Abstract
Climate change and anthropopression significantly impact plant communities by leading to the spread of expansive and alien invasive plants, thus reducing their biodiversity. Due to significant elevation gradients, high-mountain plant communities in a small area allow for the monitoring of the most important environmental changes. Additionally, being a tourist attraction, they are exposed to direct human influence (e.g., trampling). Airborne hyperspectral remote sensing is one of the best data sources for vegetation mapping, but flight campaign costs limit the repeatability of surveys. A possible alternative approach is to use satellite data from the Copernicus Earth observation program. In our study, we compared multitemporal Sentinel-2 data with HySpex airborne hyperspectral images to map the plant communities on Tatra Mountains based on open-source R programing implementation of Random Forest and Support Vector Machine classifiers. As high-mountain ecosystems are adapted to topographic conditions, the input of Digital Elevation Model (DEM) derivatives on the classification accuracy was analyzed and the effect of the number of training pixels was tested to procure practical information for field campaign planning. For 13 classes (from rock scree communities and alpine grasslands to montane conifer and deciduous forests), we achieved results in the range of 76–90% F1-score depending on the data set. Topographic features: digital terrain model (DTM), normalized digital surface model (nDSM), and aspect and slope maps improved the accuracy of HySpex spectral images, transforming their minimum noise fraction (MNF) bands and Sentinel-2 data sets by 5–15% of the F1-score. Maps obtained on the basis of HySpex imagery (2 m; 430 bands) had a high similarity to maps obtained on the basis of multitemporal Sentinel-2 data (10 m; 132 bands; 11 acquisition dates), which was less than one percentage point for classifications based on 500–1000 pixels; for sets consisting of 50–100 pixels, Random Forest (RF) offered better accuracy.
Highlights
Due to the large variety of environmental conditions in the vertical and horizontal gradients, mountain vegetation has developed specific habitat adaptations
The average F1-score values fluctuated around 0.9; the interquartile range (IQR) values for individual sets were similar
In the case of a smaller number of training polygons for classification, better results were obtained for sets based on the minimum noise fraction (MNF) data and the Random Forest classifier, while when the set contained more than 300 pixels in the training patterns, the differences between the data sets and classifiers provided comparable results (Table 5)
Summary
Due to the large variety of environmental conditions in the vertical and horizontal gradients, mountain vegetation has developed specific habitat adaptations. The adaptations are a consequence of highly differentiated vegetation belts, e.g., temperature, sunlight, exposure to high-energy UV radiation, strong, drying winds, water vapor, soil nutrients, and water content. These factors influence the survival strategies of individual species, visible in the plant physiology and morphology [2,3]. When the winters are relatively warm, plants have a chance to survive in harsher conditions, beginning to occupy higher-located habitats, which under normal circumstances would not be available to them, and during colder winters or when the snow cover decreases, plants are exposed to frost, which initiates fungal and insect-related diseases, causing plant dieback [4–6].
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