Full catalog/clms_vlcc_grassland-mowing-events-confidence-layer_europe_10m_yearly_v1
clms_vlcc_grassland-mowing-events-confidence-layer_europe_10m_yearly_v1·dataset

Reliability scores for the grassland-mowing map (Copernicus)

CLMS VLCC Grassland Mowing Events Confidence Layer (GRAMECL) Europe 10m yearly V1
biosphere ESA ESA Copernicus active
In plain English

What it measures. A companion quality layer showing how trustworthy the matching count of grassland mowing events is at each location.

How it's made. Produced yearly from 2017 onward by the EU's Copernicus Land Monitoring Service as a 10-metre grid across Europe's 38 countries, including French overseas territories.

How & where you'd use it. A technical support file that helps users judge the reliability of the mowing-events data before relying on it.

What's measured

CLMSCopernicusEuropeRasterLand coverLand useGrasslandMowingGRAM

Coverage & cadence

  • Time span2017-01-01 → ongoing
  • Spatial extent-180, -90, 180, 90

What you can do with it

  • Map vegetation, forests and biomass
  • Monitor ecosystem productivity and carbon
  • Support habitat and biodiversity studies
Official description

The High Resolution Layer Grassland Mowing Events Confidence Layer (GRAMECL) is a quality support raster product. This dataset is provided annually starting in 2017 in 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Grasslands product is part of the European Union's Copernicus Land Monitoring Service. This dataset includes data from the French Overseas Territories (DOMs).

Get the data

copernicus_access.py
# ESA Copernicus Data Space — open STAC API (free account)
from pystac_client import Client

cat = Client.open("https://stac.dataspace.copernicus.eu/v1")
search = cat.search(
    collections=["clms_vlcc_grassland-mowing-events-confidence-layer_europe_10m_yearly_v1"],   # add _cog or _nc for a format variant
    bbox=(-10, 35, 30, 60),             # your area (W,S,E,N)
    datetime="2024-01-01/2024-12-31",
)
items = list(search.items())            # then read assets with rioxarray / xarray
Browsing the Copernicus STAC is open; downloading bytes needs a free Copernicus Data Space account.