FINAL REPORT Food and Agriculture Organization of the ... Project... · Contact person: Mr Freddy...

FINAL REPORT

to the

Food and Agriculture Organization of the United Nations (FAO)

Contact person: Mr Freddy Nachtergaele, Land and Water Division

SOUTH AFRICAN NATIONAL LAND-COVER CHANGE MAP

Project GW 51/037/01

Report No GW/A/2010/47

By: Schoeman, F., Newby, T. S., Thompson, M.W. and Van den Berg, E.C.

June 2010

Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW)

Private Bag X79, Pretoria, 0001, South Africa

Contact person: T. Newby

Tel: +27 12 310-2500, Fax: +27 12 323-1157

E-mail: [email protected]

i

Table of Contents

TALE OF CONTENTS...........................................................................................I

LIST OF FIGURES ..............................................................................................III

LIST OF TABLES............................................................................................... IV

LIST OF APPENDICES...................................................................................... VI

1. TERMS OF REFERENCE .............................................................................1

2. INTRODUCTION ...........................................................................................3

3. MATERIALS..................................................................................................4

3.1. Data Sources and Acknowledgements........................................................................4 3.1.1. National Land-Cover 1994 ..........................................................................................4 3.1.2. National Land-Cover 2000 ..........................................................................................5 3.1.3. “Five Class” National Land-Cover 2005.......................................................................5

4. METHODOLOGY ..........................................................................................8

4.1. Workflow .......................................................................................................................8

4.2. Process Description .....................................................................................................9 4.2.1. Base Grid ...................................................................................................................9 4.2.2. Conversion to Standardised Land-Cover Datasets ....................................................10 4.2.3. Zonal Attributes ........................................................................................................10 4.2.4. Final Annual Land-Cover Datasets............................................................................10

4.3. Temporal Land-Cover Change Modelling Issues ......................................................11 4.3.1. Land-Cover Change – Two Date Sequence Logic Review ........................................12 4.3.2. Land-Cover Change – Three Date Sequence Logic Review......................................13

4.4. Modelling.....................................................................................................................14 4.4.1. Modelling process (explanation)................................................................................14 4.4.2. Data normalisation - Number of changes made to original cell values .......................14

5. RESULTS....................................................................................................16

5.1. Land-cover Statistics per Assessment Year .............................................................16

5.2. Land-cover Change Statistics between Assessment Years .....................................22

5.3. Comment on Accuracy of Change Assessment........................................................23

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6. CONCLUSIONS AND RECOMMENDATIONS ...........................................25

7. SOUTH AFRICAN LAND-COVER CHANGE PRODUCT DESCRIPTION & METADATA........................................................................................................27

7.1.1. Data description........................................................................................................27 7.1.2. Attribute Description .................................................................................................27

8. MAPS ..........................................................................................................29

9. REFERENCES ............................................................................................29

10. APPENDICES..........................................................................................30

iii

List of Figures

Figure 4.1. Illustration of the overall workflow that was followed to convert the individual national

land-cover datasets into a standardised 5 x class legend format within the 500 x 500 m

national grid structure..........................................................................................................9

Figure 4.2. Example of the attribute table showing the three land-cover codes...........................11

Figure 4.3. Shows an example of the year-on-year land-cover code sequence (refer to Table 10.2

in Appendix A for full table). The 3 x year columns on the left indicate the original 3 x date

land-cover sequences, whilst those on the right illustrate the corrected sequences. Red cells

indicate land-cover sequences that could not be logically corrected and were thus labelled

as “mapping error” in the final data results.........................................................................14

Figure 4.4. Example of the data normalisation results table (refer to Appendix A Table 10.3 for full

table). ...............................................................................................................................15

iv

List of Tables

Table 1.1. Definitions of the five land-cover classes on which the land-cover change has been

based .................................................................................................................................2

Table 3.1. Details of owners and datasets used to compile the 5 class national land-cover 2005..6

Table 4.1. Example of the two date sequence (refer to Appendix A Table 10.1 for full table) ......13

Table 5.1. Indicates the number of cells and percentage of the total number of cells that have

been classed as transformed on a national scale on each of the three dates .....................16

Table 5.2. Breakdown of transformation per class for each of the three dates ............................17

Table 5.3. Breakdown of transformation per class for each of the three dates for the Eastern

Cape Province ..................................................................................................................17

Table 5.4. Breakdown of transformation per class for each of the three dates for the Free State

Province ...........................................................................................................................18

Table 5.5. Breakdown of transformation per class for each of the three dates for the Gauteng

Province ...........................................................................................................................18

Table 5.6. Breakdown of transformation per class for each of the three dates for the KwaZulu-

Natal Province ..................................................................................................................19

Table 5.7. Breakdown of transformation per class for each of the three dates for the Limpopo

Province ...........................................................................................................................19

Table 5.8. Breakdown of transformation per class for each of the three dates for the Mpumalanga

Province ...........................................................................................................................20

Table 5.9. Breakdown of transformation per class for each of the three dates for the Northern

Cape Province ..................................................................................................................20

Table 5.10. Breakdown of transformation per class for each of the three dates for the North West

Province ...........................................................................................................................21

Table 5.11. Breakdown of transformation per class for each of the three dates for the North West

Province ...........................................................................................................................21

Table 5.12. Illustration of the percentage change in land-cover classes between the different

assessment years .............................................................................................................22

Table 7.1. Description of the attributes in the attribute table of the digital 500x500m grid vector

data ..................................................................................................................................27

Table 10.1. Two date land-cover sequence ...............................................................................30

Table 10.2. Three date year-on-year land-cover sequence and the resulting logical corrections

(original sequence on left, corrected sequence on right). ...................................................31

Table 10.3. Cell changes resulting from logical corrections ........................................................39

Table 10.4. Eastern Cape land-cover change ............................................................................45

Table 10.5. Free State Land-cover change ................................................................................46

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Table 10.6. Gauteng Land-cover change ...................................................................................47

Table 10.7. KwaZulu Natal Land-cover change..........................................................................48

Table 10.8. Lesotho Land-cover change....................................................................................49

Table 10.9. Limpopo Land-cover change ...................................................................................50

Table 10.10. Mpumalanga Land-cover change ..........................................................................51

Table 10.11. North West Land-cover change .............................................................................52

Table 10.12. Northern Cape Land-cover change........................................................................53

Table 10.13. Swaziland Land-cover change ..............................................................................54

Table 10.14. Western Cape Land-cover change ........................................................................55

Table 10.15. National Land-cover change..................................................................................56

vi

List of Appendices

Appendix A. Data Tables ..........................................................................................................30

Appendix B. Results Summaries ...............................................................................................45

Appendix C. Maps.....................................................................................................................57

1

1. Terms of Reference

The FAO has contracted (Annexure 1) the ARC-ISCW to prepare a national land-

cover change map of South Africa. The subsequent activities have been

identified to support and prepare a national land-cover change map:

• Generating a reporting framework.

• Coding of the 1994/1995 national land-cover dataset.

• Coding of the national land-cover 2000 dataset.

• Coding of available 2005 land-cover data.

• Mapping and coding of 2005 gaps from imagery.

• Implementing a quality improvement procedure.

The final products will include:

a) A vector-based land-cover information system for South Africa for

the year 1995.

b) A vector-based land-cover information system for South Africa for

the year 2000.

c) A vector-based land-cover information system for South Africa for

the year 2005

d) A methodological report covering the preparation of (a), (b) and (c)

e) An analytical report on the main land-cover changes that have

taken place in the country over the period considered and their

geographic locations (a land-cover change map) with overall

statistics.

Land-cover change will be based on five land-cover classes: Urban, Mining,

Forestry, Cultivation and Other. These have been defined in Table 1.1.

