Oxford MAP: Malaria Atlas Project Fractional International Geosphere-Biosphere Programme Landcover

Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual
Dataset Availability
2001-01-01T00:00:00 - 2013-01-01T00:00:00
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual")
Tags
igpb landcover oxford map

Description

The underlying dataset for this landcover product is the IGBP layer found within the MODIS annual landcover product (MCD12Q1). This data was converted from its categorical format, which has a ≈500 meter resolution, to a fractional product indicating the integer percentage (0-100) of the output pixel covered by each of the 17 landcover classes (1 per band).

This dataset was produced by Harry Gibson and Daniel Weiss of the Malaria Atlas Project (Big Data Institute, University of Oxford, United Kingdom, http://www.map.ox.ac.uk/).

Bands

Resolution
5000 meters

Bands

Name Units Min Max Description
Overall_Class 0 17

Dominant class of each resulting pixel

Water % 0 100

Percentage of water

Evergreen_Needleleaf_Forest % 0 100

Percentage of evergreen needleleaf forest

Evergreen_Broadleaf_Forest % 0 100

Percentage of evergreen broadleaf forest

Deciduous_Needleleaf_Forest % 0 100

Percentage of deciduous needleleaf forest

Deciduous_Broadleaf_Forest % 0 100

Percentage of deciduous broadleaf forest

Mixed_Forest % 0 100

Percentage of mixed forest

Closed_Shrublands % 0 100

Percentage of closed shrublands

Open_Shrublands % 0 100

Percentage of open shrublands

Woody_Savannas % 0 100

Percentage of woody savannas

Savannas % 0 100

Percentage of savannas

Grasslands % 0 100

Percentage of grasslands

Permanent_Wetlands % 0 100

Percentage of permanent wetlands

Croplands % 0 100

Percentage of croplands

Urban_And_Built_Up % 0 100

Percentage of urban and built up

Cropland_Natural_Vegetation_Mosaic % 0 100

Percentage of cropland natural vegetation mosaic

Snow_And_Ice % 0 100

Percentage of snow and ice

Barren_Or_Sparsely_Populated % 0 100

Percentage of barren or sparsely populated

Unclassified % 0 100

Percentage of unclassified

No_Data % 0 100

Percentage of no data

Overall_Class Class Table

Value Color Description
0 032f7e Water
1 02740b Evergreen_Needleleaf_Fores
2 02740b Evergreen_Broadleaf_Forest
3 8cf502 Deciduous_Needleleaf_Forest
4 8cf502 Deciduous_Broadleaf_Forest
5 a4da01 Mixed_Forest
6 ffbd05 Closed_Shrublands
7 ffbd05 Open_Shrublands
8 7a5a02 Woody_Savannas
9 f0ff0f Savannas
10 869b36 Grasslands
11 6091b4 Permanent_Wetlands
12 ff4e4e Croplands
13 999999 Urban_and_Built-up
14 ff4e4e Cropland_Natural_Vegetation_Mosaic
15 ffffff Snow_and_Ice
16 feffc0 Barren_Or_Sparsely_Vegetated
17 020202 Unclassified

Terms of Use

Terms of Use

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citations

Citations:
  • Weiss, D.J., P.M. Atkinson, S. Bhatt, B. Mappin, S.I. Hay & P.W. Gething (2014) An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 106-118.

Explore in Earth Engine

var dataset =
    ee.ImageCollection('Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual')
        .filter(ee.Filter.date('2012-01-01', '2012-12-31'));
var landcover = dataset.select('Overall_Class');
var landcoverVis = {
  min: 1.0,
  max: 19.0,
  palette: [
    '032f7e', '02740b', '02740b', '8cf502', '8cf502', 'a4da01', 'ffbd05',
    'ffbd05', '7a5a02', 'f0ff0f', '869b36', '6091b4', '999999', 'ff4e4e',
    'ff4e4e', 'ffffff', 'feffc0', '020202', '020202'
  ],
};
Map.setCenter(-88.6, 26.4, 1);
Map.addLayer(landcover, landcoverVis, 'Landcover');