Detection of land cover changes around Lake ...

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Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques a

b

T Dube , W Gumindoga & M Chawira

c

a

Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa b

Department of Civil Engineering, University of Zimbabwe, Harare, Zimbabwe

c

Zimbabwe Environmental Law Association, Harare, Zimbabwe Published online: 13 Feb 2014.

Click for updates To cite this article: T Dube, W Gumindoga & M Chawira (2014) Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques, African Journal of Aquatic Science, 39:1, 89-95, DOI: 10.2989/16085914.2013.870068 To link to this article: http://dx.doi.org/10.2989/16085914.2013.870068

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AFRICAN JOURNAL OF AQUATIC SCIENCE ISSN 1608-5914 EISSN 1727-9364 http://dx.doi.org/10.2989/16085914.2013.870068

Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques T Dube1*, W Gumindoga2 and M Chawira3 Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa 2 Department of Civil Engineering, University of Zimbabwe, Harare, Zimbabwe 3 Zimbabwe Environmental Law Association, Harare, Zimbabwe * Corresponding author, e-mail: [email protected]

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Land cover changes around Lake Mutirikwi in 1984–2011 were mapped from Landsat images using traditional image classification methods including the maximum likelihood classifier algorithm. The possibility of mapping the coverage and abundance of surface floating aquatic weeds was also tested. Landsat images from 1984, 1995, 2001 and 2011 were used to compute a normalised difference vegetation index (NDVI), which was then used as a proxy for indicating areas infested by surface floating aquatic weeds. Forest and shrubs covered 310.8 km2 in 1984, but had deteriorated by 24.87% to 77.3 km2 in 2011, while the area under cultivation increased by 51.44% between 1984 and 2011. Runoff from surrounding farms could be responsible for washing soil nutrients into Lake Mutirikwi, enriching its water. A large aggregation of surface floating aquatic weeds concentrated upstream along tributaries of Lake Mutirikwi, mainly the Mucheke which received sewage from Masvingo town, with less coverage in the central parts of the lake. Vegetation indices such as NDVI proved successful as a proxy for mapping the coverage of surface floating aquatic weeds in Lake Mutirikwi in space and time. Keywords: eutrophication, land cover classification, Landsat, NDVI, surface floating aquatic weeds

Introduction Increasing population pressures and land development pose ongoing problems to the management of the environment (Satterthwaite 1997, Hardoy and Mitlin 2001, Achankeng 2003). This phenomenon is evident especially in Zimbabwe where, between 2002 and 2012, water quality severely deteriorated in most inland water bodies due to pollution and consequent eutrophication. Furthermore, a steady accumulation of nutrients in many catchments has resulted in extensive growths of surface floating aquatic weeds in freshwater and floodplain habitats, causing a decrease in biodiversity, threatening critical habitats, altering nutrient cycles and degrading water quality (Chikwenhere 2001, Ustin 2008, Nhapi 2009). The major lakes in Zimbabwe such as Chivero, Kariba, Manyame and Mutirikwi have suffered particularly from eutrophication issues (Shekede et al. 2008, Chawira et al. 2013) encompassing both point and non-point pollutant sources. Examples of point-source pollution include raw sewage from illegal industrial and domestic waste disposal, as well as infrequent garbage collection by authorities (Achankeng 2003, Khan 2003, Nhapi et al. 2003). The non-point sources are the result of agriculture and mining. This problem has received limited attention from the responsible authorities, due to either the limited resources available such as lack of funding or the lack of technical expertise. Lake Mutirikwi, formerly Lake Kyle, one of the largest inland freshwater bodies in Zimbabwe, is under threat from both point and non-point pollution due to

raw sewage that constantly spills into its major tributary, the Mushagashe River (Mapira 2011). The continuous disposal of raw sewage has enhanced the growth of surface floating aquatic weeds such as water hyacinth Eichhornia crassipes. Pieterse (1990) defines surface floating aquatic weeds as ‘aquatic plants, or group of plants, not desired by the manager/s of the water body where it occurs, either when growing in abundance or when interfering with the growth of crop plants or ornamentals.’ Increased abundance of surface floating aquatic weeds has a number of undesirable impacts on lakes, including a reduction in the area of open water and water-column habitat for other aquatic organisms (Shekede et al. 2008), reduction in shoreline access to lakes for fishing and recreation, altered invertebrate and fish habitats, a lack of subsurface photosynthetic active radiation (PAR) and even hypoxia (Malone and Kemp 1986, Duarte 1995, Gulati and van Donk 2002). Pilot research projects conducted in Lake Mutirikwi have found that the lake’s water, the main source of domestic and industrial water for the city of Masvingo and which also contributes 65% of irrigation water for the sugarcane industry in the south-eastern lowveld, may become unusable if the problem continues unabated (Marshall 2005, Mapira 2011). Previous studies on water quality in Lake Mutirikwi have been based largely on conventional methods such as pointbased monitoring techniques (Mapira 2011). However, pointbased methods alone are not enough to provide spatial

