Journal of Global Resources
Volume 5July 2017
ISSN: 2395-3160 (Print), 2455-2445 (Online)

Page 20-2704
IMPACT OF LAND USE LAND COVER CHANGE ON RURAL TOWN OFTHOHOYANDOU, SOUTH AFRICA
O. A. Osidele1 N.S. Nethengwe2 and F. Dondofema3
1

Department of Geography & Geo-Information Sciences, School of Environmental Sciences,
University of Venda, Thohoyandou, South-Africa.
Email: jimmyosidele@yahoo.com

2

Head of department/deputy Dean, Department of Geography & Geo-Information Sciences,
School of Environmental Sciences, University of Venda, Thohoyandou, South-Africa.
Email: nthaduleni.nethengwe@univen.ac.za
3GIS Resource Center, Department of Geography & Geo-Information Sciences, School ofEnvironmental Sciences, University of Venda, Thohoyandou, South-Africa.
Email: Farai.Dondofema@univen.ac.za

Abstract: Over the years, various human activities such as housing, agricultural,commercial and industrial activities, have led to land use/land cover changes in space andtime on Earth. The study utilises Geographic Information Systems and remote sensing
techniques to detect the extent of land use land cover changes on the rural town ofThohoyandou. The analysis is based on satellite images of Thohoyandou between 2004 and2013 using GIS tools and applications. The satellite images used to detect changes in land
use land cover within the study area were of 2004, 2008, 2009 and 2013. Land use
identification and classification techniques were also employed to map relevant changes that
occurred within the year intervals. The findings reflect the extent to which residential andcommercial activities have expanded in the town, and how this resulted in the present land
cover change in the study area.
Key words: Change Detection; Land use/Land cover; GIS and Remote Sensing
Introduction
The quest for human habitation and development leads to different anthropogenic activities,
that in turn affect and change land use land cover patterns within different geographicallocations (Meyer, 2005). This contributes to spatial patterns of such locations as seen fromspace at different time intervals (years). Land use and land cover changes, especially thosecaused by human activities are the most important components of global environmental
change with possibly the greatest impacts of any other global changes (Turner et.al. 2007;
Jensen, 2005). The rural town of Thohoyandou is experiencing significant influx of people
from neighboring villages seeking employment and economic opportunities. The larger
populations of these people are within the economic and working class group. They often
see Thohoyandou as the nearest town where more opportunities and better living condition
can be found when compared to their various villages. This resulted into changes in thespatial organisation of the town as well as changes in land usage by providing land areas for20residential, recreational and administrative purpose. The result from this research waspresented orally by the first author at the 33rd IGU Congress 21 – 25 August, 2016 held atBeijing, Republic of China.
Aim and Objectives
The aim of the study was to evaluate the spatial land use land cover changes that haveoccurred in the rural town of Thohoyandou.
The research is geared towards achieving the following objectives, namely to:• Identify and describe land use change patterns during the two five year intervals,(2004-2008 and 2009-2013);• Evaluate the impact of these changes on overall rural expansion of the study area.
Study Area
Thohoyandou is located in the Thulamela Local Municipality at the eastern tip of the Vhembe
District in the Limpopo Province of South Africa. It is situated along the northern side of thePunda Maria route (R524) between Louis Trichardt and Kruger National Park. It is thesecond largest town in the Vhembe District. Figure 1 shows Thohoyandou’s locational site inrelation to the Thulamela Local Municipality, Vhembe District and Limpopo Province. The
study area Thohoyandou (means head of the Elephant) lies approximately between latitude
220 57'00'' south and longitude 300 28' 59''east. The elevation is about 734 meters (2408feet)above sea level.
Figure 1: Map showing the study area21

Thohoyandou was chosen because it is a notable and the most importantcommercial rural town in the Thulamela Municipality, due to its social-economic growth andpopulation increase. The population of Thohoyandou was about 39,756 in 2004 and 69,453
in 2011 (SA census 2011). The population growth rate pose challenging demand for open
spaces to be converted into residential and commercial purposes in other to serve the needof the yearly growing population. The climate of Thohoyandou is characterised by hot to very
hot summer months with afternoon thunderstorms. The winter months are also sunny butdry, with temperatures dropping considerably in the mornings and evenings. Daily
temperatures vary from 17oc to 40oc in summer and between 4oc and 26oc in winter. Figure 2
shows the layout map of Thohoyandou in relation to the zoning demarcation of the town.
Figure 2: Layout map showing zoning demarcation of the study area

