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Full catalog/Vegetation_greenness_trend_1576
Vegetation_greenness_trend_1576·v1·dataset

Long-term greening trends, Alaska and Canada (NDVI, Landsat)

ABoVE: NDVI Trends across Alaska and Canada from Landsat, 1984-2012
biosphere NASA ORNL_CLOUD Level 4 GeoTIFF
In plain English

What it measures. How much vegetation across Alaska and Canada got greener or browner between 1984 and 2012, measured as a long-term trend in plant 'greenness' (NDVI), with a marker for whether each trend is statistically meaningful.

How it's made. Calculated pixel-by-pixel from decades of cloud-free summer Landsat 5 and 7 images, fitting a straight-line trend through each location's greenness over time.

How & where you'd use it. Lets researchers see where northern landscapes are greening or browning over time, a signal tied to warming, shrub growth, and ecosystem change.

What's measured

BIOSPHERE › VEGETATION › VEGETATION INDEX › NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)

Coverage & cadence

  • Time span1984-01-01 → 2012-12-31
  • Measured byLANDSAT-5 (TM) · LANDSAT-7 (ETM+)
  • Processing levelLevel 4
  • Spatial extent-169.966, 41.61, -50.1694, 80.5083
  • FormatsGeoTIFF
  • 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 the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the "trend" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the "trend_sig" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend.

Get the data

vegetation_greenness_trend_1576_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="Vegetation_greenness_trend_1576",
    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.