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Full catalog/clms_vlcc_tree-cover-density-confidence-layer_europe_10m_yearly_v1
clms_vlcc_tree-cover-density-confidence-layer_europe_10m_yearly_v1·dataset

How trustworthy each European forest map pixel is (Copernicus)

CLMS VLCC Tree Cover Density Confidence Layer (TCDCL) Europe 10m yearly V1
biosphere ESA ESA Copernicus active
In plain English

What it measures. A quality-control companion to the Tree Cover Density maps. Rather than showing forests directly, it flags how reliable the tree-cover reading is for each small patch of ground.

How it's made. Produced yearly from 2018 onward by the EU's Copernicus Land Monitoring Service as 10-metre grid tiles covering 38 European countries plus French overseas departments.

How & where you'd use it. Helps analysts and forest scientists know which parts of a tree-cover map to trust and which to treat with caution. It is a support layer, not a standalone forest map.

What's measured

CLMSCopernicusEuropeRasterLand coverLand useTree cover densityForestTree coverTCD

Coverage & cadence

  • Time span2018-01-01 → 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 Tree Cover Density Confidence Layer (TCDCL dataset provides a quality support raster product. This dataset is provided annually starting with 2018 in 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Tree Cover and Forest 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_tree-cover-density-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.