2

Table 1.1. Definitions of the five land-cover classes on which the land-cover change has been based

Land-cover class Class definition

Urban Human settlements, both rural and urban

Mining Areas covered by mining and related mining activities, also includes mine dumps

Forestry and plantations All forestry and plantations including woodlots and clear fell areas (excludes indigenous natural forests)

Cultivation All areas used for agricultural activities, including old fields and subsistence agriculture

Other All other areas not covered by those listed above

The work has been carried out by a consortium comprising the Agricultural

Research Council (South Africa) and GeoTerraImage (Pty) Ltd., a private

company, located in South Africa

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2. Introduction

Globally, countries face a changing environment due to population growth,

increase in agricultural production, increasing demand on natural resources,

climate change and resultant degradation of the natural environment. One means

of monitoring this changing scenario is through land-cover change mapping.

Modern Earth Observation (EO) technologies, especially those EO datasets

comprising a multi-year data archive, lend themselves to land-cover change

studies. South Africa has over recent years experienced significant changes in

policy, legislation and service delivery. The Food and Agriculture Organization of

the United Nations (FAO) has contracted the Agricultural Research Council

(ARC) to prepare a national land-cover change map to quantitatively assess the

impacts these developments have had on land-cover. This project will use a

practical and cost-effective approach for monitoring land-cover change at a

national scale over time using EO data.

4

3. Materials

3.1. Data Sources and Acknowledgements

Land-cover data for the 3 x assessment years was sourced from a combination

of existing datasets and data generated specifically for the FAO land-cover

change assessment. The 1994 land-cover data was extracted from the existing

1994 South African National Land-Cover Dataset (NLC 94). The 2000 land-cover

data was extracted from the existing 2000 South African National Land-Cover

Dataset (NLC 2000). The 2005 land-cover data was generated from a

combination of provincial land-cover datasets, captured from various satellite

data acquired between 2005 - 2009, and from new land-cover data captured

specifically for the FAO project from historical 2005 Landsat imagery.

All existing land-cover datasets contained sufficient levels of land-cover detail to

enable standardised re-formatting into the required four basic change

assessment land-cover types, namely: urban / built-up, cultivated, mines, and

(forest) plantation. All new land-cover datasets were only mapped in terms of the

required 4 x change assessment land-cover classes.

3.1.1. National Land-Cover 1994

The NLC 94 data was mapped manually from 1:250,000 scale hardcopy Landsat

image maps, based on imagery acquired 1994 – 1995. The original land-cover

data was captured as a digital vector dataset with a minimum mapping unit of

25ha, and contained 31 land-cover classes (Fairbanks et al., 2000). The original

land-cover class detail was simplified into the required 4 x class change

assessment legend format, with all excluded original land-cover classes being

amalgamated into a new “other / background” class. The final map accuracy of

the NLC 94 dataset (averaged over the 3 x geographical production phases) was

79.4% (78.5 – 80.4% at the 90% confidence limits), with a kappa index of

74.8(Fairbanks et al., 2000).

5

3.1.2. National Land-Cover 2000

The NLC 2000 data was generated from digital Landsat imagery, acquired

primarily from 2000 to 2001. The original land-cover data was captured as a

digital raster dataset with a minimum mapping unit of 2ha, and contained 45

land-cover classes (Van den Berg et al., 2008). The original land-cover class

detail was simplified into the required 4 x class change assessment legend

format, with all excluded original land-cover classes being amalgamated into a

new “other/background” class. The final map accuracy for NLC 2000 was 65.8%

(65.10 – 66.52% at the 90% confidence limits), with a kappa statistic of 57 (Van

den Berg et al., 2008).

3.1.3. “Five Class” National Land-Cover 2005

The 2005 land-cover data was derived from a combination of existing provincial

land-cover datasets (which had been generated from suitably dated satellite

imagery), and new land-cover data mapped specifically for this project, off

historical Landsat imagery circa 2005 (Table 3.1).

The existing provincial land-cover datasets were all generated independently

using SPOT satellite data and contained comparable levels of detailed land-

cover information. These datasets included detailed coverage of Gauteng

Province, KwaZulu Natal Province, significant parts of the Western and Northern

Cape, and North West Province, (and nationally, all urban / built-up areas).

Permission to use and extract relevant land-cover class information from these

datasets was granted by Cape Nature, Ezemvelo KZN Wildlife, ESKOM,

GeoTerraImage Pty Ltd, and North West Dept of Agriculture, Conservation and

Environment. The datasets that were used as well as the Organisations or

Companies that own the copyright to the datasets are listed below:

6

Table 3.1. Details of owners and datasets used to compile the 5 class national land-cover 2005

Organisation/Company Offered Land-Cover Dataset

ESKOM National SPOT Building Count (SBC) (2006 SPOT data)

Cape Nature Fine Scale Biodiversity Land-Cover Dataset (2005 SPOT data)

Ezemvelo KZN Wildlife KwaZulu Natal Provincial Land-Cover (2005 SPOT data)

NW Dept Agric, Conservation & Environment North West Provincial Land-Cover (2006 SPOT data)

GeoTerraImage Pty Ltd Gauteng Provincial Land-Cover (2009 SPOT data)

The original land-cover class detail in these existing land-cover datasets was

simplified into the required 4 x class change assessment legend format , with all

excluded original land-cover classes being amalgamated into a new “other /

background” class.

The reported mapping accuracies for these existing datasets were as follows:

• The final map accuracy the EKZNW 2005 KZN Provincial land-

cover dataset was 83.06% (81.26 – 84.86% at the 90% confidence

limits), with a kappa index of 81.5(GTI EKZNW Land-Cover Report,

2008).

• The (amalgamated) final map accuracy for the Cape Fine Scale

Biodiversity land-cover was 81.63% (74.88 – 88.42% at the 90%

confidence limits), with a kappa index of 77.98(GTI Cape Nature

Report, 2008).

• The final map accuracy the NW Province land-cover dataset (Level

1) was 80.37% (78.97 – 81.77% at the 90% confidence limits), with

a kappa index of 78.57(GTI NW Province Land-Cover Report,

2008).

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• No formal statistical mapping accuracies were calculated for the

ESKOM SBC dataset or the GTI 2009 Gauteng Province land-

cover dataset. Both datasets have however received extensive

independent end-user reviews and applications, and are assumed

to be of a suitable level of accuracy for use in the FAO change

assessment project having both been derived from high resolution

SPOT5 imagery.

Areas for which no suitable 2005 land-cover data existed were mapped using

conventionally digital classification techniques from archival 2005 Landsat

imagery, as part of the data preparation activities for this project. These datasets

were only generated in terms of the required 4 x basic land-cover change

assessment classes, and not as full detail land-cover legends. New 4 x class

land-cover data were generated for Mpulalanga, Eastern Cape and Limpopo

Provinces.

8

4. Methodology

The basic approach taken for the land-cover change assessment was to

compare standardised 4 x class land-cover datasets representing the 3 x

assessment years (i.e. 1995, 2000 and 2005), within a uniform national grid,

based on 500 x 500 m cells. The land-cover allocated to each cell in each year

represented the spatially dominant land-cover within that cell, as determined from

the original 1994, 2000 and 2005 land-cover datasets. Various spatial modelling

procedures, as described below, were used to ensure compilation of comparable

and standardised land-cover class allocations to each 500 x 500 m cell for each

year, prior to any year-on-year change analyses.

A 500 x 500 cell size was chosen since this is the same as the 25 ha theoretical

minimum mapping unit associated with the original NLC1995 land-cover dataset,

and as such represented the coarsest level of mapping detail in the input

datasets. All other input datasets were thus spatially downgraded to this coarsest

level.

4.1. Workflow

The flow chart below (Figure 4.1) illustrates the overall workflow that was

followed in order to convert the individual 1995, 2000 and 2005 national land-

cover datasets into standardised 5 x class legend format, and code the cells

within the 500 x 500 m national grid structure, on which all change assessments

were based.

9

Figure 4.1. Illustration of the overall workflow that was followed to convert the individual national land-cover datasets into a standardised 5 x class legend format within the 500 x 500 m national grid structure.

