This R-Package provides several functions for interacting with databases WCS/WCPS based on OGC standards. We use the Rasdaman implementation in order to host Copernicus Sentinel Data in multidimensionla arrays (Data Cubes) as used for the Sentinel Alpine Observatory The package can be directly imported in R by typing:
This package offers several possibilities to interact with Data Cubes as listed below. If you need more information feel free to contact us or consult the two HTML Files located in the Documentary directory
It is possible to discover the whole Rasdaman environment (Capabilities) as well as the Coverages (Data Cubes) by calling the
getCapailities functionality followed by the respecive URLs. The function automatically calls and parses the XML response from the server and collects the data necessary to describe the datasets desired.
All the data returned by the server is accessible with the functions beginning with coverage_get_. These are explicitly for retrieving metadata corresponding to each of the coverage.
Each function has an automatic query handler translating the input in WCPS queries and hands them over to the Rasdaman Server. Additionally, all of these functions are functional for subsetting the Data Cube in two to three dimensions (x-y-z). The Functions image_from_coverage returns either an image or a subset of an image. The pixel_history function returns the time series for one single Pixel and the geocoded_pixel_buffer function for a specific area surrounding the Pixel.
The WCPS expansion allows the Data Cube to become not only a data provider but also a powerful tool for direct computation on the fly. The queries can be expanded by mathematical operator to perform operation between multiple spatial subsets. We implemented a functionality to calculate the NDVI (Normalized Difference Vegetation Index) directly on the Cube. The norm_diff_raster function is calculating on a raster or a subset of a raster at one given time and returns a TIFF raster. The norm_diff_pixel returns the calculated NDVI from one or multiple pixel over time as a Data Frame