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
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
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');