4.2. Process Description

4.2.1. Base Grid

A national vector grid frame, based on 500 x 500 m cells and covering

all of South Africa was created as the base template. All cells were

given national and provincial name attributes to assist with final

results reporting and analysis on a sub-national level. Boundary cells

were clipped according to the definitive national boundary and thus

are not necessarily complete 500 x 500 m square cell structures (see example)

10

4.2.2. Conversion to Standardised Land-Cover Datasets

Prior to encoding of the 500 x 500 m national base grid, each of the individual

national land-cover datasets for 1995, 2000 and 2005 were initially converted to

comparable 30m raster national datasets, based on the 4 x class basic land-

cover legend format to be used in the change analysis. This approach was done

to ensure, as far as possible, that comparable results would be achieved, year-

on-year, when spatially summarising the original land-cover to the required 500 x

500m cell format.

4.2.3. Zonal Attributes

Encoding of the individual 500 x 500m cells with the appropriate land-cover for

each year was achieved using a “zonal majority” modelling process (ERDAS ©).

This process generated an attribute code for each cell based on the spatially

dominant land-cover class located within that cell extent.

Due to the physical size of the datasets being processed in this manner, it was

necessary to sub-divide the country into a series of non-overlapping data clips,

which were processed individually before being re-combined into a single

national coverage.

4.2.4. Final Annual Land-Cover Datasets

The land-cover codes for each year are represented as different attributes for

each cell within the same base grid template, rather than generate separate grid

templates for each assessment year (Figure 4.2). Similarly, all change

assessment results are reported as additional attributes within the same data

coverage. This approach has been taken since it allows a single data coverage

to be the final product deliverable, and facilities transparency of results reporting.

11

Figure 4.2. Example of the attribute table showing the three land-cover codes.

4.3. Temporal Land-Cover Change Modelling Issues

The accuracy of (land-cover) change modelling is directly dependent on the

accuracy of the input data, between which any changes are to be determined.

As indicated previously, a significant proportion of the input land-cover data used

in the FAO change assessment project was based on pre-existing land-cover

data. In such cases, these datasets are known to have a certain error component

in the original mapping content which could influence the accuracy and reliability

of comparative change analyses.

In order to minimise, as far as possible errors in change detection resulting from

original land-cover data misclassifications in the individual year datasets, two

systematic desk-top assessment procedures were used to identify and correct

any likely land-cover misclassifications based on the logic of the 3 x year

sequence of reported land-cover types within each specific grid cell. Whilst the

limitations of such an approach are acknowledged, the approach allows a

secondary level of individual year land-cover normalisation to be achieved prior

12

to any year-on-year change analysis, with commensurate increases in the

reliability and accuracy of final change assessment results.

The two corrective modelling procedures were implemented within a single

integrated modelling approach.

4.3.1. Land-Cover Change – Two Date Sequence Logic Review

The first logic assessment was based on the likelihood of any two-date pair

sequence of land-cover classes actually occurring in reality. These pair-based

logic assumptions are illustrated in the table 4.1 below.

For example, it is quite possible for a forestry plantation to be cleared and

replaced by an urban area, but highly unlikely that an urban area will be cleared

for forestry plantation. It is also highly unlikely for urban areas to be cleared for

any of the other land-cover classes.

Forestry will most likely not be cleared for cultivation as this is usually located in

areas where the slope is to steep for agricultural applications or the soil

conditions are marginal. This also applies for changes to class “Other”.

Mining areas are not likely to be converted to either urban or plantation, even

after rehabilitation, although a significant number of mines in the Mpumalanga

Highveld are re-converted to either cultivated lands or grasslands (i.e. “other”) as

a result of local land-use and land-cover characteristics.

The reasoning followed in developing these rules are based on logical principals

associated with drivers such as land use economics and physical landscape

criteria.

13

Table 4.1. Example of the two date sequence (refer to Appendix A Table 10.1 for full table)

Change from code Change to code Logical

Urban Forestry/Plantation No

Urban Mining/Quarries No

Urban Cultivation No

Urban Other No

4.3.2. Land-Cover Change – Three Date Sequence Logic Review

The second logic assessment was based on the likelihood of any three-date

sequence of land-cover classes actually occurring in reality, taking into account

the assumptions of the previous 2-date logic, when seen as part of a longer 3-

date sequence.

For example, it is quite possible for a forestry plantation (first date) to be cleared

and replaced by an urban area (second date), and that the urban area will remain

in the third date. But it is highly unlikely that the urban area (second) will be

cleared for replanting as a forestry plantation in the third year again. In such a 3-

date sequence (i.e. plantation-urban-plantation) it is more likely the case that the

second year “urban” code is a misclassification in the original land-cover dataset,

and should therefore be corrected to a second date “plantation” code (i.e.

plantation-plantation-plantation).

Figure 4.3 illustrates the various year-on-year land-cover code sequences that

could occur between the project legend classes (i.e. urban, plantation, cultivation,

mines and other); and the corrective code sequences that could be logically

applied to improve the initial accuracy of the 3 x year land-cover datasets before

change analysis. Note that in some instances, the logic of the 3 x year sequence

did not allow any corrective re-coding and in such cases these sequences were

identified as “mapping errors” within the final change analysis.

14

Figure 4.3. Shows an example of the year-on-year land-cover code sequence (refer to Table 10.2 in Appendix A for full table). The 3 x year columns on the left indicate the original 3 x date land-cover sequences, whilst those on the right illustrate the corrected sequences. Red cells indicate land-cover sequences that could not be logically corrected and were thus labelled as “mapping error” in the final data results.

4.4. Modelling

4.4.1. Modelling process (explanation)

The 2- and 3-date logic rules were applied to each cell within the national grid

template to correct, as far as possible, any land-cover misclassifications in the

original land-cover datasets that were now represented in the 500 x 500 m cell

attributes (after zonal majority modelling).

4.4.2. Data normalisation - Number of changes made to original

cell values

Table 10.3 (Appendix A) indicates the number individual cell values that were

changed within any 3-date sequence (on a year-by-year basis) within the full

national grid template, prior to any change assessment (Figure 4.4).

As can be seen, in most cases the number of changed cell values, including non-

correctable “mapping error” cells (per land-cover class, per year) was

significantly less than 10%, with many being less than 2%, which indicates that

15

although original mapping errors did exist, they are unlikely to have significantly

affected the reliability of the change assessment results.

Figure 4.4. Example of the data normalisation results table (refer to Appendix A Table 10.3 for full table).

16

5. Results

All change assessment results are illustrated in a series of tables contained in

Appendix B. Digital copies of the same tables are supplied along with the report

in Excel format.

5.1. Land-cover Statistics per Assessment Year

The tables below illustrate the total areas (and percentages) of each of the

mapped land-cover classes within each assessment year, at both a national and

provincial level. Since all mapped classes are representative of transformed

landscapes (i.e. changed from a natural state), these year-on-year statistics are

also broadly indicative of the level of landscape transformation across South

Africa.

Table 5.1 illustrates the total area of transformation, as represented by a

combination of all mapped land-cover classes across the entire country.

Table 5.1. Indicates the number of cells and percentage of the total number of cells that have been classed as transformed on a national scale on each of the three dates

Transformed (cell count)

Other (cell count)

*Mapping Error (cell

count)

Percentage Transformed

Percentage Other

Percentage *Mapping

Error

Total

1994 844306 4953730 8928 14.5% 85.3% 0.2% 100.0%

2000 770412 5027624 8928 13.3% 86.6% 0.2% 100.0%

2005 909633 4888403 8928 15.7% 84.2% 0.2% 100.0%

*Non-correctable mapping errors in final land-cover datasets after all possible

logical corrections have been applied

Table 5.2 is the same as Table 5.1, but sub-divided in terms of individual land-

cover classes.

17

Table 5.2. Breakdown of transformation per class for each of the three dates

1994 2000 2005

Cell count Percentage Cell count Percentage Cell count Percentage

1 - Urban 49116 0.8% 86469 1.5% 114987 2.0%

2 - Forestry and Plantations 67346 1.2% 83540 1.4% 93849 1.6%

3 - Mining and Quarries 7321 0.1% 7726 0.1% 9779 0.2%

4 - Cultivated 720523 12.4% 592677 10.2% 691018 11.9%

5 - Other 4953730 85.3% 5027624 86.6% 4888403 84.2%

5798036 5798036 5798036

Mapping errors 8928 0.2% 8928 0.2% 8928 0.2%

Total 5806964 100.0% 5806964 100.0% 5806964 100.0%

Tables 5.3 to Table 5.11 present the same information (on transformation extent)

but on a province by province basis.

