# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 1 Introduction
The requirements of global, continental, and national policies on environment sustainability give a new impulse in this direction [39; 40], as stated in the European Biodiversity Strategy 2030, a core part of the European Green Deal, and in the 2030 Agenda for Sustainable Development [41; 42]. The increasing demand from national institutions for updated information to monitor ecosystems and detect their changes in time and space plays a crucial role in demonstrating spatial products as an essential tool for biodiversity assessments [28; 43]. In this direction, the EU Copernicus Programme, especially through the Copernicus Land Monitoring Service (CLMS) [44] and the launch of Sentinel Earth observation satellite constellations, provides information services and it is promoting and supporting common frameworks for an updated land environmental monitoring at a European (local and in situ) [45; 46] and global scale [47]. In the last decades, the habitat distribution modelling (HDM) has been used to predict the distribution of habitats across landscapes using their relationship with the environ mental variables, similarly to the species distribution models [48; 49; 50]. Although HDM often uses environmental big data, such as climate or geomorphology variables, to detect the habitat distribution, only recently has their combination with the EO data been applied [50; 51; 52; 53]. Moreover, many research initiatives experimented with several multidisciplinary approaches, including: the use of ensemble modelling (i.e., Random forests) for mapping individual tree species at high spatial resolution [50], the hierarchical habitat classification models using very high resolution satellite optical multispectral data [54], and the evaluation of input features for tree species classification [55]. Along with supporting policies on environment sustainability using spatial products on biodiversity assessments, comes the need for procedures to generate habitat mapping products that could cover wide geographical areas with standard classification coding and legend, and reasonably undertake a multidisciplinary approach combining big data analytics, ecological modelling, and Earth Observation data, providing unprecedented insights for the monitoring of Sustainable Development Goals (SDGs) indicators [56]. This article shows a first result of a novel approach for a spatially explicit habitat mapping of forest in Italy using a supervised machine learning model and the combination of vegetation dataset, high resolution EO data, and environmental variables. The obtained results could be useful for monitoring the spatial patterns of ecosystems in space and time. The specific objectives of this study are: (i) to present a procedure to generate habitat mapping products combining environmental, EO, and vegetation big data with high spatial and thematic resolution; (ii) to evaluate the importance of predictor variables deriving from different data source for the classification of forest habitat types; and, (iii) to demonstrate the effectiveness of the developed hierarchical classification scheme for the habitat identification in the Italian national territory.