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Full catalog/Annual_Landcover_ABoVE_1691
Annual_Landcover_ABoVE_1691·v1·dataset

What covers the land each year (Arctic, 1984-2014)

ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014
land NASA ORNL_CLOUD Level 3 multiple
In plain English

What it measures. Year-by-year maps of what covers the land across the Arctic-Boreal region from 1984 to 2014, at 30-meter resolution. It identifies the dominant plant type in each pixel each year, with accuracy figures included.

How it's made. Derived from Landsat 5 and Landsat 7 surface reflectance combined with field training data and machine-learning (Random Forest) modeling to assign land-cover classes.

How & where you'd use it. Useful for tracking how Arctic and boreal vegetation shifts over three decades and studying the region's vulnerability to a changing climate.

What's measured

LAND SURFACE › LAND USE/LAND COVER › LAND USE/LAND COVER CLASSIFICATIONBIOSPHERE › ECOSYSTEMS › TERRESTRIAL ECOSYSTEMS › FORESTSBIOSPHERE › VEGETATION › VEGETATION COVERBIOSPHERE › ECOSYSTEMS › TERRESTRIAL ECOSYSTEMS › ALPINE/TUNDRALAND SURFACE › SURFACE RADIATIVE PROPERTIES › REFLECTANCEBIOSPHERE › VEGETATION › DOMINANT SPECIES

Coverage & cadence

  • Time span1984-01-01 → 2014-12-31
  • Measured byMODELS (Computer) · LANDSAT-5 (TM) · LANDSAT-7 (ETM+)
  • Processing levelLevel 3
  • Spatial extent-170.006, 50.259, -98.974, 76.2257
  • Formatsmultiple
  • 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 two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas.

Get the data

annual_landcover_above_1691_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="Annual_Landcover_ABoVE_1691",
    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.