# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Methods
#### 2.3.1 Feature Extraction  
We extracted multiple features to quantify and understand their added value for mapping mountain pines. Vegetation indices such as the NDVI provide reliable information on the photosynthetic activity and health of plants [42], and additional NDVI layers can improve the tree species mapping in mountain environments [6; 10; 23; 24]. Therefore, we calculated the NDVI for each PlanetScope and Sentinel-2 image. Topographic features allow for identifying the species-specific distribution within an alpine environment [23; 24] and can increase the discrimination of alpine species [6; 9; 28]. The direct linkage of alpine vegetation with the relief offers a unique opportunity for the mapping of mountain pine areas. Mountain pines are mainly distributed at the treeline, colonize slopes, which are too steep for upright growing tree species, and are influenced by the exposition [1; 6]; e.g., north-facing slopes in the Sarntal Alps are scarcely populated by the pine species [5]. For this reason, we calculated the slope and aspect based on the DTM. Mountain pines grow in dense patches and show a characteristic growth structure that is more homogenous compared to other subalpine conifers [2; 20; 24]. By including this textural information, the class separability between mountain pines and other coniferous tree species can be improved [10; 25; 43; 28]. We calculated a gray level co-occurrence matrix (GLCM) by [PERSON] et al. [44] for every PlanetScope and Sentinel-2 image. The GLCM is a popular approach to derive textural features and was successfully applied to map alpine vegetation types [22; 24; 25; 28]. The GLCM describes the relationship of the gray values of two or more neighboring pixels and combines this into a frequency matrix [43; 45] that can be used to calculate secondary textural statistics [46]. We chose the respective NIR band to calculate the GLCM due to its sensitivity to vegetation type, density and general plant health, following recent studies [28; 29; 46]. For computational reasons and statistical validity, quantization levels were reduced to 5-bit [43]. The distance between pixels during the calculation of GLCM was kept constant at one. We used the average of the four main interpixel angles (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\)) based on our assumption that mountain pine areas do not have a preferred texture orientation. The selection of the secondary statistical features has a significant impact on the classification [43; 46]. The homogeneity is particularly useful to capture the characteristic dense and intertwined growing form of mountain pines [28]. Additionally, secondary statistical features detecting edges can be useful to delineate single mountain pine patches [46]. Five secondary statistics--homogeneity, contrast, entropy, spatial mean and spatial variance (for a detailed description see [45; 46])--were derived from GLCM for every PlanetScope resp. Sentinel-2 image. The GLCM window size plays a crucial role, as the secondary statistics are calculated depending on it [46; 47]. To exploit the complete capabilities of textural analysis, the selection of GLCM window size should be performed with respect to the target size and sensor spatial resolution [43]. Too-small windows cannot recognize the patterns as a whole, and too-large windows can mix the patterns of several classes [43; 46]. For this reason, we calculated the five secondary statistics with iteratively increasing GLCM window sizes (\(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\), \(11\times 11\), \(13\times 13\), \(15\times 15\), \(17\times 17\), \(23\times 23\), \(29\times 29\), \(35\times 35\), \(43\times 43\) and \(51\times 51\) pixels) for both PlanetScope and Sentinel-2 data. Starting from the minimum window size of \(3\times 3\) pixels, we increased the windows by the minimum step size of \(2\) pixels until reaching a window size of \(17\times 17\) pixels.