Impact of Land use change on Wetland in Lake Victoria Basin, Tanzania
Shadrack Mwakalila
Department of Geography, University of Dar es Salaam
P. O. Box 35049, Dar es Salaam, TANZANIA
Tel: +255-713-271299; Fax: +255-22-2410393
*Corresponding author email: smwakalila@yahoo.com
ABSTRACT
This paper attempts to address how the land use change has resulted in degradation of the
wetlands in Lake Victoria basin in Tanzania. The analyses drawn upon the use of remote sensing
data coupled with some field data for assessment of wetland resources: land, forestry, agriculture
settlement, grazing, and wetland management, and highlight the physical and technical
characteristics of the resource. Landsat images used for vegetation mapping and land use/ cover
change study were from: 1985, 1995, 2001 and 2005. Landsat satellite images were used to
inform landscape qualities over broad areas under the study area. From interpretation, broad
classes of land use/land cover were established and maps prepared. After ground truthing (field
verification) was carried out, the broad classes of land use/land cover established by image
interpretation were digitised and georeferenced to UTM co-ordinates Zone 36 using ARC/INFO
and then fed into ARC/VIEW software for spatial analyses. Both supervised and unsupervised
interpretation methods were used to extract different land use/land cover classes from the images.
Visual interpretation was performed to discriminate those uses/covers which were spectrally
inseparable. The area of each land cover type was calculated for each data set and the difference
in aerial coverage between the various land cover types over time was then determined. The
results indicates progressive depletion of wetland size as seen from changes in biomass cover on
the floodplain drained by Simiyu river and its tributaries. There is progressive degradation of land
resources in the Lake Victoria basin that the bare ground increased from 11.4% in 1985 to 37% in
2005.
Keywords: land cover, Landsat satellite images, degradation of the wetlands.
INTRODUCTION
The riparian countries (Uganda, Kenya, and Tanzania) in the recent years have seen the Lake
Victoria drainage basin (LVDB) as a resource that requires rational management to protect it
from pollution and degradation (Klohn and Andjelic, 2001). For this reason they have joined to
assess the resources and the problems of the Lake, to develop management tools and to establish
adequate institutions and capacity. With the growing population the multiple activities in the lake
basin have increasingly come into conflict. This has contributed to rendering the lake
environmentally unstable. The problems have arisen in the surrounding basins due to human
activities.
Subsistence agriculture is the major activity within the Lake’s watershed, but there are significant
industrial developments. Fishing activity has exacerbated population pressure on the lake’s
ecosystem. Ownership and management jurisdiction of the lake’s aquatic resources has caused
concern. Pollution levels from farms and industries, and reclamation of wetlands for agriculture
raise concern. Poverty levels are pushing the landless to the ecologically marginal wetlands.
Therefore, the need for up-to-date and timely information on the Lake Victoria basin’s
environment for sustainable development planning is crucial. Tanzania being a country that
largely depends on its natural resources base for economic development, comprehensive
information on its resources dynamics is key in implementing the poverty alleviation strategy,
improving the human condition and preserving the biological systems upon which the country's
population depends.
Wetlands of LVDB can benefit greatly from satellite imaging because of the presence of
particular biotic assemblages that are characteristic of particular conditions of growth. The
application of Remote sensing to fully address the human aspects of land-cover change is well
documented (Running, et al., 1994; Asrar, et al. 1992; Meyer and Turner, 1994; and Myneni, et
al. 1995; Anyamba, et al., 2001; Anyamba, et al., 2002; FAO, 1995; Ojima, et al., 1994;
Houghton, 1994).
THE STUDY AREA
Although the entire area of the Lake Victoria drainage basin (LVDB) is over 184,000 km2 (Kohln
and Andlejic, 2001) this paper focus on Simiyu drainage basin of Magu district, Mwanza region.
The basin is a sub-basin of LVDB (Figure 1). This case study is purposively chosen because it
bears excellent examples of activities that threaten the sustainability of the LVDB wetlands. The
Simiyu drainage basin comprises of a diversity of habitats, including a swampy area, riverine
areas, ponds, seasonally inundated grassland, and bush land. The swampy area is dominated by
Cyperus papyrus, and is associated with sedges, reeds, cattails and other plant species. The
floodplains and drawdowns are used for agricultural purposes especially for cultivation of rice.
The Simiyu Wetland is within the 242 Km² area, the focus of this study, and extends between
33º22'30" to 33º32'00"E and 2º31'00" to 2º38'00"S. The wetland is within a east-west extending
trough (depression) and receives run off from mainly the highlands to the south. Magu Township
is the main administration center in the area.
