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

2024-02-02 10:05:03 浏览数 (1)

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:

代码语言:javascript复制
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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