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

Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual
Dataset Availability
2001-01-01T00:00:00Z–2013-01-01T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual")
Cadence
1 Year
Tags
landcover landuse-landcover map oxford
igbp

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, https://malariaatlas.org/).

Bands

Pixel Size
5000 meters

Bands

Name Units Min Max Pixel Size Description
Overall_Class 0 17 meters

Dominant class of each resulting pixel

Water % 0 100 meters

Percentage of water

Evergreen_Needleleaf_Forest % 0 100 meters

Percentage of evergreen needleleaf forest

Evergreen_Broadleaf_Forest % 0 100 meters

Percentage of evergreen broadleaf forest

Deciduous_Needleleaf_Forest % 0 100 meters

Percentage of deciduous needleleaf forest

Deciduous_Broadleaf_Forest % 0 100 meters

Percentage of deciduous broadleaf forest

Mixed_Forest % 0 100 meters

Percentage of mixed forest

Closed_Shrublands % 0 100 meters

Percentage of closed shrublands

Open_Shrublands % 0 100 meters

Percentage of open shrublands

Woody_Savannas % 0 100 meters

Percentage of woody savannas

Savannas % 0 100 meters

Percentage of savannas

Grasslands % 0 100 meters

Percentage of grasslands

Permanent_Wetlands % 0 100 meters

Percentage of permanent wetlands

Croplands % 0 100 meters

Percentage of croplands

Urban_And_Built_Up % 0 100 meters

Percentage of urban and built up

Cropland_Natural_Vegetation_Mosaic % 0 100 meters

Percentage of cropland natural vegetation mosaic

Snow_And_Ice % 0 100 meters

Percentage of snow and ice

Barren_Or_Sparsely_Populated % 0 100 meters

Percentage of barren or sparsely populated

Unclassified % 0 100 meters

Percentage of unclassified

No_Data % 0 100 meters

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

CC-BY-NC-SA-4.0

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 with Earth Engine

Code Editor (JavaScript)

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');
Open in Code Editor