How structurally complex forests are (GEDI laser, ver 2) — GEDI_L4C_WSCI_Fusion_2474

In plain English

What it measures. Global maps rating how structurally complex forests are, meaning how varied and layered their canopy and vegetation are. Each 25-meter pixel gives a complexity estimate plus a sense of how uncertain that number is.

How it's made. Built with deep learning that fuses sparse laser measurements of forest height from NASA's GEDI instrument on the Space Station with radar imagery from Sentinel-1 and ALOS-2 and elevation data, producing complete coverage even where the laser didn't reach.

How & where you'd use it. Supports research on forest health, habitat, and carbon by giving continuous, gap-free maps of forest structure over 2015 to 2022, extending beyond the latitudes the laser alone could cover.

What's measured

SPECTRAL/ENGINEERING › LIDAR › LIDAR WAVEFORMBIOSPHERE › VEGETATION › CANOPY CHARACTERISTICSBIOSPHERE › VEGETATION › FOREST COMPOSITION/VEGETATION STRUCTUREBIOSPHERE › ECOSYSTEMS › TERRESTRIAL ECOSYSTEMS

Coverage & cadence

Time span2015-01-01 → 2023-01-01
Measured bySentinel-1B (SENTINEL-1 C-SAR) · TSX (X-SAR) · ALOS-2 (PALSAR-2) · Sentinel-1A (SENTINEL-1 C-SAR) · ISS (GEDI)
Processing levelLevel 4
Spatial extent-177.692, -56.0002, 178.685, 75.1138
FormatsCOG
StatusCOMPLETE

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

This dataset provides global, wall-to-wall estimates of the Waveform Structural Complexity Index (WSCI) at 25-meter spatial resolution with 3-monthly temporal frequency from 2015 to 2022. The product advances the sparse footprint-level GEDI L4C product by using deep learning to fuse spaceborne lidar observations with multi-sensor Synthetic Aperture Radar (SAR) data, producing spatially continuous estimates of forest structural complexity. The dataset employed an adapted EfficientNet version 2 architecture that integrates Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band SAR mosaics, and Copernicus Digital Elevation Model data. Each 25-meter pixel contains the mean WSCI estimate along with quantified uncertainties (aleatoric and epistemic standard deviations) and data quality indicators. Model training utilized approximately 133 million GEDI footprints collected within the operational domain (51.6 degrees N to 51.6 degrees S) from April 2019 to December 2022, with predictions extending both temporally (2015-2022) and spatially (beyond GEDI's latitude limits) through the fusion approach.

Get the data

gedi_l4c_wsci_fusion_2474_access.py

import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="GEDI_L4C_WSCI_Fusion_2474",
    version="2",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from ORNL_CLOUD

Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.

Official links

Earthdata Search allows users to search, discover, visualize, refine, and access NASA Earth Observation data. GET DATA
ORNL DAAC Data Set Documentation VIEW RELATED INFORMATION
GEDI L4C Global Waveform Structural Complexity Index (WSCI) Fusion Product, Version 2: GEDI_L4C_WSCI_Fusion.pdf VIEW RELATED INFORMATION