification (Tables 5 and 6). In needleleaved forest stands, Mediterranean and temperate coniferous trees show a net separability in forest classification as the result of a clear distinct chorological and ecological pattern, thus reflecting the quite different species composition (Table 7). Otherwise, _Larix_ forest, the only deciduous confer, was well classified (UA 73.5 % and PA 67.6 %). The misclassification with temperate mountain _Pica_ forest is explained by ecological and altitudinal overlapping of the two habitats, while the coexistence with temperate and sub-mediterranean montane _P. sylvestris-P. nigra_ forest is frequent on stone slopes. Another justification of the misclassification of RF results could be the \"class imbalances\", as proposed by other researchers [30; 55]. The EUNIS detection can be affected by the class with more samples, which is preferred by the RF classifier and, consequently, highlights the importance of an equilibrate sample design [116; 117]. However, when a research includes several targets and refers to a wide extent, the only database available is collected from different sources over a large span of time, and it is difficult to manage (i.e., resampling thematic plots versus geographic). Moreover, in practice, some habitat-types often bring about mixed stands (i.e., see \"Coexistence Theory\") with others, and it is difficult to detect at fine spatial resolution data.