LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019

Description

This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB.

Resources

Name Format Description Link
Download this dataset HTML This link allows direct data access via Earthdata login Open resource
Download this dataset HTML This link allows direct data access via Earthdata login Open resource
Get a related visualization JPG Area fraction of each grid cell of primary lands covered by forests in the year 2000. Source: states.nc4 Open resource
View this dataset's user's guide HTML ORNL DAAC Data Set Documentation Open resource
Download this dataset ZIP Collection bundle Open resource
View documentation related to this dataset ZIP Collection bundle Open resource
View documentation related to this dataset PDF LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019: LUH2_GCB2019.pdf Open resource
View documentation related to this dataset HTML LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019: netcdf_header_info.txt Open resource
This dataset's landing page HTML Data set Landing Page DOI URL Open resource
Google Scholar search results HTML Search results for publications that cite this dataset by its DOI. Open resource

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