Google Earth Engine——全球土地覆盖产品的基础数据集是MODIS年度土地覆盖产品(MCD12Q1)中的IGBP层

简介: Google Earth Engine——全球土地覆盖产品的基础数据集是MODIS年度土地覆盖产品(MCD12Q1)中的IGBP层

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/] (http://www.map.ox.ac.uk/)).


这个土地覆盖产品的基础数据集是MODIS年度土地覆盖产品(MCD12Q1)中的IGBP层。该数据从其分类格式(具有≈500米的分辨率)转换为分数产品,表明17个土地覆被等级(每个波段1个)覆盖的输出像素的整数百分比(0-100)。

这个数据集是由Malaria Atlas项目的Harry Gibson和Daniel Weiss制作的(英国牛津大学大数据研究所,[http://www.map.ox.ac.uk/](http://www.map.ox.ac.uk/))。

Dataset Availability

2001-01-01T00:00:00 - 2013-01-01T00:00:00

Dataset Provider

Oxford Malaria Atlas Project

Collection Snippet

ee.ImageCollection("Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual")

Resolution

5000 meters

Bands Table

Name Description Min Max Units
Overall_Class Dominant class of each resulting pixel 0 17
Water Percentage of water 0 100 %
Evergreen_Needleleaf_Forest Percentage of evergreen needleleaf forest 0 100 %
Evergreen_Broadleaf_Forest Percentage of evergreen broadleaf forest 0 100 %
Deciduous_Needleleaf_Forest Percentage of deciduous needleleaf forest 0 100 %
Deciduous_Broadleaf_Forest Percentage of deciduous broadleaf forest 0 100 %
Mixed_Forest Percentage of mixed forest 0 100 %
Closed_Shrublands Percentage of closed shrublands 0 100 %
Open_Shrublands Percentage of open shrublands 0 100 %
Woody_Savannas Percentage of woody savannas 0 100 %
Savannas Percentage of savannas 0 100 %
Grasslands Percentage of grasslands 0 100 %
Permanent_Wetlands Percentage of permanent wetlands 0 100 %
Croplands Percentage of croplands 0 100 %
Urban_And_Built_Up Percentage of urban and built up 0 100 %
Cropland_Natural_Vegetation_Mosaic Percentage of cropland natural vegetation mosaic 0 100 %
Snow_And_Ice Percentage of snow and ice 0 100 %
Barren_Or_Sparsely_Populated Percentage of barren or sparsely populated 0 100 %
Unclassified Percentage of unclassified 0 100 %
No_Data Percentage of no data 0 100 %

Class Table: Overall_Class

Value Color Color Value 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


数据引用:

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.

代码:

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');


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