## 3 Materials and Methods
### Reference Dataset Design
#### 3.2.2 Sampling and Response Design  
The marked plots were distributed through a random stratified sampling that was derived from a vegetation-non vegetation stratification based on an S2 annual maximum NDVI composite. The sampling for the validation process was additionally fine-tuned based on the classification results using the recommendations for adjusting for classes that were less-represented in the map [53].  
The samples of the reference dataset were collected by the visual interpretation of very high resolution (VHR) images from Google Earth with the free and open-access software tool Collect Earth [54]. Collect Earth, a tool developed by the UN-FAO for the monitoring of land cover and land use, is a state-of-the-art technique for the visual interpretation of VHR images [55]. Collect Earth allows for the collection of plot-level information using various sources of remote-sensing data. Built on Google Earth technologies, it provides access to all of the available satellite archives of VHR images [55]. It also facilitates access to Bing Maps' collection and enables multi-user interpretation.  
The interpretation was performed on a square plot of 0.25 ha, corresponding to 25 S2 pixels at a 10 m resolution. In order to take into account the possible shift from the Google Earth and Bing images with the S2 and S1 images, the classification considered the 9 central pixels of the square plot, equivalent to an area of 0.09 ha.  
Various tests showed that a visual distinction between trees and shrubs could not be carried out consistently on the country scale solely on the basis of the VHR imagery. Indeed, the presence of multiple stems and the height of the woody element are two discriminant features that are commonly used to differentiate between trees and shrubs when collecting field data, but these parameters cannot be retrieved from optical remotely-sensed VHR imagery. It was therefore decided that any woody element having a crown of at least 5 m in the 0.25 ha window would be interpreted as a tree element. For the visual interpretation, tree cover was defined hereafter as a woody element with a canopy crown \(\geq\) 5 m in diameter and a height of more than 3 m (these are features that cannot be seen on the VHR images). As illustrated in Figure 5, the interpreter visualizes a 0.25 ha square plot, a 0.09 ha central square plot and a circle of 5 m diameter around the central point. The circle is there  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level 0** & & **Level 1** & **Level 2** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Tree Cover \(\geq 10\%\)} & \multirow{4}{*}{10} & \multirow{4}{*}{80–100\% tree canopy closure} & 101 & Evergreen or semi-evergreen \\ \cline{3-4}  & & & & 102 & Deciduous \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \cline{3-4}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Legend for the reference dataset.  
indicating the 5 m tree crown threshold that is fixed to discriminate woody vegetation. The four categories of tree cover were evaluated according to the number of yellow squares that were covered by tree canopies. In addition to the VHR image archives that are available in Google Earth and Bing Maps, the interpreter had access to the S2 composite for the year 2018. The composite was displayed with the three bands which are useful in the context of forest monitoring (band 11 SWIR 1, band 8 NIR and band 4 RED). To document the seasonality of the 80-100% plots specifically, NDVI profiles from Landsat 8 and MODIS were provided through a script running on the GEE platform.  
Only plots with a VHR image that was available from close to year 2018 in Google Earth or Bing were interpreted. The interpreter also discarded any plot that did not present homogeneous vegetation cover in the 0.25 ha square (e.g., plots at the border of a forest). A total of 4196 plots were interpreted with a sufficient level of confidence (split between 3060 plots for training and 1136 for validation--Figure 6).