Figure 1. The locational map of the Simiyu basin
METHODOLOGY
Landsat satellite data of 1973 (1973, 1985, 1995, and 2005 and topographic maps at a scale of
1:50 000 of 1978 constituted the input data for this study. Landsat image data were acquired from
the Regional Mapping Centre for Research and Development in Nairobi, Kenya. Remote sensing
technologies were applied in assessing (tracking) land use/cover changes at a temporal scale of a
10-year interval from 1973.The size of the farms and the vegetation cover changes mapped,
coupled with the pixel size of the chosen satellite remote sensing data types, together provided the
potential for accurate remote sensing assessment of the changes in land use and land cover in the
four study sites. Image interpretation was made based on land cover types and supervised land
cover classification computed from the various image dates. Tonal, hue values and image texture
and structure were considered in making decisions on the land cover changes. The Landsat
satellite data sets that were acquired and used for the analysis are given in Table 1.
Table 1 Landsat satellite data sets for Simiyu Basin
Mwanza
TM
P170/r062
Mar. 1985
TM
P170/r062
Sept1995
ETM
P170/r062
May 2001
ETM
P170/r062
Jun2005
From interpretation, broad classes of land use/land cover were established and maps prepared.
After ground truthing (field verification) was carried out, the broad classes of land use/land cover
established by image interpretation were digitised and georeferenced to UTM co-ordinates Zone
36 using ARC/INFO and then fed into ARC/VIEW software for spatial analyses.
Both supervised and unsupervised interpretation methods were used to extract different land
use/land cover classes from the images. Visual interpretation was performed to discriminate those
uses/covers which were spectrally inseparable. The area of each land cover type was calculated
for each data set and the difference in aerial coverage between the various land cover types over
time was then determined.
RESULTS AND DISCUSSION
The land cover classification results from Satellite Images analysis, shows the area of land
cover/use and the percentage (%) of those areas for each of the imaging date (see Table 2). Broad
classes of land use/cover established by interpretation of Satellite Images of the study area were,
bare ground, burnt area, clear water marshland vegetation, drained wetland, grassland, shrubland,
riverine vegetation,farmland/cultivated areas, urban areas/settlement, forest and woodland. Forest is
vegetation consisting of large and dense trees with definite bole or trunk can be natural or artificial,
while woodland is vegetation consisting of trees and shrubs but not as dense as forest. Shrubs are
trees that do not have definite bole or trunk. Farmland refers to land cultivated. Riverine vegetation is
the vegetation along the river, which may be forest or woodland and urban areas or settlement is the
area consisting of buildings where people reside.
Table 2 Land Cover Classification Analysis
Land cover/use
Bare Ground
Burnt Area
Clear Water
Dense Marshland Vegetation
Drained Wetland
Grassland
Open Shrubland
Riverine Vegetation
Silted Water
Small Scale Farmland
Large Scale Farmland
Wooded Shrubland
Marshland Vegetation
Urban Areas
Indigenous Forest/vegetation
1985
Area (km2)
150.0
12.0
190.0
85.0
18.0
17.0
310.0
71.0
100.0
65.0
0.0
63.0
150.0
27.0
1.5
1995
2
% Area (km )
11.4
261.9
0.9
18.7
14.5
158.6
6.5
14.9
1.4
9.8
12.8
185.8
23.4
193.5
5.5
66.5
8.0
135.8
5.0
63.7
0.0
32.9
4.8
44.1
3.6
81.3
2.1
10.5
0.1
0
2001
2
% Area (km )
20.5
241.1
1.5
114.1
12.4
208.3
1.2
19.6
0.8
5.3
14.5
180.8
15.1
300.2
5.2
3.3
10.6
84.2
5.0
61.2
2.6
37.4
3.4
62.8
6.4
72.7
0.8
12.9
0.0
0.0
2005
2
% Area (km )
16.6
473.5
7.8
16.5
14.3
45.1
1.3
18.4
0.4
6.3
12.4
166.9
20.6
21.4
0.2
97.9
5.8
244.9
4.2
19.7
2.6
36.2
4.3
48.1
8.4
67.2
0.9
15.4
0.0
0.0
%
37.0
1.3
3.5
1.4
0.5
13.1
1.7
7.7
19.2
1.5
2.8
3.8
5.2
1.2
0.0
All images were geo-referenced and enhanced to facilitate interpretation. The following were
interpreted/extracted from the image data/information (see also Figure 2- 6).
From 1985 to 2005 indicates progressive depletion of wetland size as seen from changes in
biomass cover on the floodplain drained by Simiyu river and its tributaries. There is progressive
degradation of land resources in the Simiyu basin as it can be observed in Table 2 above, that the
bare ground increased from 11.4% in 1985 to 37% in 2005. Also due to land degradation,
indigenous forest has been disappeared completely. Beyond the valley of river Simiyu (northern
side) it shows that, there was initially some settlement which has disappeared.