Table 5.3. Breakdown of transformation per class for each of the three dates for the Eastern Cape Province

1994 2000 2005 Eastern Cape


1 - Urban 6980 0.9% 15826 2.0% 17136 2.1%



4 - Cultivated 63530 8.0% 23033 2.9% 64701 8.1%

5 - Other 720726 90.4% 750584 94.1% 706744 88.6%

795323 795323 795323

Mapping errors 1929 0.2% 1929 0.2% 1929 0.2%

Total 797252 100.0% 797252 100.0% 797252 100.0%

18

Table 5.4. Breakdown of transformation per class for each of the three dates for the Free State Province

1994 2000 2005 Free State


1 - Urban 2945 0.5% 4417 0.7% 5220 0.9%



4 - Cultivated 168252 28.5% 139998 23.7% 173892 29.5%

5 - Other 417642 70.8% 444554 75.4% 409336 69.4%

589750 589750 589750

Mapping errors 229 0.0% 229 0.0% 229 0.0%

Total 589979 100.0% 589979 100.0% 589979 100.0%

Table 5.5. Breakdown of transformation per class for each of the three dates for the Gauteng Province

1994 2000 2005 Gauteng


1 - Urban 12318 16.7% 15341 20.8% 17768 24.1%



4 - Cultivated 15998 21.7% 9919 13.5% 20730 28.2%

5 - Other 42824 58.2% 45865 62.3% 32562 44.2%

72581 72581 72581

Mapping errors 1017 1.4% 1017 1.4% 1017 1.4%

Total 73598 100.0% 73598 100.0% 73598 100.0%

19

Table 5.6. Breakdown of transformation per class for each of the three dates for the KwaZulu-Natal Province

1994 2000 2005 KwaZulu-Natal


1 - Urban 5289 1.2% 7993 1.9% 16918 3.9%

2 -Forestry and Plantations 24203 5.6% 29922 6.9% 33601 7.8%


4 - Cultivated 73607 17.1% 55063 12.8% 55314 12.8%

5 - Other 324976 75.5% 335174 77.8% 322344 74.9%

428349 428349 428349

Mapping errors 2201 0.5% 2201 0.5% 2201 0.5%

Total 430550 100.0% 430550 100.0% 430550 100.0%

Table 5.7. Breakdown of transformation per class for each of the three dates for the Limpopo Province

1994 2000 2005 Limpopo


1 - Urban 6198 1.1% 14488 2.6% 21933 4.0%



4 - Cultivated 79519 14.4% 65248 11.9% 58214 10.6%

5 - Other 460214 83.6% 465013 84.5% 464109 84.3%

549686 549686 549686

Mapping errors 897 0.2% 897 0.2% 897 0.2%

Total 550583 100.0% 550583 100.0% 550583 100.0%

20

Table 5.8. Breakdown of transformation per class for each of the three dates for the Mpumalanga Province

1994 2000 2005 Mpumalanga


1 - Urban 4360 1.3% 7821 2.3% 11563 3.4%



4 - Cultivated 56490 16.6% 48043 14.1% 73929 21.8%

5 - Other 247992 73.0% 247640 72.9% 212863 62.6%

338851 338851 338851

Mapping errors 1000 0.3% 1000 0.3% 1000 0.3%

Total 339851 100.0% 339851 100.0% 339851 100.0%

Table 5.9. Breakdown of transformation per class for each of the three dates for the Northern Cape Province

1994 2000 2005 Northern Cape


1 - Urban 1075 0.1% 3307 0.2% 3974 0.2%



4 - Cultivated 12324 0.7% 9567 0.6% 11585 0.7%

5 - Other 1701991 99.1% 1701756 99.1% 1698724 99.0%

1716720 1716720 1716720

Mapping errors 28 0.0% 28 0.0% 28 0.0%

Total 1716748 100.0% 1716748 100.0% 1716748 100.0%

21

Table 5.10. Breakdown of transformation per class for each of the three dates for the North West Province

1994 2000 2005 North West


1 - Urban 5070 1.1% 7503 1.6% 8669 1.8%



4 - Cultivated 94574 19.9% 101444 21.4% 106739 22.5%

5 - Other 372712 78.6% 363187 76.6% 356469 75.1%

473457 473457 473457

Mapping errors 944 0.2% 944 0.2% 944 0.2%

Total 474401 100.0% 474401 100.0% 474401 100.0%

Table 5.11. Breakdown of transformation per class for each of the three dates for the North West Province

1994 2000 2005 Western Cape


1 - Urban 4617 0.7% 6473 1.1% 7724 1.3%



4 - Cultivated 108524 17.6% 105627 17.1% 100112 16.2%

5 - Other 499948 81.1% 499570 81.1% 503237 81.7%

615732 615732 615732

Mapping errors 493 0.1% 493 0.1% 493 0.1%

Total 616225 100.0% 616225 100.0% 616225 100.0%

22

5.2. Land-cover Change Statistics between Assessment

Years

Tables 5.12 below illustrate the national changes in land-cover class between the

different assessment years. Note that the percentage values indicated in the

tables below are the percentage of the original cells that have changed to

another class; and not its area in relation to the total area of South Africa, i.e.

53% of the total area of mining in 1994 is still mining in 2000.

Table 5.12. Illustration of the percentage change in land-cover classes between the different assessment years

Year: 2000

Percentage Urban Forestry Mining Cultivation Open Total

Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.4% 99.5% 0.1% 0.0% 0.0% 100%

Mining 0.0% 0.0% 53.2% 4.1% 42.7% 100%

Cultivation 1.7% 0.3% 0.1% 58.4% 39.5% 100%

Yea

r: 19

94

Open 0.5% 0.3% 0.1% 3.5% 95.7% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.3% 99.2% 0.5% 0.5% 0.0% 100%

Mining 0.0% 0.0% 76.0% 2.3% 21.8% 100%

Cultivation 1.5% 0.2% 0.1% 76.8% 21.4% 100%

Ye

ar: 2

000

Open 0.4% 0.2% 0.1% 4.7% 94.7% 100%

23

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.6% 98.7% 0.1% 0.6% 0.0% 100%

Mining 2.5% 0.7% 58.2% 3.4% 35.2% 100%

Cultivation 3.2% 0.5% 0.2% 65.3% 30.9% 100%

Ye

ar: 1

994

Open 0.9% 0.5% 0.1% 4.4% 94.1% 100%

For example 1.7% of the cells that were classified as Cultivation in the 1994 land-

cover have changed to Urban in the 2000 land-cover. Also 76.8% of the cells

classified as Cultivation in the 2000 land-cover was still classified as Cultivation

in 2005 whereas 21.4% of the cells classified as Cultivation in 2000 were

classified as Open in 2005 land-cover.

Similarly the tables indicate that 4.4% of the cells classed as Open in the 1994

land-cover, were classed as Cultivation in the 2005 land-cover.

5.3. Comment on Accuracy of Change Assessment

The accuracy of land-cover change detection is directly linked to the accuracy of

the input land-cover data being used to detect any change.

Both the 1994 and 2000 land-cover datasets have previously been independently

validated using comprehensive statistical sampling, as has a significant

proportion of the 2005 land-cover data (see section 3).

The assumption is that all the new land-cover data, created specifically for the

FAO change project in order to complete the 2005 national data coverage has

been generated with comparable levels of mapping accuracy, since in many

cases the same experienced remote sensing analysts have been used for this

process as were involved in the previous NLC 94 and NLC 2000 mapping

activities.

It is therefore assumed that the (logic-based) desk-top corrective measures

applied to the original land-cover data prior to change analysis should have

24

corrected, where possible, the identified misclassifications, and so improved

further the reliability of the change detection results.

25

6. Conclusions and Recommendations

The primary objective of the study was to determine the extent of transformed

landscape change within South Africa over a ten year period between 1994 and

2005. In order to achieve this objective, the project used three generalised land-

cover datasets (for 1994, 2000 and 2005) and quantified the change between

these assessment years.