Methodology and Data set
The systematic sampling frame was the major transportation route of R524, R523 andD3717 road within Thohoyandou. All areas that fell within this sampling frame which are; theCentral Business District, Unit D, P East, Block F, and Block G residential areas were taken
as unit of analysis for this study. The changes observed over a particular period were indescriptive form. GIS and remote sensing techniques were used for assessing and analysing
satellite images of the study area, to detect changes in land use during the study period(Olaleye 2006). For this study, four satellite image scenes for the years 2004, 2008, 2009
and 2013 with multi-spectral band and suitable radiometric resolution were used (see Table1). This was guided by the available satellite image of the study area. GIS and RS
techniques were used to analyse and study the spatial pattern land use changes in ArcGIS

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10.The land area demarcation for the study area was 4263 hectares, and was consideredthroughout the study period.

Satellite Data
SPOT 4SPOT 4SPOT 4SPOT 4

Table 1: Satellite Image used in the study
DateResolution
21 – 11 – 2004
30m
30 – 08 – 2008
30m
25 – 10 – 200930m
27 – 09 – 2013
30m

Band4444Classification of Images
SPOT 4 satellite images of 2004, 2008, 2009 and 2013 obtained from South Africa National
Space Agency (SANSA) have been pre-processed and corrected for both geometric andradiometric correction. The images were then digitally supervised classified using maximum
likelihood classification method. This was followed by a ground truthing field survey of thearea. Prior knowledge of land use categories of the study area assisted greatly in achieving
good and visible interpretation of the different satellite images considered for use in theresearch. Land use land cover map for different years were produced from classification
output of satellite images and the result obtained from comparing the study year intervals(2004-2008) and (2009-2013). Extraction of the study area by mask was done to limit thesatellite image of Thohoyandou to areas within the sampling frame. The classifications
accuracy assessment was evaluated by the Kappa indexes (Mallupattu, et.al 2013). The
land use classification Kappa index obtained was above 0.60 and about 67% accuracy.
While the land cover classification Kappa index obtained was above 0.65 and accuracy
above 70%.Finally, the results were presented in tables for easy numerical interpretation.
Results and Discussion
Change detection for the study period (2004-2008)
The change detection analysis was done using the map algebra raster calculator to produce
raster change classification. The percentage of the difference was calculated from the output
raster layer attribute table. The image of the study area for 2004 was regarded as the land
use pattern before change, whereas the image for 2008 was regarded as showing changes
which occurred between 2004 and 2008. In 2004 the CBD was seen as an area with fewbuilding structures, while residential areas of P East were clustered with building structures,
Unit D, Block F and Block G were residential areas with fewer structures (see Figure 3).
Forests and open areas had 29.43% and 36.33% of land usage respectively, while
residential areas had 27.65% and commercial areas 6.59% of land usage. Changes
detected during 2008, showed a gradual increase in the percentage of the land usage. Bareand open areas became 33.98%, forest areas 25.64%, while residential and commercial
areas increased to 31.56% and 8.82% respectively.

23Figure 3: Land use classification and satellite images for 2004 & 2008.

Land use Land cover analyses of the study area between 2004-2008
The land use/land cover change analysis of the first study period of 2004-2008 is presented
in Table 2 below. It shows that during 2004, commercial and administrative areas occupied
280.93 ha and residential areas occupied 1178.71 ha of land, while 1254.60 ha and 1548.75
ha of land were occupied by forests and bare areas respectively. There were changes in theland cover in 2008 with 376.00 ha devoted to commercial land area, 1345.40 ha toresidential land area, 1448.57 ha to bare /open areas and 1093.03 ha of land to forests.
From Table 2 below, the study area witnessed a loss of 3.79% of forest area and 2.35% ofbare /open area between 2004 and 2008. However, there was an increase of 2.23% and3.91% land usage to commercial and residential area land cover respectively.
Table 2: Land use land cover consumption and change analyses (2004-2008)Land
use/landcover types

