# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 5 Conclusions  
This study used multitemporal PlanetScope (3 m) and Sentinel-2 (10 m) imagery in combination with NDVI to map mountain pine (_P. mugo_) stands in the Sarntal Alps, South Tyrol. By successively adding additional features (textural features (GLCM), topographic features (elevation, slope and aspect) and canopy height model (CHM)), the overall classification accuracy of mountain pines improved. Thereby, the largest accuracy increase was obtained by including textural information based on an optimal GLCM window size for PlanetScope (29 \(\times\) 29 pixels (87 \(\times\) 87 m)) and Sentinel-2 (9 \(\times\) 9 pixels (90 \(\times\) 90 m)). The inclusion of LiDAR-derived high-resolution auxiliary datasets, such as topographical data and canopy height, only marginally improved the classification result. Since textural features directly derived from the satellite imagery already produce sufficient mapping results, there is no pressing need for the implementation of costly auxiliary datasets (e.g., Lidar derived DTM or CHM). This results in the easy transferability of our methodology to other study regions if sufficient reference data are available. The best classification was obtained when combining PlanetScope with the GLCM statistics, topographical features and canopy height model. However, comparable results were achieved with Sentinel-2 data instead of PlanetScope, indicating that the spatial resolution has no major influence on the mapping accuracy of mountain pines in our study area. Because of the free availability of Sentinel-2 data, this classification approach is particularly well-suited for further mapping of mountain pine stands in other alpine regions. The final mountain pine map will support local forest management by reducing the required resources to map _P. mugo_ stands. This allows for more comprehensive monitoring and provides the basis for understanding changes due to the mountain pine usage and climatic changes at the treeline ecotone. Therefore, further research should implement the presented classification approach to map the annual extent of mountain pines in future years. Conceptualization, M.R., R.S., S.B. and T.U. ; Formal analysis, M.R. ; Methodology, M.R., R.S., S.B. and T.U. ; Resources, M.R. and R.S. ; Software, M.R. ; Supervision, R.S., S.B. and T.U. ; Validation, M.R. ; Visualization, M.R. ; Writing--original draft, M.R. ; Writing--review and editing, M.R., R.S., S.B. and T.U. All authors have read and agreed to the published version of the manuscript. This publication was supported by the Open Access Publication Fund of the University of Wuerzburg and the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano. No further external funding was received. The PlanetScope imagery used in this work was provided by Planet Labs through DLR's RESA program (RESA-RESA, BMWi grant 50 EE 1612). The datasets generated during the study are available from the corresponding author on reasonable request. We kindly thank [PERSON] (Office for Forest Planning, Autonomous Province of Bolzano), [PERSON] and [PERSON] (Forestry station Sarntal, Autonomous Province of Bolzano) for providing the reference data, sharing their local knowledge, and expertise and organization of the field trip. The authors thank the Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano for covering the Open Access publication costs. The authors declare no conflict of interest.