The year 2000 data although not part of the primary project objective proved to

play a critical role in the validation and correction of the 1994 and 2005 datasets

in terms of determining logical land-cover change sequences. As such the 2000

dataset and associated results should not be seen as part of the primary output.

In summary the results indicate at a national level that there has been a total

increase of 1.2% in transformed land specifically associated with Urban,

Cultivation, Plantation Forestry and Mining. This represents an increase from

14.5% transformed land in 1994 to 15.7% in 2005 across South Africa. (see table

5.1a)

On a national basis the areas of Urban, Forestry and Mining have all increased

over the 10 year period where as Cultivated areas have decreased. Urban has

increased from 0.8% to 2%, Forestry from 1.2% to 1.6%, Mining has increased

from 0.1% to 0.2%, while Cultivated has decreased from 12.4% to 11.9%. The

spatial patterns do however vary geographically across provinces in South Africa.

(see table 5.1b)

Although the modelling procedures are considered sound and can form a

framework for similar change assessments in future, it should be noted that the

ouputs are dependent on the quality, compatibility and accuracy of the input

datasets. In this project it should be noted that the differences in the source

datasets relating to mapping methodology, scale and classification systems

used, will still have had an influence on the final project output. This is despite

the corrective modelling procedures implemented.

26

Woodcock and Strahler (1987) discuss the difference between high resolution

and low resolution imagery or spatial data and how the size and spatial

relationship of the object of interest influence the variability with in land-cover

classes. In this study the reported increase in mining in Mpumelanga province

from 0.6% in 1994 to 0.9% in 2005 which represents a 50%

increase in mining activity in the province is possibly an under

estimation. This is the result of the fact that strip mining areas

are generally not the dominant cover with in a 500 x 500 m cell

due to their linear shape. (see inset)

The dataset resulting from the process described can however still be considered

a useful resource for further research. It is presented in a format that facilitates

further research and analysis where researchers can alter assumptions made by

the current research team and introduce their own assumptions. The format of

the dataset allows for ease of re-analysis and further interrogation.

It is recommended that further research should include investigation into the

transformed cover classes with the objective of identifying the drivers and type of

change that has occurred as well as the impacts, socially, environmentally and

economically of these changes over time.

27

7. South African Land-Cover Change Product

Description & MetaData

7.1.1. Data description

The resulting dataset created through the processes described is a grid of

500x500m cells covering the extent of South Africa including Swaziland and

Lesotho.

Type of data: vector (polygon) digital data

Data storage: the data is stored in an ESRI file type geodatabase as it consists of

5,806,964 features/objects thus making it a very large file and to ensure ease of

use the decision was made to store in a file type geodatabase.

7.1.2. Attribute Description

The following fields (Table 7.1) are present in the attribute table of the 500x500m

grid.

Table 7.1. Description of the attributes in the attribute table of the digital 500x500m grid vector data

Attribute Description

ObjectID System generated Object identification. Internal feature number

Shape Feature geometry

Province or Country name for processing and querying purposes

LC_1994 1994 Land-cover code as per FAO code. See *Subclass description

MAJ_F94 Majority fraction of class found in the cell for the 1994 land-cover





LC_1994c Normalised 1994 Land-cover code as per FAO code. See *Subclass

28

description

LC_2000c Normalised 2000 Land-cover code as per FAO code. See *Subclass description

LC_2005c Normalised 2005 Land-cover code as per FAO code. See *Subclass description

LC_CHANGE Different land-cover codes found over the three dates. Indicated as a series of codes, for example “4;2;1” will indicated the cell value for 1994 was Cultivated, in 2000 Forestry and Plantations and in 2005 became Urban. This is based on the normalised data values

LCC94_00 Land-cover change between 1994 and 2000. Summarised in Table 3.1.12



* Subclass: FAO code and description

1 – Urban

2 – Forestry and plantations

3 – Mining and quarries

4 – Cultivation and agriculture

5 - Other

29

8. Maps

Included in Appendix C.

List of maps

• 1994 summarised FAO land-cover map



• map showing areas where change occurred

9. References

Fairbanks DHK, Thompson MW, Vink DE, Newby TS, Berg van den HM, and

Everard DA; 2000. The South African Land-Cover Characteristics Database: a

synopsis of the landscape. SA Journal of Science. 96. Feb 2000 p 69 – 82.

GeoTerraImage, 2008. C.A.P.E. NATURE Fine Scale Biodiversity Planning

Conservation Project. Land-Cover Classifications from SPOT5 Satellite Imagey.

End Users Summary Report and Metadata. Unpublished project report, February

2008.

GeoTerraImage, 2008. KZN Province Land-Cover Mapping (from SPOT2/4

Satellite Imagery 2005-06). Data Users Report and Metadata. Unpublished

project report, February 2008.

GeoTerraImage, 2008. North West Province 2006 Land-Cover Project. Summary

MetaData and End-Users Report. Unpublished project report, June 2008

Van den Berg, E.C., Plarre, C., Van den Berg, H.M. and Thompson, M.W. 2008.

The South African National Land-cover 2000. Agricultural Research Council-

Institute for Soil, Climate and Water. Pretoria. (Report No. GW/A/2008/86).

Woodcock, C.E. & Strahler, A.H. (1987), The Factor of Scale in Remote Sensing.

RSoEnv. 21: 311-332.

30

10. Appendices

Appendix A. Data Tables

Table 10.1. Two date land-cover sequence

Change from Code

Change to Code Possible?

Urban Forestry/Plantations No

Urban Mining/Quarries No

Urban Cultivation No

Urban Other No

Forestry/Plantations Urban Yes

Forestry/Plantations Mining/Quarries Yes

Forestry/Plantations Cultivation No

Forestry/Plantations Other No

Mining/Quarries Urban No

Mining/Quarries Forestry/Plantations No

Mining/Quarries Cultivation Yes

Mining/Quarries Other Yes

Cultivation Urban Yes

Cultivation Forestry/Plantations Yes

Cultivation Mining/Quarries Yes

Cultivation Other Yes

Other Urban Yes

Other Forestry/Plantations Yes

Other Mining/Quarries Yes

Other Cultivation Yes

31

Table 10.2. Three date year-on-year land-cover sequence and the resulting logical corrections (original sequence on left, corrected sequence on right).

FAO Land-cover Change - potential Cell value combinations FAO Land-cover Change – logical corrections – modeling results

1994 2000 2005 Logical 1994 2000 � 2005

Urban/Built-up Urban/Built-up Urban/Built-up Y 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Urban/Built-up Forestry/Plantations N 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Urban/Built-up Mining/Quarries N 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Urban/Built-up Cultivation N 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Urban/Built-up Other X 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Forestry/Plantations Urban/Built-up X 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Forestry/Plantations Forestry/Plantations X 2;2;2 Forestry/Plantations Forestry/Plantations � Forestry/Plantations 2;2;2

Urban/Built-up Forestry/Plantations Mining/Quarries N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Forestry/Plantations Cultivation N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Forestry/Plantations Other N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Mining/Quarries Urban/Built-up X 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Mining/Quarries Forestry/Plantations N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Mining/Quarries Mining/Quarries Y 3;3;3 Mining/Quarries Mining/Quarries � Mining/Quarries 3;3;3

Urban/Built-up Mining/Quarries Cultivation N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Mining/Quarries Other N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Cultivation Urban/Built-up X 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Cultivation Forestry/Plantations N mapping error

Mapping Error Mapping Error � Mapping Error 0;0;0

32

Urban/Built-up Cultivation Mining/Quarries N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Cultivation Cultivation X 4;4;4 Cultivation Cultivation � Cultivation 4;4;4

Urban/Built-up Cultivation Other N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Other Urban/Built-up X 1;1;1 Urban/Built-up Urban/Built-up � Urban/Built-up 1;1;1

Urban/Built-up Other Forestry/Plantations N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Other Mining/Quarries N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Other Cultivation N mapping error Mapping Error Mapping Error � Mapping Error 0;0;0

Urban/Built-up Other Other X 5;5;5 Other Other � Other 5;5;5


1994 2000 2005 Logical 1994 2000 2005 Logical

Forestry/Plantations Urban/Built-up Urban/Built-up Y 2;1;1 Forestry/Plantations Urban/Built-up Urban/Built-up Y 2;1;1

