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Full catalog/ABoVE_AGBD_Uncertainty_Maps_2465
ABoVE_AGBD_Uncertainty_Maps_2465·v1·dataset

Yearly forest plant weight and uncertainty (Landsat, 1984-2022)

ABoVE: Landsat-derived Annual Aboveground Biomass Density and Uncertainty, 1984-2022
biosphere NASA ORNL_CLOUD Level 4 COG
In plain English

What it measures. Yearly maps of how much living plant matter (aboveground biomass) is standing in forests across Alaska and Canada from 1984 to 2022, plus a matching map of how uncertain each estimate is.

How it's made. Built from decades of Landsat satellite imagery combined with tens of thousands of ground plots and airborne laser (lidar) measurements, run through a machine-learning model and delivered at about 30-meter resolution.

How & where you'd use it. Helps researchers understand how much carbon northern forests store and how it changes year to year across the Arctic and boreal regions of North America.

What's measured

BIOSPHERE › ECOLOGICAL DYNAMICS › ECOSYSTEM FUNCTIONS › BIOMASS DYNAMICSBIOSPHERE › VEGETATION › BIOMASS

Coverage & cadence

  • Time span1984-01-01 → 2022-12-31
  • Measured byLANDSAT-9 (OLI) · LANDSAT-8 (OLI) · LANDSAT-7 (ETM+) · LANDSAT-5 (TM) · LANDSAT-4 (TM)
  • Processing levelLevel 4
  • Spatial extent-171.845, 41.9212, -52.6617, 82.3001
  • FormatsCOG
  • StatusCOMPLETE

What you can do with it

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

This dataset provides annual aboveground biomass (AGB) maps and associated uncertainty maps for Alaska and Canada from 1984 to 2022 at ~30 m resolution (0.00027 degrees). The dataset was derived using predictors from synthetic spectral features from Landsat Collection 2 and Continuous Change Detection and Classification algorithm. Extensive collections of ground plots (n = 45,002) and airborne lidar data (n = 421,942) were compiled for reference AGB in order to calibrate AGB models using Extreme Gradient Boosting (XGBoost) per ecoregion. Fifty AGB predictions were derived, of which the mean and standard deviation was used as per-pixel AGB prediction and uncertainty, respectively. The dataset can promote better understanding of carbon dynamics across arctic and boreal regions of North America. The data are provided in cloud optimized GeoTIFF format.

Get the data

above_agbd_uncertainty_maps_2465_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="ABoVE_AGBD_Uncertainty_Maps_2465",
    version="1",
    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.