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Full catalog/clms_tcd_pantropical_10m_yearly_v1
clms_tcd_pantropical_10m_yearly_v1·dataset

Tropical forest tree-cover density map (Copernicus)

CLMS TCD Pantropical 10m yearly V1
biosphere ESA ESA Copernicus active COG
In plain English

What it measures. Shows, for each 10-metre pixel across the tropics, what percentage of the ground is shaded by tree canopy, for the 2020 baseline year.

How it's made. ESA Copernicus builds it from high-resolution imagery taken by the MSI camera on the Sentinel-2 satellites, as part of its tropical forest mapping service.

How & where you'd use it. A key reference for spotting and measuring deforestation and forest degradation, and for supporting international forest-protection and sustainability efforts.

What's measured

CopernicusCLMSTCDTree Cover Densityforest monitoringpantropicalyearly10mSentinel-2MSI

Coverage & cadence

  • Time span2020-01-01 → ongoing
  • Spatial extent-179.9999999, -48, 179.9999999, 36
  • FormatsCOG

What you can do with it

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

Provides pantropical tree cover density as projective tree cover in percent per pixel at 10 m resolution for the 2020 base year. The product belongs to the Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring Service (LCFM) and builds upon initiatives like the REDDCopernicus, EO4SD Forest Monitoring and pan-European Vegetated Land Cover Characteristics. It advances tropical forest monitoring capabilities, ensuring alignment with international sustainability initiatives and providing critical information for analysis and monitoring of deforestation and forest degradation.

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_tcd_pantropical_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.