# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 1 Introduction  
The mountain pine (_P. mugo_) is a shrublike, multi-stemmed tree species [1] that occurs on ecologically unfavorable sites in mountainous environments [2]. As an adaption to harsh climatic conditions, mountain pines have low-lying, elastically branches [3, 4] and normally do not grow higher than 3 m [1]. Due to the low requirements of nutrients and heat [4, 5, 6], mountain pines primarily colonize the treeline ecotone [2] but can also occur in more complex terrain (e.g., talus or mudflow slopes) [5] or pioneer subalpine open areas (e.g., former clearings or pastures) [7]. The mountain pine is widespread and native to the subalpine zone of the eastern and southern European Alps, the Carpathians and theDinaric Alps [1; 8; 9; 10], while small-scale populations also exist in the Apennine mountains, Jura mountains and Vosges [3; 7; 11]. The mountain pine is a key species within the treeline ecotone, providing multiple ecosystem functions. The extensive root system stabilizes the soil and thus protects it from erosion [4]. Furthermore, mountain pines can protect lower-altitude forest from rockfalls, avalanches and intensive surface runoff [5; 9; 12]. Mountain pines also provide shelter for the regrowing tree species on former clearing areas [2; 4] and thus can contribute to long-term reforestation [5]. Climatic changes in the alpine environment [13] lead to changes in the species composition at the treeline ecotone, with warm-adapted species expanding and cold-adapted species retrieving [11; 14]. The mountain pine benefits from the warming trend and advances into higher altitudes (e.g., the alpine altitudinal zone) [12; 15]. However, human activities are one of the greatest threats to the mountain pine and responsible for widespread changes [16], e.g., logging for pasture management [7] or extraction of mountain pine oil [5], which is often used in wellness or cosmetic products due to its curative effect against respiratory diseases [16; 17]. For this, mountain pines are mostly harvested by clear-cutting, with negative impacts on biodiversity [18] and soil functions [19]. Based on this, up-to-date and accurate maps of mountain pines are needed to monitor and understand climatically and anthropogenically induced changes. However, such maps are rare and traditionally require time- and labor-intensive field surveys in alpine terrains [5], which strongly limit their spatial coverage and repeatability [10; 20]. Using optical remote sensing data, alpine vegetation species can be mapped more comprehensively and cost-effectively. Thereby, only a few studies have assessed how accurate mountain pines can be classified and were mostly using airborne remote sensing imagery [7; 9]. Only a small number of studies employed spaceborne sensors but focused on the temporal changes in greenness [12] or the ecological favorability of mountain pine sites [6] rather than on the accurate mapping of mountain pine stands. Mountain pines tend to grow in dense patches ranging from several square meters up to multiple hectares [1; 5]. However, the physiognomic resemblance of mountain pines and other subalpine conifers [3; 21] results in a high spectral similarity, which causes ambiguities in multispectral Earth observation data [10; 22; 23; 24], especially in areas with a varying position of the treeline and strong intertwining between mountain pines and forested areas. Depending on the study area and patch size, increasing the spatial resolution may have a positive effect on the classification accuracy. In general, studies focusing on the mapping of subalpine vegetation have shown benefits from a high spatial sensor resolution, ranging from submeter aerial imagery [22; 25] to, most commonly used, spaceborne data, e.g., Sentinel-2 (10-20 m) [8; 10; 23; 24; 26], SPOT 5 (10 m) [27], RapidEye (5 m) [28] and GeoEye-1 (2 m) [29]. Besides sensor choice, the usage of additional features results in an increased accuracy of alpine vegetation classes.