Changes in the Greenness of Mountain Pine (Pinus mugo Turra) in the Subalpine Zone Related to the Winter Climate

Low sensitivity of Pinus mugo to surface ozone pollution in the subalpine zone of continental Europe

High altitudes have been exposed to enhanced levels of surface ozone (O₃) concentrations over recent decades compared to the pre-industrial era. The responses of vegetation to this toxic pollutant are species-specific and depend on the climate conditions. In this paper, we explored the reaction of Pinus mugo (P. mugo) to O₃-induced stress in the continental climate of an ozone-rich mountain area in the High Tatra Mountains (Western Carpathians). The effects of O₃ doses modelled by a deposition model, O₃ concentrations and other factors on P. mugo were identified from (a) satellite-based data via NDVI (normalised differenced vegetation index) over 2000-2020 and (b) visible injury on needle samples gathered from P. mugo individuals at ground-truth sites in 2019 and 2020. Analysing the NDVI trend, we observed non-significant changes (p > 0.05) in the greenness of P. mugo despite growing in an environment with the average seasonal O₃ concentration around 51.6 ppbv, the maximum hourly concentrations more than 90 ppbv and increasing trend of O₃ doses by 0.1 mmol m^-2 PLA (plant leaf area) year^-1. The visible O₃ injury of samples collected at study sites was low (mean injury observed on 1-10% of needles' surface), and the symptoms of injury caused by other biotic and abiotic factors prevailed over those caused by O₃. In addition, the correlation analyses between NDVI and the climatic factors indicated a significant (p < 0.05) and positive relationship with photosynthetic active radiation (R = 0.45) in July, and with stomatal conductance (R = 0.52) and temperature factor (R = 0.43) in August. Therefore, we concluded that the positive effect of climate conditions, which support the growth processes of P. mugo, may suppress the negative effect of the mean O₃ doses of 17.8 mmol m^-2 PLA accumulated over the growing season.

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