Global Gridded Land Management Dataset at 0.01-degrees

Global Land Management Dataset 2020This dataset provides a global, spatially explicit map of land management intensity for the year 2020 at a spatial resolution of 0.01° (~1 km at the equator). Management intensity is classified across cropland, pasture, and forest systems using a harmonised, rule-based framework that integrates global datasets on land use, fertiliser inputs, irrigation, and forest harvest intensity.The dataset enables consistent global analyses of land management patterns and supports applications in land-use modelling, sustainability assessments, and human-environment research.

Spatial Extent: Global land areasResolution: 0.01° (~1 km)Coordinate system: WGS84 (EPSG:4326)Year: 2020

Each grid cell is assigned a single discrete class (values 1–50), representing land management intensity.Cropland: Very extensive, extensive (rainfed/irrigated), and intensive (rainfed/irrigated) classes for major crops (maize, wheat, rice, soybean, starchy roots), plus other cropsPasture: Very extensive, extensive, intensive, and unmanagedForest: Unmanaged, extensive, and intensive management for major forest typesOther classes: Urban, agroforestry, photovoltaics, unmanaged land, and water
... mehr

| Value | Class name                                           || ----: | ---------------------------------------------------- ||     1 | Urban                                                ||   2–6 | Maize (Very extensive ? Intensive irrigated)         ||  7–11 | Wheat (Very extensive ? Intensive irrigated)         || 12–16 | Rice (Very extensive ? Intensive irrigated)          || 17–21 | Soy (Very extensive ? Intensive irrigated)           || 22–26 | Starchy roots (Very extensive ? Intensive irrigated) ||    27 | Other crops                                          ||    28 | Photovoltaics (PV)                                   ||    29 | Agroforestry (AF)                                    ||    30 | Unmanaged land                                       || 31–45 | Forest classes (by type and intensity)               || 46–49 | Pasture classes                                      ||    50 | Water                                                |

Class codes:

    1:  ('Urban', '#EE0F05'),

    2:  ('VExt_maize', '#F5D081'),    3:  ('Ext_rf_maize', '#E8C278'),    4:  ('Ext_irr_maize', '#D6B55F'),    5:  ('Int_rf_maize', '#F3B361'),    6:  ('Int_irr_maize', '#D99A2B'),

    7:  ('VExt_wheat', '#F5DEB3'),    8:  ('Ext_rf_wheat', '#E8C258'),    9:  ('Ext_irr_wheat', '#D6AF3F'),    10: ('Int_rf_wheat', '#EDAD37'),    11: ('Int_irr_wheat', '#C9972B'),

    12: ('VExt_rice', '#F3D44D'),    13: ('Ext_rf_rice', '#DCAC64'),    14: ('Ext_irr_rice', '#C9963C'),    15: ('Int_rf_rice', '#F5B829'),    16: ('Int_irr_rice', '#D49A1F'),

    17: ('VExt_soy', '#DEB887'),    18: ('Ext_rf_soy', '#E8C288'),    19: ('Ext_irr_soy', '#CFA36D'),    20: ('Int_rf_soy', '#F3B351'),    21: ('Int_irr_soy', '#C98C2E'),

    22: ('VExt_sroots', '#FFC0CB'),    23: ('Ext_rf_sroots', '#F3B8E1'),    24: ('Ext_irr_sroots', '#D99CC9'),    25: ('Int_rf_sroots', '#F789E6'),    26: ('Int_irr_sroots', '#C96AB3'),

    27: ('Other_crops', '#F3B341'),    28: ('PV', '#D66528'),    29: ('AF', '#D0E9C8'),    30: ('UNM', '#BFBFBF'),

    31: ('UNM_mxdF', '#335C33'),    32: ('Ext_mxdF', '#5D9A5D'),    33: ('Int_mxdF', '#88CC88'),

    34: ('UNM_ndl_evrg', '#004D00'),    35: ('Ext_ndl_evrg', '#058A03'),    36: ('Int_ndl_evrg', '#BDED50'),

    37: ('UNM_brdl_evrg', '#006600'),    38: ('Ext_brdl_evrg', '#268C20'),    39: ('Int_brdl_evrg', '#BDED80'),

    40: ('UNM_ndl_dcds', '#066B04'),    41: ('Ext_ndl_dcds', '#009917'),    42: ('Int_ndl_dcds', '#A4F1B5'),

    43: ('UNM_brdl_dcds', '#213907'),    44: ('Ext_brdl_dcds', '#2DA03E'),    45: ('Int_brdl_dcds', '#8FF186'),

    46: ('VExt_pas', '#979006'),    47: ('Ext_pas',  '#E5E217'),    48: ('Int_pas',  '#FFFB1C'),    49: ('UNM_pas',  '#767005'),

    50: ('Water', '#0000FF')