Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy

The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand quantify impacts increasing logging activities climatic changes in European Alps, accurate information on occurrence distribution stands needed. While Earth observation provides up-to-date land cover, space-borne mapping pines challenging as different coniferous species are spectrally similar, small-structured patches may remain undetected due to sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) Sentinel-2 (10 combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, mean variance) gray level co-occurrence matrix (GLCM), topographic (elevation, slope aspect) canopy height information) overcome present challenges stands. Specifically, we assessed influence resolution feature space composition including GLCM window size for features. site covering Sarntal Italy, a region known large pine. Our results show that can be accurately mapped (PlanetScope (90.96%) (90.65%)) by combining all In general, achieve comparable independent set composition, despite lower particular, inclusion improved accuracy +8% (PlanetScope) +3% (Sentinel-2), whereas improvements were low. derived map Alps supports local forest management monitor assess recent ongoing anthropogenic at treeline. Furthermore, our highlights importance freely available data image-derived Alpine environments.