Skip to contents

Acquire various raster layers from ODS Europe and crops to an eLTER site boundary.

Source: R/get_site_ODS.R
get_site_ODS.Rd

[Stable] Download and return a SpatRaster object containing the requested dataset from ODS, cropped to an eLTER site boundary, which is obtained from the DEIMS-SDR API.

Usage

get_site_ODS(deimsid, dataset = "landcover")

Arguments

deimsid

A character. The DEIMS ID of the site from DEIMS-SDR website. DEIMS ID information here.

dataset

A character. The requested dataset. One of: "landcover", "clc2018", "osm_buildings", "natura2000", "ndvi_spring", "ndvi_summer", "ndvi_autumn", "ndvi_winter", "ndvi_trend", "forest_broadleaf", "forest_mixed", "forest_coniferous". Default is "landcover".

Value

The function returns a SpatRaster object (from the terra package) of the requested dataset, cropped to the site boundaries The user should save the raster to disk, if necessary. i.e. writeRaster(ds_site, "site_dataset.tif")

Details

Supported datasets from the ODS repository include: Landcover: Land-cover class according to the highest probability, generated by a spatiotemporal ensemble-ML model. 30 m. resolution CLC2018: Corine land cover rasterized to 100m spatial resolution and provided by Copernicus Land Monitoring Service. OSM buildings: Buildings according to OSM polygons and the Copernicus impervious build-up layer (2018), aggregated and rasterized first to 10m spatial resolution and after downsampled to 30m by spatial average. Natura2000: Protected areas rasterized from NATURA 2000 (A, B and C site categories) and OSM (IUCN Ia, IUCN Ib, IUCN 2, IUCN 3, IUCN 4, IUCN 5, IUCN 6 and others categories), first to 10m spatial resolution and after downsampled to 30m by spatial average. The overlap areas are indicated in a new category. NDVI: NDVI time-series, derived from the Landsat quarterly temporal composites NDVI Trend from 2000 - 2019 as OLS regression Forests: Broadleaf, coniferous or mixed forests All datasets are georeferenced to the EPSG:3035 coordinate reference system. and all except clc2018 have 30 meters resolution

The function output

NDVI for Eisenwurzen

References

Wickham H, François R, Henry L, Müller K (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.9, https://CRAN.R-project.org/package=dplyr.

Pebesma E (2018). “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal, 10(1), 439--446. doi:10.32614/RJ-2018-009 .

Hijmans RJ (2022). terra: Spatial Data Analysis. R package version 1.5-21, https://CRAN.R-project.org/package=terra.

Author

Micha Silver, phD (2020) silverm@post.bgu.ac.il

Alessandro Oggioni, phD (2020) oggioni.a@irea.cnr.it

Examples

 if (FALSE) {
# Landcover for Angelo Mosso
siteLandcover <- get_site_ODS(
  deimsid = "https://deims.org/17210eba-d832-4759-89fa-9ff127cbdf6e",
  dataset = "landcover"
)
siteLandcover
terra::plot(siteLandcover)

# NDVI for Eisenwurzen
siteNDVI <- get_site_ODS(
  deimsid = "https://deims.org/d0a8da18-0881-4ebe-bccf-bc4cb4e25701",
  dataset = "ndvi_summer"
)
siteNDVI
terra::plot(siteNDVI)
}