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Full catalog/GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303
GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303·dataset

Global Forest Canopy Height From Space

GEDI Mean Canopy Height AGL
land NASA VEDA COG
In plain English

What it measures. A global map of average forest canopy height, that is, how tall trees stand above the ground, to help measure ecosystem structure and the carbon forests store.

How it's made. Made with the GEDI laser instrument (lidar) on the International Space Station, which beams laser pulses to measure the 3-D structure of forests.

How & where you'd use it. Helps scientists estimate forest carbon and biodiversity and monitor changes in the world's forests.

What's measured

GEDIISSCanopyHeightMeanRH100201904202303

Coverage & cadence

  • Time span2019-04-18 → 2019-04-18
  • Spatial extent-180, -90, 180, 90
  • FormatsCOG

What you can do with it

  • Track deforestation, fire scars and land-cover change
  • Monitor crop and vegetation health (NDVI/EVI)
  • Map how built-up vs. green an area is over time
Official description

The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.

Get the data

veda_access.py
# NASA VEDA — open STAC API, anonymous (cloud-optimized GeoTIFFs)
from pystac_client import Client

cat = Client.open("https://openveda.cloud/api/stac")
col = cat.get_collection("GEDI_ISS_L3_Canopy_Height_Mean_RH100_201904-202303")
items = list(col.get_items())          # browse the analysis-ready COGs
# open an asset with rioxarray:
# import rioxarray; da = rioxarray.open_rasterio(items[0].assets["cog_default"].href)
NASA VEDA is an open STAC catalog — browse and stream the cloud-optimized GeoTIFFs anonymously (no login).