# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 4 Discussion
Apart from its reduced saturation in forest sites as compared to spectral indices (e.g., NDVI) [107], LAI derived predictors played a key role in the forest habitats discrimination. In fact, its derived temporal statistics resulted in high importance values within the classification model (annual standard deviation LAI, annual delta LAI, and Sentinel-2 MSI LAI value at yearly month 05), being even higher than single spectral predictors. Overall, this study demonstrates the suitability of the Sentinel-2 MSI derived information for the separability of broad cover forest habitat types (i.e., EUNIS II level, based on plant functional traits), as well as for the identification of detailed EUNIS classification types (i.e., III level), based on their ecological features (i.e., species composition) [55]. The Gini index confirms how the environmental predictors remarkably contribute to the final habitat classification (see also [58]), for both the EUNIS II and III levels (see Table 10) [29; 108]. In terms of explanatory variable importance, the results show how the variability of climatic and geographic pattern of the Italian territory play a paramount role in affecting the plant community trait responses and, consequently, determining a wide diversity of forest habitat types along the peninsula, which includes some of most important forest ecosystems in southern Europe (i.e., the Mediterranean biogeographical region) [109]. The high elevation-relief ratio between elevation versus distance from the shoreline, and the presence of high mountain ranges (i.e., reaching a maximum of over 2500 m for the Apenning and 4500 in the Alps), are all considered to be determiningfactors that affect the bio-climate in Italy (i.e., from Mediterranean to Alpine through the Continental climate zones) [110]. According to the hypotheses that was proposed by [PERSON] & [PERSON] [50] regarding the proportional increasing of importance of the topo-climatic variables with the increasing of study area and pixels grain size, a different importance weight between environmental and spectral predictors was observed. However, looking at the predictors importance at EUNIS II level of classification, our results show that the information related to plant phenology (leaf longevity, greening, flowering, browning, and leaf senescence) estimated from EO spectral and temporal predictors plays an important role in the separability of broadleaved deciduous, broadleaved evergreen, and needleleaved forests, according to [108]. The mismatching between the classified pixels and not classified pixels along the mountain ranges are due to the undetermined values of spectral variables, where cloud cover and shadows are affected by both topographic (i.e., slope and aspect) and climate conditions [111]. The results of classified tiles show an underestimation of needleleaved forests in central and southern Italy (see panel a of Figure 6), where most of them are aforested areas not available in the vegetation database (see Table 3). The natural distribution of native conifer forests in central and southern Italy is limited by environmental and climatic conditions and it often overlaps the broadleaved forests belts [112]. In the Apennine needleleaved forests, there are scarce data on the impact of forestation/afforestation on the indigenous stands (eg. forests of _Ppinea_, _Phalepensis_, _P. nigra_, and _Pica abies_ in northern Apennine), with the exception of the well-known natural mixed coniferous-broadleaved forests [113; 114], such as the woods with European silver fir (_A. alba_) in southern Italy, or Bosnian Pine (_P. hedleecriii_) in the southernmost western part of the peninsula. Indeed, aforested stands are excluded from most of the surveys from botanists compiling biogeographic archives, thus determining a geographical bias [30; 115] with an underestimation of the aforested coniferous stands in the Apennine.