# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 1 Introduction  
Global-scale environmental issues, from climate change to biosphere integrity [1], are creating an intense social pressure and a growing need for information with appropriate reliability and suitable spatial scale (from the local to global analysis and vice versa) that must be provided by the scientific community [2; 3; 4; 5]. Therefore, it is strictly urgent to ensure the integrity of the Bio, Hydro, and Geosphere by following the advance of the high technologies. The High-Tech frontier looks furtherahead than the growing results obtained from environmental sciences research, which, in certain fields, still trudges due to the inaccessibility of large datasets [6]. It is reasonable that the potential availability of a huge amount of \"big data\" in the future will allow for the use of advanced analytic techniques, extracting useful information from different large datasets, including those observing and measuring the ecosystem processes in response to environmental drivers of changes [7]. A multidisciplinary approach, including machine learning techniques, data mining, big data analytics, and ecological modelling, is highly recommended to interpret ecological processes and identify adequate solutions for the Anthropocene environmental issues [8]. However, the use of big data today represents a big challenge, from detailed analysis on specific topics or geographic areas to issues at wider scales and over broader timescales [5]. In light of the \"Biological Diversity\" concept [9; 10], habitats are cardinal pieces for biodiversity quantitative estimation at the local and global scale. They are basic units of ecosystems and biomes, which are identified by abiotic environmental factors, such as climate, geomorphology, pedology, as well as by plant species composition (i.e., vegetation units) [11; 12; 13]. The main criterion that assesses spatial symptoms of habitat collapse is the reduction of the area of occupancy over time, according to the European Red List of Habitats [14]. Indeed, records of occurrence and abundance of plant species, which represent the origin of data for the habitat identification, are an irreplaceable source of information needed to evaluate and monitor the favourable/unfavourable conservation status of the habitats [15]. Starting from 2010, efforts in establishing vegetation databases at a continental scale e.g., [16; 17; 18], led to the renewing of the EUNIS (European Nature Information System) classification, the comprehensive hierarchical pan-European system for habitat identification [19; 20; 21], with clearly defined units that are useful for their assessments and with a coherent ecological and biogeographical background [22]. The EUNIS habitat classification is developed and maintained by the European Environmental Agency, for policy-related ecosystem and habitat assessments [23]. It constitutes the mandatory reference lists for national and regional classifications for geospatial mainstream data sharing. Moreover, Earth Observation (EO) data that are acquired by satellite sensors offer new opportunities for environmental sciences and are revolutionizing the methodologies applied, from experimental/theoretical to computational science [24], projecting big data from space in the mainstream of the ecological analysis. Therefore, it is easily foreseeable that, in the next decades, new technologies that are based on EO data will affect ecosystem surveys, mapping, and monitoring, thus opening a new era [25]. Recently, several scientific projects and studies have focused on natural habitats detection and the monitoring of their conservation status by using EO products [26; 27; 28; 29; 30; 31]. Satellite data have been used for mapping grasslands using multitemporal high resolution optical and multispectral optical data [32], forest communities using NDVI time series [33], coastal habitats using spectral mixing analysis [34; 35], phenological analysis using Sentinel-2 vegetation indices time series [36; 37], or the assessment of plant functional types using radar backscatter [38].