# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 4 Discussion
### Feature Selection and Importance  
The satisfactory results from feature spaces PS and S2 (scheme 1) are most likely due to their composition as multitemporal stacks, confirming several studies showing such effects (e.g., [10; 23; 48]). However, obtaining multiple useful optical acquisitions over a regional-scale mountainous area such as the Sarntal Alps (583 km\({}^{2}\)) is challenging due to short seasons, long snow cover presence and high cloud cover. Especially the high repetition rate of PlanetScope (majority of acquired images with revisit time \(<\)36 h) [34], allows to generate multitemporal image stacks in alpine environments. Temporal differences between the August (21 days) and September (13 days) scenes of PlanetScope and Sentinel-2 may capture differences in the phenological cycle of different species. Compared to other species (e.g., _Larix decidua_), the mountain pine remains constant in color and does not lose its needles. Therefore, the results of both feature spaces (PS and S2) should still be comparable. The addition of the GLCM statistics (scheme 2), led to the greatest improvement of the OA for both PlanetSope (8.29%) and Sentinel-2 (2.87%). Texture features circumvent the challenging spectral discrimination between mountain pines and other subalpine conifers [23; 28] by including the characteristic physiogonomy of the mountain pine. While previous studies used GLCM features to map alpine vegetation species with a predefined window size without prior testing [22; 24; 25], our results suggest that an optimization process to identify the best GLCM window size (GLCMMAX) can improve the classification result significantly and should be implemented in future mapping approaches to account for study site characteristics and observed classes. In this study, mountain pine stands in the Sarntal Valley can be best captured with a window size ranging from 87 to 90 m-29 \(\times\) 29 (PlanetScope) (7569 m\({}^{2}\)) and 9 \(\times\) 9 (Sentinel-2) pixels (8100 m\({}^{2}\)). Topographical features (scheme 3) eliminated misclassifications on the slopes of the Sarntal Valley (Figure 6), but changes in the OA are indistinct. Our results indicate that LiDAR-derived high-resolution topographic data (2.5 m) could be likely substituted byfreely available lower resolution DEMs (e.g., Copernicus DEM (30 m) or SRTM DEM (30 m)) since the DEM does not lead to a more accurate detection of small-scale patches but, rather, incorporates large-scale elevation differences into the classification. The CHM is a valuable feature for identifying mountain pines due to the distinct height difference to other coniferous trees [1; 26; 49] but only in combination with other information. When using just CHM as the input, confusion with objects of a similar height leads to a low OA (64.21%). The added value of CHM as an additional dataset is visible in classification scheme 4, where the best OA for both PlanetScope (90.96%) and Sentinel-2 (90.65%) was achieved. The temporal mismatch between the CHM and reference data might have had an influence on all classification results, including the CHM feature. However, we ensured that no reference points are located in areas affected by clearings between 2006 and 2020 by visually comparing orthophotos of these years. Our results indicate that with an adequate reference sampling technique and classification scheme, even an outdated CHM is useful for classifying mountain pines. Nevertheless, the findings indicate that using solely multitemporal spectral and textural information from four-band (blue, green, red, NIR) optical imagery already produces satisfactory results for mapping mountain pines in the study area--without considering auxiliary data sources (e.g., DTM or CHM). It should be noted that airborne LiDAR acquisitions are expensive and not available for large areas, which limits the transferability of an approach relying on the CHM.