# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 2 Materials and Methods
### Data Gathering and Processing
#### 2.3.2 Predictor Variables  
A spatially explicit dataset of predictors was produced to generate classified habitat maps by the classification approach selected. Dataset of predictors includes three main categories:  
1. environmental data: variables related to geographic, topographic, climatic, and soil properties;  
Figure 2: Overview of the Italian territory at continental scale and the distribution of European biogeographical regions (adapted from [65]). spectral data: variables extracted from EO satellite sensors; and,
* temporal data: variables representing temporal statistics and phenological metrics estimated from the biophysical index time series, generated from satellite EO data. All of the variables were collected or calculated from dataset distributed under open-access policy and spatially resampled at 20 m resolution. See Table A1 for a complete list of variables, with name, description, units, and data sources. Reference spatial grid has been set to 20 m spatial resolution for the coordinate reference systems EPSG 32632 and EPSG 32633. The environmental variables were selected based on the relevance as drivers of plant distribution [55; 70]. Among the geographic variables, the linear distance in km from both shoreline and river network were computed for each grid cell [71]. A digital elevation model [72] at 20 m spatial resolution was used to calculate elevation, slope, and aspect using a 3 \(\times\) 3 pixel neighbourhood. Northernness and easternness components were later calculated form aspect, using sine or cosine transformation, in order to obtain continuous variables. The total rainfall [73] and average temperature [74] at 1 km resolution were interpolated from cell centroid coordinates to reference grid resolution using regularized spline with tension. The average temperature was normalized for the altitude effect [75]. Daily average solar irradiance was estimated at 20 m spatial resolution using the equations for computation of solar energy related parameters [76] and while considering local topography. Daily solar irradiance was aggregated to calculate monthly averages, and later daytime cloud cover collected from the ESA-Climate Change Initiative cloud dataset, for the period 2004-2014, were used to calculate the monthly average climatology cloud cover percentage [77]. Cloud cover percentage has been resampled to 20 m spatial resolution using regularized spline with tension and then used to weight solar irradiance with a factor of 0.75, to account for sky-diffuse solar radiation on cloudy days [78]. Finally, monthly weighted solar irradiance was used to calculate daily average solar radiation. Datasets representing soil properties at 250 m, specifically the soil organic carbon stock, pH, and absolute depth to bedrock, were collected from the SoilGrids (see Table A1) repository and interpolated to reference grid resolution using a Bartlett filter with 750 m radius. Spectral variables, specifically spectral reflectance bands and spectral indices, were calculated from EO data that were acquired by Multi-Spectral Instrument (MSI) sensor onboard Sentinel-2 satellites. MSI is a multispectral sensor with a high spatial resolution (10 m, 20 m, and 60 m), high revisit capability (five days with a two satellite constellations), an orbital swath width of 290 km, and a spectral band set from the visible to shortwave infrared. Sentinel-2 MSI L2A data used for the analysis, distributed by Theia in MUSCATE format, represent the bottom of the atmosphere (BOA) reflectance, orthorectified, terrain-flattened, and atmospherically corrected with MACCS-ATCOR Joint Algorithm (MAJA) [79; 80]. All of the satellite acquisitions of the period 2016-2019 with cloud cover lower than 90% were collected for the entire study area, for a total of around 26000 images distributed in 61 granules and corresponding to around 34 Terabyte (TB) of data stored.