## 3 Methods
### Characterization of Forest Ecosystems
#### 3.1.2 Forest Mask  
Pre-processing  
First, atmospheric, radiometric, and topographic corrections, using the MAJA algorithm for Sentinel-2 data, were applied on the time-series available, followed by a cloud masking. High Resolution Landsat and Sentinel-2 images for epoch n \(\pm\) 1 in enlarged vegetation season from 1 st of May to 31 st of October were then selected in order to obtain a full mosaic coverage of the study area. Classification of the Forest Mask  
Machine Learning methods based on the random forest algorithm is a common practice found in literature for the mapping of land use and land cover, and more specifically of forest areas [9; 10]. The classification methodology is based on Geographic Object-Based Image Analysis (GEOBIA) using the Broceliande processing chain [13; 14]. This methodology has proven its efficiency for the mapping of the Copernicus High Resolution Layers Small Woody Features and Forest for the reference years 2015 and 2018, at Pan-European scale covering an area of about 6 million km\({}^{2}\)[8; 9]. The approach is actually a mixture of both GEOBIA and pixel-based analysis. It relies on two main components which are feature extraction based on attribute profiles, and a semi-supervised classification using a random forest algorithm at pixel level. The first step consists of computing predefined indexes such as the Normalized Difference Vegetation Index (NDVI), as well as texture characterization such as Sobel gradient and Haar-like features based on integral image representations. The Sobel gradient has proven to be more efficient than the renowned Haralick (1973) texture feature extraction based on a Gray Level Co-occurrence matrix (GLCM) which requires a time-consuming computation of a set of statistics for multiple distances and orientations. The texture information is extracted from each spectral band of the original EO multispectral image, as well as from the NDVI. This textural information will have a significant role in allowing the discrimination between Built-up and Bare soil, as their spectral response can be close. A first binary mask is produced, where all pixels flagged as no-data are set to a NoData value. Multi-scale features called Attribute Profiles (AP) and Differential Attribute Profiles (DAP) are derived through a model of morphological tree, which can be seen as a hierarchical segmentation: these trees allow to identify, depending on the way they are pruned, morphological objects from the EO data (see Figure 4). The second step is the use of the random forest classifier at pixel level. Reference samples were collected, i.e., \(\sim\)30 forest samples (examples) and 30 non-forest samples (counterexamples) to train the classifier. An in-house Machine Learning \begin{table}
\begin{tabular}{l l} \hline \hline
**Accuracies** & \\ \hline Geometric positional accuracy: & 1 pixel \\ Thematic accuracy: & 85\% \\ \hline \hline
**Minimum Mapping Unit** & \\
**(MMU)** & \\ \hline Sentinel-2 (10 m) & 0.25 ha (25 px) for forested area (No MMU for other classes and for changes) \\ Landsat 5–8 (30 m) & 1 ha (11 px) for forested area (No MMU for other classes and for changes) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._object-oriented classification algorithm was used to derive a probability map, depicting the probability for each pixel to belong to the forest class. A manual thresholding approach enabled the conversion of the latter into a binary forest/non-forest layer. 3. Post-processing  
The post-processing step consisted of the application of a Minimum Mapping Unit (MMU) of 1 ha for Landsat data, i.e., 10 Landsat pixels at 30 m spatial resolution, and in a MMU of 0.5 ha for Sentinel-2 data, i.e., 25 Sentinel-2 pixels at 10 m spatial resolution. 1. Computing of raw Forest Change Mask  
The Forest Change Mask at 30 m resolution was then computed by subtracting the previous monitoring epoch from the most recent one, 2019-2015, 2015-2010, 2010-2005, 2005-2002, 2002-2000, 2000-1995, 1995-1991, as well as 2019-1991 over the whole 28-year monitoring cycle. 1.