Forestry/Plantations Urban/Built-up Forestry/Plantations X 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations X 2;2;2

Forestry/Plantations Urban/Built-up Mining/Quarries N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Urban/Built-up Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Urban/Built-up Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Forestry/Plantations Urban/Built-up Y 2;2;1 Forestry/Plantations Forestry/Plantations Urban/Built-up Y 2;2;1

Forestry/Plantations Forestry/Plantations Forestry/Plantations Y 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations Y 2;2;2

Forestry/Plantations Forestry/Plantations Mining/Quarries Y 2;2;3 Forestry/Plantations Forestry/Plantations Mining/Quarries Y 2;2;3

Forestry/Plantations Forestry/Plantations Cultivation Y 2;2;4

Forestry/Plantations Forestry/Plantations Cultivation Y 2;2;4

33

Forestry/Plantations Forestry/Plantations Other Y 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations Y 2;2;2

Forestry/Plantations Mining/Quarries Urban/Built-up N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Mining/Quarries Forestry/Plantations X 2;2;2 Forestry/Plantations Mining/Quarries Forestry/Plantations X 2;2;2

Forestry/Plantations Mining/Quarries Mining/Quarries Y 2;3;3 Forestry/Plantations Mining/Quarries Mining/Quarries Y 2;3;3

Forestry/Plantations Mining/Quarries Cultivation Y 2;3;4 Forestry/Plantations Mining/Quarries Cultivation Y 2;3;4

Forestry/Plantations Mining/Quarries Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Cultivation Urban/Built-up N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Cultivation Forestry/Plantations X 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations X 2;2;2

Forestry/Plantations Cultivation Mining/Quarries N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Cultivation Cultivation X 4;4;4 Cultivation Cultivation Cultivation X 4;4;4

Forestry/Plantations Cultivation Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Other Urban/Built-up N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Other Forestry/Plantations X 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations X 2;2;2

Forestry/Plantations Other Mining/Quarries N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Other Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Forestry/Plantations Other Other X 5;5;5 Other Other Other X 5;5;5

34


1994 2000 2005 Logical 1994 2000 2005 Logical

Mining/Quarries Urban/Built-up Urban/Built-up X 1;1;1 Urban/Built-up Urban/Built-up Urban/Built-up X 1;1;1

Mining/Quarries Urban/Built-up Forestry/Plantations N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Urban/Built-up Mining/Quarries X 3;3;3 Mining/Quarries Mining/Quarries Mining/Quarries X 3;3;3

Mining/Quarries Urban/Built-up Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Urban/Built-up Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Forestry/Plantations Urban/Built-up N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Forestry/Plantations Forestry/Plantations X 2;2;2 Forestry/Plantations Forestry/Plantations Forestry/Plantations X 2;2;2

Mining/Quarries Forestry/Plantations Mining/Quarries X 3;3;3 Mining/Quarries Mining/Quarries Mining/Quarries X 3;3;3

Mining/Quarries Forestry/Plantations Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Forestry/Plantations Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Mining/Quarries Mining/Quarries Urban/Built-up X 3;3;3 Mining/Quarries Mining/Quarries Mining/Quarries X 3;3;3

Mining/Quarries Mining/Quarries Forestry/Plantations X 3;3;3 Mining/Quarries Mining/Quarries Mining/Quarries X 3;3;3

Mining/Quarries Mining/Quarries Mining/Quarries Y 3;3;3 Mining/Quarries Mining/Quarries Mining/Quarries Y 3;3;3

Mining/Quarries Mining/Quarries Cultivation Y 3;3;4 Mining/Quarries Mining/Quarries Cultivation Y 3;3;4

Mining/Quarries Mining/Quarries Other Y 3;3;5 Mining/Quarries Mining/Quarries Other Y 3;3;5

Mining/Quarries Cultivation Urban/Built-up Y 3;4;1 Mining/Quarries Cultivation Urban/Built-up Y 3;4;1

Mining/Quarries Cultivation Forestry/Plantations Y 3;4;2 Mining/Quarries Cultivation Forestry/Plantations Y 3;4;2

Mining/Quarries Cultivation Mining/Quarries Y 3;4;3 Mining/Quarries Cultivation Mining/Quarries Y 3;4;3

Mining/Quarries Cultivation Cultivation Y 3;4;4

Mining/Quarries Cultivation Cultivation Y 3;4;4

35

Mining/Quarries Cultivation Other Y 3;4;5 Mining/Quarries Cultivation Other Y 3;4;5

Mining/Quarries Other Urban/Built-up Y 3;5;1 Mining/Quarries Other Urban/Built-up Y 3;5;1

Mining/Quarries Other Forestry/Plantations Y 3;5;2 Mining/Quarries Other Forestry/Plantations Y 3;5;2

Mining/Quarries Other Mining/Quarries Y 3;3;3 Mining/Quarries Other Mining/Quarries Y 3;3;3

Mining/Quarries Other Cultivation Y 3;5;4 Mining/Quarries Other Cultivation Y 3;5;4

Mining/Quarries Other Other Y 3;5;5 Mining/Quarries Other Other Y 3;5;5


1994 2000 2005 Logical 1994 2000 2005 Logical

Cultivation Urban/Built-up Urban/Built-up Y 4;1;1 Cultivation Urban/Built-up Urban/Built-up Y 4;1;1

Cultivation Urban/Built-up Forestry/Plantations N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Cultivation Urban/Built-up Mining/Quarries N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Cultivation Urban/Built-up Cultivation X 4;4;4 Cultivation Cultivation Cultivation X 4;4;4

Cultivation Urban/Built-up Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Cultivation Forestry/Plantations Urban/Built-up Y 4;2;1 Cultivation Forestry/Plantations Urban/Built-up Y 4;2;1

Cultivation Forestry/Plantations Forestry/Plantations Y 4;2;2 Cultivation Forestry/Plantations Forestry/Plantations Y 4;2;2

Cultivation Forestry/Plantations Mining/Quarries Y 4;2;3 Cultivation Forestry/Plantations Mining/Quarries Y 4;2;3

Cultivation Forestry/Plantations Cultivation X 4;4;4 Cultivation Cultivation Cultivation X 4;4;4

Cultivation Forestry/Plantations Other N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Cultivation Mining/Quarries Urban/Built-up N mapping error

Mapping Error Mapping Error Mapping Error N 0;0;0

36

Cultivation Mining/Quarries Forestry/Plantations N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Cultivation Mining/Quarries Mining/Quarries Y 4;3;3 Cultivation Mining/Quarries Mining/Quarries Y 4;3;3

Cultivation Mining/Quarries Cultivation Y 4;3;4 Cultivation Mining/Quarries Cultivation Y 4;3;4

Cultivation Mining/Quarries Other Y 4;3;5 Cultivation Mining/Quarries Other Y 4;3;5

Cultivation Cultivation Urban/Built-up Y 4;4;1 Cultivation Cultivation Urban/Built-up Y 4;4;1

Cultivation Cultivation Forestry/Plantations Y 4;4;2 Cultivation Cultivation Forestry/Plantations Y 4;4;2

Cultivation Cultivation Mining/Quarries Y 4;4;3 Cultivation Cultivation Mining/Quarries Y 4;4;3

Cultivation Cultivation Cultivation Y 4;4;4 Cultivation Cultivation Cultivation Y 4;4;4

Cultivation Cultivation Other Y 4;4;5 Cultivation Cultivation Other Y 4;4;5

Cultivation Other Urban/Built-up Y 4;5;1 Cultivation Other Urban/Built-up Y 4;5;1

Cultivation Other Forestry/Plantations Y 4;5;2 Cultivation Other Forestry/Plantations Y 4;5;2

Cultivation Other Mining/Quarries Y 4;5;3 Cultivation Other Mining/Quarries Y 4;5;3

Cultivation Other Cultivation Y 4;5;4 Cultivation Other Cultivation Y 4;5;4

Cultivation Other Other Y 4;5;5 Cultivation Other Other Y 4;5;5

37


1994 2000 2005 Logical 1994 2000 2005 Logical

Other Urban/Built-up Urban/Built-up Y 5;1;1 Other Urban/Built-up Urban/Built-up Y 5;1;1

