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  • The service, starting from a multi-class shapefile containing the land cover classes in the scene, expressed in FAO-LCCS taxonomy, aims to generate a series of shapefiles, one for each different class. A numeric code is also associated to each class. The algorithm uses as input training and test data used to obtain the multi-class land cover mapping of the scene by a supervised, pixel-based classification. Within the Ailanthus workflow of the Internal Joint Intiative, it represents the step 1 and 2 of the first stage (where a multi-class problem is considered).

  • This service aims at creating a stack of coregistered multi-spectral raster images. It groups four multi-seasons images and stack them to obtain a unique image at native resolution (30 meters) by using the Landsat 5 sensor. It represents the Step 3 of the Ailanthus Workflow within the Internal Joint Initiative.

  • A service that aims at verifying the coordinates (latitude and longitude in column 3 and 4 of file “Pop_trop_SIA.csv”) match with the location (“country” and “location” in column 1 and 2 of file “Pop_troph_SIA.csv”) using the “SIAShapefile.shp”. This is very important because these geographic coordinates will be used in other services to extract and retrieve environmental data on the location of the species observation/occurrence. So scientists need to ensure these coordinates are correct. It represents the Step 3.2 of the Crustaceans Workflow within the Internal Joint Initiative.

  • A service that aims at verifying the species names (performing the taxonomy check) by using the input file SIA.csv. It represents the Step 1 of the Crustaceans Workflow within the Internal Joint Initiative.

  • This service aims at checking which taxa detected and identified from eDNA metabarcoding sequences are listed as NIS and which taxa are not (i.e. native or unrecorded NIS). This service uses available GBIF (Global Biodiversity Information Facility) records of NIS for each Country provided by the Invasive Species Specialist Group (ISSG). it checks if a species is present in the checklist and if this is the case, the species is flagged as being invasive for that country by adding 1 (yes) or 0 (no) to the column isInChecklist of the data frame, and the checklist key or a note to the ref_checklistKey column, for the corresponding cases. It represents the Step 3 of the Metabarcoding Workflow within the Internal Joint Initiative.

  • This service aims at calculating a spatialized cumulative impact index, generating vulnerability maps, which is based on the spatially-explicit model of global geographical application "Cumulative IMPacts of invasive ALien species" by Katsanevakis et al. 2016. It represents the Step 6 of the Biotope vulnerability Workflow within the Internal Joint Initiative.

  • This service aims at crossing the datacube with a geographic layer in order to compute incidence information per zone. This information is extracted based on the geolocation code. It includes a tool to visualize the outputs rasters. It represents the Step 5 of the Biotope vulnerability Workflow within the Internal Joint Initiative.

  • It is a Support Vector Machine, pixel-based, classifier trained for a multi-class problem. It represents the Step 4 of the Ailanthus Workflow within the Internal Joint Initiative.

  • This service aims at creating a stack of coregistered multi-spectral raster images. It groups two seasons images (summer and autumn) and stack them to obtain a unique image at native resolution (2 meters) by using the Worldview-2 sensor. It represents the Step 5 of the Ailanthus Workflow within the Internal Joint Initiative.

  • This service aims at building two datacubes based on the occurrences. The datacube aggregates (=counts) occurrences on a regular grid. This count has a stochastic component that takes the uncertainty field into account. All this process has been developed in R in the frame of the TrIAS project. It represents the Step 4 of the Biotope vulnerability Workflow within the Internal Joint Initiative.