Commercial andAdministrative
ResidentialForest
Bare and OpenTOTALLand Areas in hectares (ha)Land Area percentage
(%)

2004
280.93

2008
376.00

2004
6.59

2008
8.82

1178.721254.60
1548.754263

1345.40
1093.03
1448.57
4263

27.65
29.43
36.33
100

31.56
25.64
33.98
100

24

ChangeDifference(ha)ChangeDifference
(%)

95.06

+2.23

166.68161.57100.18

+3.91
-3.79
-2.35

Land use Land cover analyses of the study area between 2009-2013
The period 2009-2013 (Table 3) enjoyed remarkable development; more residential andcommercial activities occurred within the study area. In 2009, commercial land area
occupied 421.61 ha, residential areas occupied 1484.38 ha, while forests and bare/openareas occupied 1016.72 ha and 1340.29 ha respectively. This followed the same trend in
2013, with 443.78 ha devoted to commercial areas and 1972.49 ha to residential areas. Theforest cover area was 697.85 ha and the bare/open areas 1148.88 ha.
Table 3: Land use/ land cover consumption and change analyses (2009-2013)Land
use/landcover types

Commercial andAdministrative
Residential
ForestBare and OpenTOTALLand Areas in hectares(ha)Land Area percentage(%)

2009421.61

2013
443.7820099.89

2013
10.41

1484.38
1016.721340.294263

1972.49697.851148.89
4263

34.82
23.85
31.44
100

46.27
16.37
26.95
100

ChangeDifference (ha)ChangeDifference
(%)

22.17

+0.52

488.11
318.87191.41+11.45-7.48-4.49

Change detection for the study period (2009-2013)
In 2009, the increase in land usage resulted in the conversion of forests and bare areas into
built up areas for residential and commercial purposes. The percentage usage was 34.82%
for residential areas, 31.44% for bare and open areas, 23.85% for forest areas and 9.89%
commercial areas. The sequence of land use activities continued with the same trend
pattern seen in 2009, but more enormous changes had taken place in 2013 within the study
area (see Figure 4). In 2013 residential areas had a percentage usage of 46.27% and bareand open areas 26.95%. Forest areas land usage became 16.37% and commercial areas,10.41%. Though the percentage of commercial land area is small, it is enjoying a steadyincrease in land usage.
Figure 4: Land use classification and satellite images for 2009 & 2013.

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Comparison of change analysis between 2004-2008 and 2009-2013
It can be said that many changes in land usage patterns occurred between 2004 and 2013.
There were tremendous changes during the period of 2009-2013, compared to 2004-2008,
more especially the conversion of forest land into commercial, administrative and residential
purposes. It is statistically significant to note that all the land loss is from forests and bareareas converted to commercial and residential areas. The overall change comparison
analysis is presented in tabular form (see Table 4).
Table 4: Change comparison between study periods (2004-2008) and (2009-2013)Land use/land Area
Change Area Change Change
Changecover type
difference (ha) difference (ha) difference (%) difference (%)(2004-2008)(2009-2013)(2004-2008)(2009-2013)
Commercial
95.06
22.17+2.23+0.52
andAdministrative
Residential
166.68488.11+3.91+11.45
Forest161.57318.87-3.79-7.48
Bare and Open 100.18
191.41-2.35-4.49
Conclusion
The findings of the research showed that about 11% of forest land and 8% of bare and open
spaces have been converted into residential and commercial land during the study period. It
also reflected that forest land will, in future, be used up to accommodate the expansion ofthe town. Zoning and planning principles are not strict enough to ensure that land is properly
distributed, especially within the Central Business District area. Due to the haphazard
planning associated with the municipality’s negligence, the aesthetic value of the Central
Business District and some residential areas of the town, is very low. This also applies to theoverall look and landscape of the town. The fragile ecosystem, biodiversity and vegetation
are lost due to land use/land cover changes in Thohoyandou town. There were also land
pollution and poor drainage challenges within the CBD and residential areas.
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