Other Urban/Built-up Forestry/Plantations N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Urban/Built-up Mining/Quarries N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Urban/Built-up Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Urban/Built-up Other X 5;5;5 Other Other Other X 5;5;5

Other Forestry/Plantations Urban/Built-up Y 5;2;1 Other Forestry/Plantations Urban/Built-up Y 5;2;1

Other Forestry/Plantations Forestry/Plantations Y 5;2;2 Other Forestry/Plantations Forestry/Plantations Y 5;2;2

Other Forestry/Plantations Mining/Quarries Y 5;2;3 Other Forestry/Plantations Mining/Quarries Y 5;2;3

Other Forestry/Plantations Cultivation N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Forestry/Plantations Other X 5;5;5 Other Other Other X 5;5;5

Other Mining/Quarries Urban/Built-up N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Mining/Quarries Forestry/Plantations N mapping error Mapping Error Mapping Error Mapping Error N 0;0;0

Other Mining/Quarries Mining/Quarries Y 5;3;3 Other Mining/Quarries Mining/Quarries Y 5;3;3

Other Mining/Quarries Cultivation Y 5;3;4 Other Mining/Quarries Cultivation Y 5;3;4

Other Mining/Quarries Other Y 5;3;5 Other Mining/Quarries Other Y 5;3;5

Other Cultivation Urban/Built-up Y 5;4;1 Other Cultivation Urban/Built-up Y 5;4;1

Other Cultivation Forestry/Plantations Y 5;4;2 Other Cultivation Forestry/Plantations Y 5;4;2

Other Cultivation Mining/Quarries Y 5;4;3 Other Cultivation Mining/Quarries Y 5;4;3

Other Cultivation Cultivation Y 5;4;4

Other Cultivation Cultivation Y 5;4;4

38

Other Cultivation Other Y 5;4;5 Other Cultivation Other Y 5;4;5

Other Other Urban/Built-up Y 5;5;1 Other Other Urban/Built-up Y 5;5;1

Other Other Forestry/Plantations Y 5;5;2 Other Other Forestry/Plantations Y 5;5;2

Other Other Mining/Quarries Y 5;5;3 Other Other Mining/Quarries Y 5;5;3

Other Other Cultivation Y 5;5;4 Other Other Cultivation Y 5;5;4

Other Other Other Y 5;5;5 Other Other Other Y 5;5;5

39

Table 10.3. Cell changes resulting from logical corrections

Normalising the data: Logical corrections made to the cell values based on the pairing possibilities and the accepted 3 date sequences

Year: 1994 Year: 2000 Year: 2005

Class Unaltered Percentage

Logically Corrected Percentage

Total Percentage



Total Percentage



Total Percentage

1 - Urban 86.09% 13.91% 100.00% 1 - Urban 99.41% 0.59% 100.00% 1 - Urban 98.58% 1.42% 100.00%

2 - Forestry 91.13% 8.87% 100.00% 2 - Forestry 94.10% 5.90% 100.00% 2 - Forestry 99.81% 0.19% 100.00%

3 - Mining 98.51% 1.49% 100.00% 3 - Mining 94.78% 5.22% 100.00% 3 - Mining 97.84% 2.16% 100.00%

4 - Cultivated 99.84% 0.16% 100.00% 4 - Cultivated 99.55% 0.45% 100.00% 4 - Cultivated 99.17% 0.83% 100.00%

Mp

um

ala

ng

a

5 - Other 99.90% 0.10% 100.00% 5 - Other 97.85% 2.15% 100.00% 5 - Other 99.69% 0.31% 100.00%


Year: 1994 Year: 2000 Year: 2005



Total Percentage



Total Percentage



Total Percentage





Easte

rn C

ap

e


40


Year: 1994 Year: 2000 Year: 2005



Total Percentage Class Unaltered Percentage


Total Percentage



Total Percentage





Fre

e S

tate



Year: 1994 Year: 2000 Year: 2005





Total Percentage



Total Percentage





Gau

ten

g


41


Year: 1994 Year: 2000 Year: 2005





Total Percentage



Total Percentage





Kw

aZ

ulu

Nata

l



Year: 1994 Year: 2000 Year: 2005







Total Percentage





Leso

tho


42


Year: 1994 Year: 2000 Year: 2005







Total Percentage





Lim

po

po



Year: 1994 Year: 2000 Year: 2005







Total Percentage





No

rthern

Cap

e


43


Year: 1994 Year: 2000 Year: 2005





Total Percentage



Total Percentage





No

rth W

est



Year: 1994 Year: 2000 Year: 2005







Total Percentage





Sw

azila

nd


44


Year: 1994 Year: 2000 Year: 2005







Total Percentage





We

ste

rn C

ap

e


45

Appendix B. Results Summaries

Table 10.4. Eastern Cape land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 26.0% 0.0% 74.0% 100%

Cultivation 4.2% 0.3% 0.0% 18.6% 76.9% 100%

Ye

ar: 1

99

4

Open 0.9% 0.2% 0.0% 1.6% 97.4% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 99.9% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 89.8% 2.0% 8.2% 100%

Cultivation 0.7% 0.1% 0.0% 55.2% 44.0% 100%

Yea

r: 20

00

Open 0.2% 0.1% 0.0% 6.9% 92.8% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 99.9% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 30.0% 8.0% 62.0% 100%

Cultivation 4.8% 0.5% 0.0% 46.7% 48.0% 100%

Ye

ar: 1

99

4

Open 1.0% 0.3% 0.0% 4.9% 93.8% 100%

Error count: 1929 0.24%

Total Number of Cells 797252

46

Table 10.5. Free State Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 90.1% 9.9% 0.0% 0.0% 100%

Mining 0.0% 0.0% 52.4% 0.9% 46.7% 100%

Cultivation 0.3% 0.0% 0.0% 67.9% 31.8% 100%

Ye

ar: 1

99

4

Open 0.2% 0.0% 0.0% 6.2% 93.6% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 99.4% 0.0% 0.6% 0.0% 100%

Mining 0.0% 0.0% 95.2% 0.3% 4.5% 100%

Cultivation 0.0% 0.0% 0.0% 91.4% 8.5% 100%

Ye

ar: 2

00

0

Open 0.2% 0.1% 0.0% 10.3% 89.4% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 89.3% 9.9% 0.8% 0.0% 100%

Mining 1.6% 0.3% 61.1% 2.7% 34.3% 100%

Cultivation 0.4% 0.1% 0.0% 79.8% 19.7% 100%

Yea

r: 19

94

Open 0.4% 0.1% 0.1% 9.5% 90.0% 100%



47

Table 10.6. Gauteng Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 67.0% 1.0% 32.0% 100%

Cultivation 5.7% 0.2% 0.4% 43.1% 50.7% 100%

Ye

ar: 1

99

4

Open 4.9% 0.3% 0.4% 7.0% 87.3% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 10.1% 87.6% 1.9% 1.9% 0.0% 101%

Mining 0.0% 0.0% 70.2% 2.9% 27.0% 100%

Cultivation 1.5% 0.1% 0.2% 89.8% 8.5% 100%

Ye

ar: 2

00

0

Open 4.9% 0.4% 0.4% 25.7% 68.6% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 5.1% 92.2% 0.0% 2.7% 0.0% 100%

Mining 6.2% 1.0% 60.3% 2.6% 29.8% 100%

Cultivation 8.3% 0.5% 0.3% 74.0% 16.8% 100%

Yea

r: 19

94

Open 9.4% 0.5% 0.4% 20.7% 69.0% 100%



48

Table 10.7. KwaZulu Natal Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 35.8% 4.0% 60.2% 100%

Cultivation 1.4% 1.5% 0.0% 45.1% 52.0% 100%

Ye

ar: 1

99

4

Open 0.5% 1.4% 0.0% 6.7% 91.3% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.2% 99.0% 0.8% 0.8% 0.0% 101%

