## 5 Discussion  
In order to map the tree cover and forest cover types over Tanzania, we have combined Copernicus S1 and S2 data with an ad-hoc reference training dataset and tested two different approaches that may be used to integrate the spatial information into classification algorithms. The combined use of S1 and S2 data allows us to produce maps of dry forests with an OA above 85% when capturing different forest cover types and above 95% for binary tree cover-no tree cover maps. The binary maps yielded higher accuracy but did not discriminate between the different forest types that present different biodiversity properties and carbon contents. Accurate information on forest type area improves the reporting of future carbon emission or gains to the UNFCCC in the framework of REDD+. The launch of the Sentinel missions and their free data policy are changing the paradigm of satellite-based tropical forest monitoring [55]. Both the increased spatial and temporal resolutions of the S1 and S2 platforms are currently being explored and tested in order to tackle technical challenges such as seasonality and sparse tree cover in the tropical domain [19; 61; 62; 63]. In tropical regions, the presence of clouds during most of the year is limiting the production of cloud-free composites, despite the 5 days revisiting time of combined S2A and S2B satellites [64]. In this study, we see that, despite the frequent cloud coverage, bi-monthly NDVI composites can be built with S2 over Tanzania. Being able to take into account the seasonality in classification algorithms improves the algorithms' ability to discriminate between the different vegetation formations, as they lose their leaves and structure at different times. The importance of S1 is also highlighted in this specific study, calling for more integration of SAR data in dry forest monitoring [19; 23]. However, the 10 m resolution still presents limitations for capturing Trees outside forests (TOF). TOF are an important natural resource that contributes to the carbon stock [65] and are usually underestimated in dry areas. The approach that was used in this study, taking into account a spatial window in order to better discriminate the class of 1-10% tree cover, allowed us to take into account a higher area that was covered by single trees than would have been possible with traditional approaches. However, many single trees are still missed. Approaches that automate the extraction of tree crowns from VHR images in dry environments show promising results for improving the counting of isolated trees in dry environments [66]. It is important to note that the maps that were used in this study do not distinguish open natural forest from degraded ones (e.g., landscapes from which tall trees have been removed). Forest degradation is a process of changes within the forest which negatively \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Forest Type**} & \multicolumn{4}{c}{**Tree Cover–No Tree Cover**} \\ \hline  & & \multicolumn{2}{c}{**Pixel**} & \multicolumn{4}{c}{**Window**} & \multicolumn{4}{c}{**Pixel**} \\ \hline  & & Area (km\({}^{2}\)) & Estimated & 95\% CI & Area (km\({}^{2}\)) & Estimated & 95\% CI & Area (km\({}^{2}\)) & Estimated & 95\% CI \\ \hline Tree cover & & & & & & & & & \\
80–100\% & 101 & 24,699 & 27,599 & 1661 & 21,291 & 26,049 & 1502 & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline Tree cover & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline  & & & & & & & & & \\  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 3: Area estimation of the tree cover and forest types in Tanzania in the year 2018 for three different classification approaches. affect the structure or function of the stand or site and thereby lower its capacity to supply products or services [67].