# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Methods
#### 2.3.2 Feature Spaces and Classification Schemes  
To assess the influence of the generated features and the spatial resolutions of PlanetScope and Sentinel-2, multiple feature spaces were created (Table 1) and tested in our classification workflow (Figure 3). The first feature space is a multi-temporal image stackof the four 4-band PlanetScope resp. Sentinel-2 images and the NDVI layer of each image (20 layers in total). The feature spaces were named PS and S2, respectively (Table 1). We used multi-temporal satellite imagery stacks because intra-annual changes in vegetation greenness can improve the classification of alpine vegetation [48] and especially of mountain pines [6; 28]. Our third feature space (Topo) consisted of the LiDAR derived topographical features (elevation, slope and aspect) (Table 1). A valuable source for identifying alpine vegetation cover and species distribution is the CHM [27; 49]. The characteristic growing height (<3 m) of mountain pines [1; 2] can be used to differentiate the mountain pine from other conifers or dwarf shrubs at the treeline ecotone. Therefore, one feature space only includes the CHM. The four feature spaces (PS, S2, Topo and CHM) comprised classification scheme 1 and were classified individually.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Schemes** & **Feature Spaces** & **Features** & **Number of Layers** & **Spatial Resolution** \\ \hline \multirow{4}{*}{Scheme 1} & PS & Multi-temporal PlanetScope + NDVI & 20 & 3 m \\  & S2 & Multi-temporal Sentinel-2 + NDVI & 20 & 10 m \\  & Topo & Elevation + slope + aspect & 3 & 3 m \\  & CHM & Canopy height model & 1 & 3 m \\ \hline \multirow{4}{*}{Scheme 2} & PS + GLCMMAX & PS + GLCM statistics for optimal window size & 40 & 3 m \\  & S2 + GLCMMAX & S2 + GLCM statistics for optimal window size & 40 & 10 m \\ \hline \multirow{4}{*}{Scheme 3} & PS + GLCMMAX + Topo & PS + GLCM statistics for optimal window size + Topo & 43 & 3 m \\  & S2 + GLCMMAX + Topo & S2 + GLCM statistics for optimal window size + Topo & 43 & 10 m \\ \hline \multirow{4}{*}{Scheme 4} & PS + GLCMMAX + Topo + CHM & PS + GLCM statistics for optimal window size + Topo + CHM & 44 & 3 m \\  & S2 + GLCMMAX + Topo + CHM & S2 + GLCM statistics for optimal window size + Topo + CHM & 44 & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification schemes and feature space description.  
Figure 3: Methodology workflow for mountain pine mapping in the Sarntal Alps. A detailed description of the different feature spaces is given in Table 1. Each individual feature space of the respective classification schemes is classified using a Random Forest model. A subsequent majority filter generates the respective final output for the different feature spaces for the four classification schemes. For identifying the optimal GLCM window size (WS) for PS and S2 feature space, each GLCM WS feature space is classified and validated. The GLCM WS feature space with the highest overall accuracy is chosen as GLCMMAX for PlanetScope and Sentinel-2, respectively.  
Classification scheme 2 included the feature spaces PS + GLCMMAX and S2 + GLCMMAX. These feature spaces contain the multi-temporal imagery and NDVI stacks from classification scheme 1 combined with the five textural statistics for each image of the optimal GLCM window size. The optimal GLCM window size (GLCMMAX) was determined by iteratively classifying and validating each feature space and subsequently selecting the one with the highest overall accuracy (Figure 3).  
Classification scheme 3 combines the feature spaces of scheme 2 (PS + GLCMMAX, S2 + GLCMMAX) with the Topo feature space. Classification scheme 4 joins the feature spaces from scheme 3 (PS + GLCMMAX + Topo, S2 + GLCMMAX + Topo) with the CHM feature space.