SA National Land-Cover Datasets

Adidas Originals Superstar Womens Red, Adidas Originals Red Shoulder Bag adidas originals superstar womens red, adidas originals red shoulder bag
Adidas Originals Gazelle Black, Adidas Originals Gazelle Black Black Adidas Originals Gazelle Black, Adidas Originals Gazelle Black Black

What is land cover data and what does it do?

Land-Cover data is a crucial reference dataset that informs a wide variety of activities ranging from environmental planning and protection, economic development, compliance monitoring, enforcement, and strategic decision making.

In order to monitor and manage landscape change over time, it is necessary to understand both, where change has occurred, as well as what has changed.


2020 South African National Landcover Data and the CALC system

The aim of the National Land-Cover Project was to develop a fully automated, operational process for the production of future national land-cover data for South Africa, based on the Gazetted land-cover classes (SANS 19144-2). The Computer Automated Land-Cover (CALC) system was developed with the ability to create automated land-cover datasets, accuracy assessments and change detection between comparable land-cover datasets with supporting reports and metadata. The CALC system has been used to generate the 2020 South African National Land-Cover (SANLC) dataset and all associated change assessments.


The following is available for download:

  • South African National Land Cover (SANLC) 2020

  • South African National Land Cover (SANLC) 2020 Change Assessment Datasets

  • New South African National Land Cover (SANLC) 2018, Computer Automated Land Cover (CALC)

  • New South African National Land Cover (SANLC) 2014 and 2018 Change Assessment Datasets, Computer Automated Land Cover (CALC)

  • PRESENTATIONS: SANLC 2020 Launch (29 June 2021)


1990 & 2013-2014

The production of the 1990 and 2013-14 SA National Land-Cover datasets allowed quantification of landscape change over a 25 year period to be determined both spatially and informatively. This process was greatly facilitated by both datasets having been generated using equivalent image data and mapping procedures so that both datasets were comparable in terms of detail, scale, and consistency of information content.

For both national land-cover datasets semi-automated spectral modelling procedures were used to generate the basic land-cover components, i.e. water, tree, bush, grass and bare ground. The basic land-cover components form the ‘building blocks’ upon which the more detailed final land-cover/landuse data were derived and defined. These semi-automated land-cover mapping techniques offered a more efficient alternative to conventional classification techniques (i.e. analyst-assisted pixel-based classifiers), allowing rapid production of standardised, yet informative land-cover information and classification. This provided the necessary standardised references from which landscape changes could be determined and quantified.


The South African National Land Cover 2018 Change Assessments

The production of national land-cover change datasets are important to support long-term environmental monitoring and to assess impacts on the natural environment. The South African National Land-Cover (SANLC) 2018 change assessment has been completed and contains a 20 class legend content which represents a simplified version of the full detail mapping legends. The new change legend provides a continuation of comparable information from the previous 1990 and 2013/14 change assessments. The SANLC 2018 change assessments are now available for download from the E-GIS website, download link.


The following is available for download:

  • SANLC 1990 and 2018 Change assessment dataset

  • SANLC 2013/14 and 2018 Change assessment dataset

  • SANLC 2018 Change Assessment Report


The South African National Land Cover 2018 dataset

The new South African National Land-Cover 2018 dataset has been generated from 20 meter multi-seasonal Sentinel 2 satellite imagery. The imagery used represents the full temporal range of available imagery acquired by Sentinel 2 during the period 01 January 2018 to 31 December 2018. The SANLC 2018 dataset is based primarily on the new gazetted land-cover classification standard (SANS 19144-2) with 73 classes of information and is comparable, with the previous 1990 and 2013-14 South African National Land-Cover (SANLC) datasets. The South African National Land-Cover 2018 dataset is available on an open licence agreement.

The SANLC 2018 data was launched on the 1st October 2019 and is now available for download from the E-GIS website, download link.


The following is available for download:

  • South African National Land-Cover (SANLC) 2018 Report
  • SANLC 2018 Accuracy Assessment Points
  • SANLC 2018 IMG (Erdas)
  • SANLC 2018 Hillshade IMG (> 8GB)
  • SANLC 2018 Launch Presentation


For Land-cover enquiries contact Zakariyyaa Oumar at


List of the 73 land-use classes available in the 2018 dataset

No. SANLC 2018 class names
1. Contiguous (indigenous) Forest (combined very high, high, medium)
2. Contiguous Low Forest & Thicket (combined classes)
3. Dense Forest & Woodland (35 - 75% cc)
4. Open Woodland (10 - 35% cc)
5. Contiguous & Dense Planted Forest (combined classes)
6. Open & Sparse Planted Forest
7. Temporary Unplanted Forest 
8. Low Shrubland (other regions)
9. Low Shrubland (Fynbos)
10. Low Shrubland (Succulent Karoo)
11. Low Shrubland (Nama Karoo)
12. Sparsely Wooded Grassland (5 - 10% cc)
13. Natural Grassland
14. Natural Rivers
15. Natural Estuaries & Lagoons
16. Natural Ocean, Coastal
17. Natural Lakes 
18. Natural Pans (flooded @ obsv time)
19. Artificial Dams (incl. canals)
20. Artificial Sewage Ponds
21. Artificial Flooded Mine Pits
22. Herbaceous Wetlands (currently mapped)
23. Herbaceous Wetlands (previous mapped extent)
24. Mangrove Wetlands
25. Natural Rock Surfaces
26. Dry Pans
27. Eroded Lands
28. Sand Dunes (terrestrial)
29. Coastal Sand Dunes & Beach Sand
30. Bare Riverbed Material
31. Other Bare
32. Cultivated Commercial Permanent Orchards 
33. Cultivated Commercial Permanent Vines
34. Cultivated Commercial Sugarcane Pivot Irrigated
35. Commercial Permanent Pineapples
36. Cultivated Commercial Sugarcane Non-Pivot (all other)
37. Cultivated Emerging Farmer Sugarcane Non-Pivot (all other)
38. Commercial Annuals Pivot Irrigated
39. Commercial Annuals Non-Pivot Irrigated
40. Commercial Annuals Crops Rain-Fed / Dryland / Non-Irrigated
41. Subsistence / Small-Scale Annual Crops
42. Fallow Land & Old Fields (Trees)
43. Fallow Land & Old Fields (Bush)
44. Fallow Land & Old Fields (Grass)
45. Fallow Land & Old Fields (Bare)
46. Fallow Land & Old Fields (Low Shrub)
47. Residential Formal (Tree)
48. Residential Formal (Bush)
49. Residential Formal (low veg / grass)
50. Residential Formal (Bare)
51. Residential Informal (Tree)
52. Residential Informal (Bush)
53. Residential Informal (low veg / grass)
54. Residential Informal (Bare)
55. Village Scattered (bare only)
56. Village Dense (bare only)
57. Smallholdings (Tree)
58. Smallholdings (Bush)
59. Smallholdings (low veg / grass)
60. Smallholdings (Bare)
61. Urban Recreational Fields (Tree)
62. Urban Recreational Fields (Bush)
63. Urban Recreational Fields (Grass)
64. Urban Recreational Fields (Bare)
65. Commercial
66. Industrial 
67. Roads & Rail (Major Linear) 
68. Mines: Surface Infrastructure
69. Mines: Extraction Sites: Open Cast & Quarries combined
70. Mines: Extraction Sites: Salt Mines
71. Mines: Waste (Tailings) & Resource Dumps
72. Land-fills
73. Fallow Land & Old Fields (wetlands)