There is observed increase in erosion, presumably due to increased density of cattle (livestock) as
it was confirmed during the ground truthing. The river’s tributaries have reduced with time
probably due to grazing which leads to high evaporation faster.
Initially the flood plain had high water table in the valley (evidence from decrease in area covered
by clear water, marshland vegetation density). In 1985 there was high water-level on flood plain
seen from the flooded flood plain. In 2001 there was increased human settlement along the
levees. 2005 cultivation takes places in the wetland and there is complete drainage of wetland.
Settlement is extended to the levees and cultivation is extended to the flood plain. There is
reduction of cultivation on the higher slopes of the flood plain and the people have moved to the
levees of the river system.
Figure 2. Land cover classification for Simiyu basin-1985.
Figure 3. Land cover classification for Simiyu basin-1995.
Figure 4. Land cover classification for Simiyu basin-2001.
Figure 5. Land cover classification for Simiyu basin-2005.
CONCLUSION
The application of Remote Sensing Technique has shown to be instrumental to assess the impact
of wetland resources utilization. Observation from the satellite images indicates progressive
depletion of wetland size as seen from changes in biomass cover on the floodplain drained by
Simiyu river and its tributaries. There is progressive degradation of land resources in the Simiyu
basin that the bare ground increased from 11.4% in 1985 to 37% in 2005. There is observed
increase in erosion, presumably due to increased density of cattle (livestock) as it was confirmed
during the ground truthing. The river’s tributaries have reduced with time probably due to grazing
which leads to high evaporation faster.
REFERENCE
Anyamba, A., Linthicum, K.J., Mahoney, R., Tucker, C.J. and Kelley, P.W. (2002): Mapping
potential risk of Rift Valley fever outbreaks in African savannas using vegetation index
time series data. Photogrammetric Engineering & Remote Sensing, 68(2): 137-145.
Anyamba, A., Tucker, C.J. and Eastman, J.R. (2001): NDVI anomaly patterns over Africa during
the 1997/98 ENSO warm events. International Journal of Remote Sensing, 22(10): 18471859.
Asrar, G., Mynem, R.B. & Choudhury, B.J. (1992). Spatial heterogeneity in vegetation canopies
and remote sensing of absorbed photosythetically active radiation: a modelling study.
Remote Sensing of Environment, 41: 85-103.
de Voogt, K., G. Kite, P. Droogers, and Hammond Murray-Rust. (2000): Modelling water
allocation between wetlands and irrigated agriculture: Case study of the Gediz basin,
Turkey. Colombo, Sri Lanka: International Water Management Institute.
Food and Agriculture Organization of the United Nations (FAO) (1995). Forest Resources
Assessment 1990. Global Synthesis. FAO Forestry Paper 124, Rome.
Houghton, R.A. (1994). The worldwide extent of land use change. Bioscince, 44: 305-313.
Howard, C.W. (1992): Biodiversity issues in African wetlands. In: Bennun, L. A, Aman, R.A,
Crafter, S.A. (eds.) Conservation of Biodiversity in Africa: Local Initiatives and
Institutional Roles, Proceedings, National Museums of Kenya 30th August-3rd September
1992. Centre for Biodiversity, National Museums of Kenya. Pp. 85.
Justice, C.O., Townshend, J.R., Holben, B.N. & Tucker, C.J. (1985). Analysis of the phenology
of global vegetation using meteorological satellite data. International Journal of Remote
Sensing, 6: 1271-1318.
Kumar, M. & Monheit, J.L. (1981). Remote sensing of plant growth. In: Smith, S. (Ed.), Plants
and Daylight Spectrum. Academic Press, New York, pp. 133-144.
Malingreau, J.P. (1986). Global vegetation dynamics of satellite observations over Asia.
International Journal of Remote Sensing, 7: 1121-1146.
Meyer, W.B. & Turner, B.L. (1994). Changes in Land Use and Land-cover: A G;lobal
Perspective. Cambridge University Press, Cambridge, UK.
Myneni, R.B., Maggion, S., Laquinta, J., Privette, J.L., Cabron, N., Pinty, B., Kimes, D.,
Verstraete, M. & Williams, D. (1995). Optical remote sensing of vegetation modelling
caveats, and algorithous. Remote Sensing of Environment, 51: 169-188.
Ojima, D.S., Galvin, K.A. & Turner, B.I. (1994). The global impact of land use change.
Bioscince, 44: 300-304.
Running, S.W., Loveland, T.R. & Pierce, I.L. (1994). A vegetation classification logic based on
remote sensing for use in global biogeochemical models. Ambio, 23: 77-