Countrywide classification of permanent grassland habitats at high spatial resolution

Abstract

European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product

“…Habitats in italics and with a '9 indicated in the Methods column were not modelled in version 1 of the habitat map. The source for validation data is indicated in the final column as detailed in the original publications [29][30][31][32]: the Swiss land use statistics (SLUS) [5], Delarze distribution maps [21], manual aerial image interpretation (AII), SwissFungi field observation (SF) [33] or not validated due to lack of data (NV). The topographic landscape model (TLM) of Switzerland is an open access detailed vector dataset that provides information on land cover at high spatial resolution interpreted from aerial imagery by swisstopo [26].…”

“…Before modelling grassland and wetland habitat types, a random forest modelling approach was used to differentiate cropland (TypoCH group 8.2) from permanent grassland [29]. Areas of known non-crop or grassland (e.g., forest, settlements, glaciers) were excluded using the TLM landcover data and the VHM with height greater than 3 m. Multi-year (2017-2019) growing-season spectral indices including normalised difference vegetation index (NDVI) derived from Sentinel 2 satellite imagery, as well as terrain indices, were used as predictors for this crop vs. permanent grassland model.…”

“…In addition, a probability map was created indicating the weighted median of the predicted probability of occurrence of the most likely grassland or wetland type. Full details can be found in [29].…”

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

“…Among Sentinel-2 bands, the red-edge bands (670-760 nm) are highly sensitive to the leaf chlorophyll content and are effective tools for discriminating vegetation classes [19,20]. Employing Sentinel-2 imagery in RF classifications has shown successful outcomes in agricultural lands [21], forests [15], wetlands [22,23], scrubland [7], and grasslands [24][25][26]. However, classification accuracy is limited by plant community complexity in composition and structure [27].…”

Different growth response of mountain rangeland habitats to annual weather fluctuations

“…In mountain regions, satellite remote sensing can be combined with specieshabitat modeling to detect pasture conversion [31,21] and monitor its management [32,33]. Predictive classification has been recently developed to detect thematic classes linked to species richness, productivity, or topographic setting [34,35,36]. Modeling experiments analyze the pasture productivity and its degradation in relation with drought conditions [37] and to detect invasive species [38,39].…”