Mapping Fire Salamander (Salamandra salamandra) Habitat Suitability in Baden-Württemberg with Multi-Temporal Sentinel-1 and Sentinel-2 Imagery
Sammanfattning: Remote sensing image classification is used in land cover, forest type and tree species classifications but rarely considered for habitat suitability modelling of animal and plant species. It is instead common that land cover products derived from remote sensing data are used in these modelling problems, even though satellite imagery can provide more detailed information. The aim of this project was thus to explore remote sensing image classification methods to classify land covers in Baden-Württemberg based on their habitat suitability for the fire salamander (Salamandra salamandra). Fire salamanders depend on both suitable aquatic and terrestrial environments, and the classification was therefore applied on multi-temporal Sentinel-1 and Sentinel-2 images combined with a waterway proximity layer derived from OpenStreetMap data. The classification used a random forest classifier which was trained to discriminate between positive samples from tree covered areas within 300 m of fire salamander observations and unlabelled samples drawn from a regular grid with 1500 m point spacing in the study area. Two classification methods were evaluated: pixel-based, in which single pixels are used in the classification, and superpixel-based, in which the classification was performed on mean pixel values of approximately equally sized (∼1 ha) vectorized regions of similar pixels derived from a Simple Linear Iterative Clustering (SLIC) segmentation of the study area. The resultant classifications were compared against a model with land cover data from Copernicus Land Monitoring Service, and the evaluation showed that the image classifications were able to discriminate better between positive and unlabelled test samples. The superpixel-based classification further achieved a higher evaluation score (AUC: 0.91) than the pixel-based classification (AUC: 0.90), and was thus the best model in the analysis. An exploratory analysis of the predictions based on LUCAS 2018 survey points further indicated that the models predicted high fire salamander habitat suitability in tree covered areas situated within roughly 200 m of stream and river features, with more than 10 % canopy cover, and with more than 25 % of broadleaved trees in the canopy composition. Remote sensing image classification for fire salamander habitat suitability modelling was concluded applicable at regional mapping scales, and more generally for habitat suitability modelling of species that are highly dependent on land cover characteristics.
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