Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content
  • Feedback

    • ABoVE: Landsat Vegetation Greenness Trends, Boreal Forest Biome, 1985-2019

    Metadata Updated: December 6, 2023

    This dataset provides information on interannual trends in annual maximum vegetation greenness from 1985 to 2019 for recently undisturbed areas in the boreal forest biome. Multi-decadal changes in remotely sensed vegetation greenness provide evidence of an emerging boreal biome shift driven by climate warming. Annual maximum vegetation greenness was assessed at about 100,000 random sample locations using an ensemble of spectral vegetation indices (NDVI, EVI2, kNDVI, and NIRv) derived from Landsat products. The dataset provides raster data summarizing vegetation greenness trends for sample locations stratified by Ecological Land Unit in GeoTIFF format. These raster data span the circum-hemispheric boreal forest biome between 45 to 70 degrees north at 300 m resolution. Estimates of uncertainty were generated using Monte Carlo simulations. Interannual trends in annual maximum vegetation greenness from 1985 to 2019 and 2000 to 2019 are provided for sample locations with adequate data for time series analysis; these data are in comma-separated values (CSV) format.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    Dates

    Metadata Created Date December 1, 2022
    Metadata Updated Date December 6, 2023

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Graphic Preview

    Change in greenness of boreal forest across the northern hemisphere between 2000 to 2019, estimated from an ensemble of vegetation indices derived from Landsat imagery. Source: boreal_greenness_median_percent_change_2000to2019_p500.tif

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date December 1, 2022
    Metadata Updated Date December 6, 2023
    Publisher ORNL_DAAC
    Maintainer
    Identifier C2262495772-ORNL_CLOUD
    Data First Published 2022-04-30
    Language en-US
    Data Last Modified 2023-06-12
    Category ABoVE, geospatial
    Public Access Level public
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Metadata Catalog ID https://data.nasa.gov/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Citation Berner, L.T., and S.J. Goetz. 2022. ABoVE: Landsat Vegetation Greenness Trends, Boreal Forest Biome, 1985-2019. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2023
    Graphic Preview Description Change in greenness of boreal forest across the northern hemisphere between 2000 to 2019, estimated from an ensemble of vegetation indices derived from Landsat imagery. Source: boreal_greenness_median_percent_change_2000to2019_p500.tif
    Graphic Preview File https://daac.ornl.gov/ABOVE/guides/BorealForest_Greenness_Trends_Fig1.jpg
    Harvest Object Id 3c5a8b8e-549e-4cde-9a46-2cd7b1297b87
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://doi.org/10.3334/ORNLDAAC/2023
    Metadata Type geospatial
    Old Spatial -180.0 45.0 180.0 72.0
    Program Code 026:001
    Source Datajson Identifier True
    Source Hash 782d7586e48830b77a16876653f1367476aab302ce7d7c74322e1e189f7fb643
    Source Schema Version 1.1
    Spatial
    Temporal 1985-01-01T00:00:00Z/2019-12-31T23:59:59Z

    Didn't find what you're looking for? Suggest a dataset here.