Land Cover – CCI/ESA

Parameter 

Land Cover 

22 classes


 

 


Represented classes

0: No Data

1: Cropland rainfed: herbaceous cover

2: Cropland rainfed: tree or shrub cover

3: Cropland irrigated or post-flooding

4: Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (50%)

5: Mosaic natural vegetation (Tree, shrub, herbaceous cover) (>50%) / cropland (<50%)

6: Tree cover, broadleaved, deciduous, closed to open (>15%),

7: Tree cover, needleleaved, evergreen, closed to open (>15%),

8: Tree cover, needleleaved, deciduous, closed to open (>15%),

9: Tree cover, mixed leaf type (broadleaved and needleleaved),

10: Mosaic tree and shrub (>50%) / herbaceous cover (50%)

11: Mosaic herbaceous cover (>50%) / tree and shrub (<50%), 12: Shrubland

13: Grassland

14: Lichens and mosses

15: Sparse vegetation (tree, shrub, herbaceous cover)

16: Tree cover, flooded, fresh or brakish water,

17: Tree cover, flooded, saline water

18: Shrub or herbaceous cover, flooded, fresh/saline/brakish water,

19: Urban areas

20: Bare areas

21: Water bodies

22: Permanent snow and ice 

Description 

The CCI-LC project delivers consistent global Land Cover maps at 300 m spatial resolution on an annual basis from 1992 to 2018.

 The change detection can find up to 13 different types of change. These datasets were built using a multi-sensor archive made of MERIS FR and RR, AVHRR, SPOTVGT, PROBA-V 1 km and 300 m.

Using an independent validation database developed within GlobCover 2009, a precursor to CCI LC, an overall accuracy value of 71.5% (n = 2329) was estimated for the 2015 LC map.

The LC maps 2016 and 2017 have already been produced using Proba-V imagery, and their quality assessed. Their overall accuracies are of 71.1% (n=1350). 

European Space Agency (ESA)

Source data

European Space Agency (ESA) Climate Change Initiative – Land Cover

Land Cover Map 


Description 

The CCI-LC project delivers consistent global Land Cover maps at 300 m spatial resolution on an annual basis from 1992 to 2018.

This dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale is key for climate modellers, while also making it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research. 

Spatial resolution 

Global coverage

300 m spatial resolution

Temporal resolution

Period of observation: 

From 1992 to 2018