# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 4 Discussion  
This study proposed an approach to map forest habitats according to EUNIS classification (II and III levels), when considering the great habitat variability of the study area, in order to support the elaboration of conservation strategies and the environmental assessment. It combined nationwide biodiversity database, environmental information, and satellite EO data (Sentinel-2 MSI at 20 m spatial resolution) to train a supervised machine learning model for forest habitat classification. Previous studies on forest habitat mapping in Italy used satellite data for classification at coarse spatial and thematic resolutions, often investigating small areas (regional or sub regional scale areas) [37; 90; 103] or focusing on single tree species [96]. Figure 6: Local spatial extent of: (**a**) T1 broadleaved deciduous forest on central Italy; (**b**) T2 broadleaved evergreen forest along Tyrrhenian coast on central Italy; and, (**c**) T3 needleleaved forest habitat in the Alps on northern Italy. For the code reference of the legend, see Table 3.  
Spectral and temporal features from Sentinel-2 MSI big datacubes were fully exploited in order to generate spectral and temporal predictor dataset (e.g., the estimation of phenological metrics). Such predictors dataset, combined with geomorphological, climate, and pedological variables, allowed for training a supervised machine learning model using a two-stages hierarchical classification scheme. A potential feature of the current study concerns the updatability of the approach proposed, which allows for the habitat maps to be updated using EO temporal predictors, providing a dynamic and key contribution to ecological monitoring and assessment. The change detection metrics derived from the proposed procedure could be relevant for environmental issues, having a strong impact on the conservation status of habitats [104]. The approach can also improve predictive distribution models of endangered animal species, which are based on the use of high resolution EO data to identify fine-scale habitat features [105]. Moreover, it could be useful to evaluate the effects of global changes on resilience and species composition of habitats [106]. When considering that Sentinel-2 satellite mission systematically acquires data worldwide over land, the proposed approach can be reproduced and extended to all of the vegetated geographical areas in the earth, thanks to the existing high resolution layers (i.e., Copernicus product) [44] and the availability of plant species archive (i.e., EVA) [17]. Moreover, in the absence of high resolution land cover and thematic maps, they could be generated from the time series analysis of satellite derived vegetation indices (e.g., NDVI, EVI, and LAI). The evaluation of the importance of the predictor categories used for the classification models was one of the scopes of this research. Using the VIF and correlation analyses, a reduced set of predictors was selected to remove the high multi-collinearity for each classification model (see Table 10). The spectral predictors that result in a higher importance for discriminating forest habitats are those related to plant greening (Sentinel-2 MSI EVI value at yearly month 04, Sentinel-2 MSI B2 and B12 value at yearly month 03), flowering and browning (Sentinel-2 MSI NDVI value at yearly month 04, 05, and 10, Sentinel-2 MSI RI value at yearly months 03, 04, and 10). This result confirms that the spring and fall seasons show the highest heterogeneity of radiometric signals due to the variability on vegetation growing and senescing pattern along the altitudinal and latitudinal gradients in Italy. Among the spectral bands, B2 (blue) showed the greater Gini index values and resulted in being more frequently selected among the predictors than other spectral bands, while the redness indices (RI and CRI1) were the spectral indices with higher Gini index values and more frequently selected. LAI biophysical information was selected as variable for the estimation of temporal predictors.