Dataset for plant species richness estimation in a wet grassland field using UAV data features

This dataset supports the estimation of plant species richness in a wet grassland field using features extracted from UAV (Unmanned Aerial Vehicle) data. It includes field and plot shapefiles, pre-processed input data, model performance metrics, spatial predictions (RASTER files).The dataset also contains geospatial imagery in the form of input and scaled GeoTIFF images, as well as two additional CSV files: date.csv, which records the cutting dates relevant to the study, and merged_obs.csv, which consolidates all the features with canopy height information extracted from Digital Elevation Model (DEM) data with field observed plant species richness. Summary: BIomass_Samples_Shapefiles: Contains shapefiles for field and plot-level data. Results: ALLDATA: Pre-processed input data for RF and PLS models. MODELPERF: Performance metrics and variable importance for RF and PLS models. RASTER: Spatially-explicit predictions (maps) for plant species richness estimation. GLCM: Pre-processed Gray Level Co-occurrence Matrix (texture features). VI: Pre-processed Vegetation Indices. TIF: Input and scaled geotiff images. rescaled: Rescaled geotiff images. resampled: Resampled geotiff images. date.csv: Contains cutting dates for the field. merged_obs.csv: Contains DEM and species richness data (number of species). This work was supported by the German Federal Ministry of Education and Research (BMBF) through the Digital Agriculture Knowledge and Information System (DAKIS) Project [Grant number 031B0729E].