Mining 0.0% 0.0% 56.9% 1.5% 41.6% 100%

Cultivation 5.9% 1.2% 0.0% 57.9% 35.0% 100%

Ye

ar: 2

00

0

Open 1.7% 1.0% 0.0% 6.9% 90.4% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.1% 98.9% 0.0% 1.0% 0.0% 100%

Mining 4.7% 1.8% 28.8% 2.6% 62.0% 100%

Cultivation 6.3% 2.4% 0.0% 49.1% 42.3% 100%

Yea

r: 19

94

Open 2.1% 2.4% 0.0% 5.8% 89.6% 100%



49

Table 10.8. Lesotho Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 83.3% 0.0% 16.7% 100%

Cultivation 2.5% 0.0% 0.0% 50.1% 47.4% 100%

Ye

ar: 1

99

4

Open 0.1% 0.0% 0.0% 8.3% 91.6% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 92.3% 0.0% 7.7% 100%

Cultivation 1.6% 0.0% 0.0% 56.2% 42.2% 100%

Ye

ar: 2

00

0

Open 0.1% 0.0% 0.0% 7.4% 92.5% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 83.3% 0.0% 16.7% 100%

Cultivation 3.8% 0.1% 0.0% 46.7% 49.4% 100%

Yea

r: 19

94

Open 0.3% 0.1% 0.0% 6.9% 92.8% 100%



50

Table 10.9. Limpopo Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 64.8% 3.1% 32.1% 100%

Cultivation 4.7% 0.2% 0.1% 54.0% 41.0% 100%

Ye

ar: 1

99

4

Open 1.0% 0.2% 0.1% 4.8% 93.9% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.8% 96.9% 2.3% 2.3% 0.0% 102%

Mining 0.0% 0.0% 83.7% 1.7% 14.6% 100%

Cultivation 4.5% 0.2% 0.0% 65.7% 29.6% 100%

Ye

ar: 2

00

0

Open 1.0% 0.1% 0.1% 3.3% 95.6% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.4% 96.4% 0.0% 3.2% 0.0% 100%

Mining 1.3% 0.0% 66.8% 2.5% 29.4% 100%

Cultivation 9.0% 0.3% 0.1% 48.1% 42.4% 100%

Yea

r: 19

94

Open 1.9% 0.3% 0.1% 4.3% 93.5% 100%



51

Table 10.10. Mpumalanga Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 99.9% 0.1% 0.0% 0.0% 100%

Mining 0.0% 0.0% 40.6% 10.8% 48.7% 100%

Cultivation 1.5% 0.5% 0.4% 57.9% 39.7% 100%

Ye

ar: 1

99

4

Open 1.1% 2.2% 0.2% 6.1% 90.4% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.3% 99.6% 0.2% 0.2% 0.0% 100%

Mining 0.0% 0.0% 76.6% 6.6% 16.8% 100%

Cultivation 1.8% 0.4% 1.1% 85.7% 10.9% 100%

Ye

ar: 2

00

0

Open 1.1% 1.5% 0.5% 13.2% 83.7% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.2% 99.5% 0.1% 0.2% 0.0% 100%

Mining 2.2% 0.9% 59.1% 7.1% 30.7% 100%

Cultivation 3.1% 1.1% 1.1% 76.3% 18.5% 100%

Yea

r: 19

94

Open 2.2% 3.7% 0.4% 12.4% 81.4% 100%



52

Table 10.11. North West Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 48.9% 2.2% 48.9% 100%

Cultivation 0.6% 0.0% 0.1% 67.1% 32.2% 100%

Ye

ar: 1

99

4

Open 0.5% 0.0% 0.2% 10.2% 89.1% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.9% 97.3% 1.8% 1.8% 0.0% 102%

Mining 0.0% 0.0% 59.4% 1.6% 39.0% 100%

Cultivation 0.2% 0.0% 0.1% 77.8% 21.9% 100%

Ye

ar: 2

00

0

Open 0.3% 0.1% 0.1% 7.7% 91.9% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 97.6% 0.0% 2.4% 0.0% 100%

Mining 3.0% 1.3% 45.7% 2.8% 47.2% 100%

Cultivation 0.9% 0.1% 0.2% 72.9% 25.9% 100%

Yea

r: 19

94

Open 0.7% 0.0% 0.2% 10.1% 88.9% 100%



53

Table 10.12. Northern Cape Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 0.0% 0.0% 0.0% 0.0% 0%

Mining 0.0% 0.0% 62.4% 0.1% 37.5% 100%

Cultivation 0.7% 0.1% 0.1% 51.0% 48.2% 100%

Ye

ar: 1

99

4

Open 0.1% 0.0% 0.1% 0.2% 99.6% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 80.1% 0.2% 19.6% 100%

Cultivation 0.3% 0.0% 0.0% 76.0% 23.7% 100%

Ye

ar: 2

00

0

Open 0.0% 0.0% 0.3% 99.7% 0.0% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 0.0% 0.0% 0.0% 0.0% 0%

Mining 0.3% 0.0% 66.5% 0.0% 33.2% 100%

Cultivation 1.2% 0.1% 0.1% 53.3% 45.3% 100%

Yea

r: 19

94

Open 0.2% 0.0% 0.1% 0.3% 99.5% 100%



54

Table 10.13. Swaziland Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 45.5% 0.0% 54.5% 100%

Cultivation 3.6% 0.0% 0.0% 37.3% 59.1% 100%

Ye

ar: 1

99

4

Open 2.2% 1.1% 0.0% 6.9% 89.8% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 7.7% 0.0% 92.3% 100%

Cultivation 0.7% 0.0% 0.0% 26.6% 72.7% 100%

Ye

ar: 2

00

0

Open 0.3% 0.7% 0.0% 1.4% 97.7% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 9.1% 0.0% 90.9% 100%

Cultivation 4.0% 0.0% 0.0% 16.5% 79.4% 100%

Yea

r: 19

94

Open 2.5% 1.8% 0.0% 1.5% 94.3% 100%



55

Table 10.14. Western Cape Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.0% 100.0% 0.0% 0.0% 0.0% 100%

Mining 0.0% 0.0% 55.4% 21.4% 23.2% 100%

Cultivation 0.6% 0.2% 0.0% 80.5% 18.7% 100%

Ye

ar: 1

99

4

Open 0.2% 0.2% 0.0% 3.7% 95.9% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 1.2% 98.6% 0.2% 0.2% 0.0% 100%

Mining 0.0% 0.0% 85.0% 0.0% 15.0% 100%

Cultivation 0.5% 0.1% 0.0% 82.3% 17.1% 100%

Ye

ar: 2

00

0

Open 0.1% 0.1% 0.0% 2.6% 97.1% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.6% 99.0% 0.0% 0.3% 0.0% 100%

Mining 0.0% 0.0% 39.3% 5.4% 55.4% 100%

Cultivation 1.2% 0.3% 0.0% 77.5% 20.9% 100%

Yea

r: 19

94

Open 0.4% 0.3% 0.0% 3.2% 96.1% 100%



56

Table 10.15. National Land-cover change

Year: 2000


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.4% 99.5% 0.1% 0.0% 0.0% 100%

Mining 0.0% 0.0% 53.2% 4.1% 42.7% 100%

Cultivation 1.7% 0.3% 0.1% 58.4% 39.5% 100%

Ye

ar: 1

99

4

Open 0.5% 0.3% 0.1% 3.5% 95.7% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.3% 99.2% 0.5% 0.5% 0.0% 100%

Mining 0.0% 0.0% 76.0% 2.3% 21.8% 100%

Cultivation 1.5% 0.2% 0.1% 76.8% 21.4% 100%

Ye

ar: 2

00

0

Open 0.4% 0.2% 0.1% 4.7% 94.7% 100%

Year: 2005


Urban 100.0% 0.0% 0.0% 0.0% 0.0% 100%

Forestry 0.6% 98.7% 0.1% 0.6% 0.0% 100%

Mining 2.5% 0.7% 58.2% 3.4% 35.2% 100%

Cultivation 3.2% 0.5% 0.2% 65.3% 30.9% 100%

Yea

r: 19

94

Open 0.9% 0.5% 0.1% 4.4% 94.1% 100%



57

Appendix C. Maps

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