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Dube, Gumindoga and Chawira


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coverage of lake-wide problems such as surface floating aquatic weed infestations (Shekede et al. 2008). It is against this background that remote sensing techniques have been adopted here for monitoring and mapping land cover changes and levels of invasion by water surface floating aquatic weeds in Lake Mutirikwi in both time and space. In this study a normalised difference vegetation index (NDVI) was utilised as proxy to detect and map surface floating aquatic weeds in Lake Mutirikwi. It has been established that NDVI is highly correlated with greenleaf biomass (Mutanga and Skidmore 2004, Shekede et al. 2008). This relationship has been used to estimate photosynthetically active radiation of plant canopies (Baret and Guyot 1991, Sellers et al. 1992), percent canopy cover, chlorophyll content (Broge and Leblanc 2000, Jayaraman and Srivastava 2002, Kromkamp and Morris 2006) and leaf area index (LAI) (Asrar et al. 1984). The empirical relationship between the NDVI and fieldmeasured vegetation cover is widely understood (Franklin 1991, Davies and Barbosa 2008) and has been extensively used in vegetation mapping (Thakur et al. 2012, Wood and Pidgeon 2012). The advantage of using NDVI as a proxy for water weeds is that it can be calculated over large spatial extents, thus giving a broad understanding of the rate of invasion and changes to weed distribution patterns. Remote sensing imaging techniques thus provide the potential to monitor ‘invasive species’ distribution and spread, enabling an assessment of areas of severe infestation and facilitating timely interventions (Shekede et al. 2008). The objective of this study was to map catchment land cover changes surrounding Lake Mutirikwi and to assess the feasibility of measuring abundance and spatial coverage of surface floating aquatic weeds over time, using freely available multispectral Landsat images. Methods Study area Lake Mutirikwi (Figure 1), constructed in 1960 on the Mutirikwi River in Masvingo Province, south-eastern Zimbabwe, at latitude 20°13′ S and longitude 30°00′ E, has a capacity of 1.378 million cubic metres and a surface area of 9 105 ha with a 398 900 ha catchment area. The lake lies at an altitude between 1 000 and 1 040 m above sea level on the Central African Plateau. The dam was built to provide irrigation water to sugarcane plantations to the south-west, around the town of Triangle. The lake is flanked by Lake Kyle Recreational Park on its northern shore and by a small park on the southern shore. The lake is supplied by eight major rivers, the Mucheke, Mbebvi, Popoteke, Matare, Umponyami, Makurumidzi, Mushagashe and Mutirikwi. Water quality in the lake has deteriorated due to continued illegal disposal of raw sewage from Masvingo’s Rujeko Pump Station and to industrial effluent from the Masvingo industrial area. This has resulted in an accumulation of nutrients leading to increased growth of surface floating aquatic weeds, mostly water hyacinth (Malone and Kemp 1986). Remote sensing data acquisition and pre-processing Cloud-free remote sensing Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) imagery with

a spatial resolution of 30 m were used (Table 1). Images for September 1984, 1995, 2001 and 2011, these being four contrasting years, were acquired so as to ensure consistency in mapping the distribution of water hyacinth in the lake. Landsat images were acquired from the online Landsat archive via GloVis web-link (http://glovis.usgs.gov/). Landsat images acquired in digital number (DN) format were calibrated to spectral radiance units (W m–2 sr –1 μm–1). The algorithm developed by Chander et al. (2009) specifically for calibrating Landsat images (Equation 1) and the calibration coefficients were provided together with the respective Landsat image files as metadata files: LO

§ LMAX O LMINO ¨ © Q cal max Q cal min

· ¸ (Q cal Q cal min) LMINO ¹

(1)

where L   spectral radiance at the sensor’s aperture (W m–2 sr–1 μm–1), Qcal  quantised calibrated pixel value (DN), Qcalmin  minimum quantised calibrated pixel value corresponding to LMIN (DN), Qcalmax  maximum quantised calibrated pixel value corresponding to LMAX  (DN), LMIN  spectral at-sensor radiance scaled to Qcalmin (W m–2 sr–1 μm–1), and LMAX  spectral at-sensor radiance scaled to Qcalmax (W m–2 sr –1 μm–1). The conversion from DN to spectral radiance was done by implementing the Chander et al. (2009) algorithm using the ENvironment for Visualizing Images (ENVI) software. For this study, only Landsat bands 5, 4 and 3 were used for image classification and NDVI calculation. The three bands were atmospherically corrected using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model (Kaufmann and Wald 1997) which is applicable only to the 0.35–2.5 μm visible region of the electromagnetic spectrum. The FLAASH model, an interface of the ENVI GIS software, is recommended for retrieving reflectance from multispectral radiance images (Kaufmann and Wald 1997). FLAASH incorporates the MODTRAN4 radiation transfer code (Berk et al. 2000) which involves the application of a correlated-k algorithm that enables precise computation of the scattering components. During the process, different parameters were set. These included viewing geometry obtained from the Landsat metadata file modelling parameters. The aerosol retrieval parameter was set to 2-Band (K-T), using 550 nm as the initial visibility. This was done because FLAASH uses the Initial Visibility (IV) value if the aerosol could not be retrieved (ENVI 2009). Land use and land cover classification from Landsat images Before undertaking image classification, Landsat images of 1984 and 2011 were imported in GeoTiff format into the Integrated Land and Water Information System (ILWIS version 3.7.1) interface via the geo-gateway, an in-built ILWIS function to ensure compatibility. Consequently, a maplist was created in ILWIS interface using three selected Landsat TM bands, 5, 4 and 3, which were assigned to red, green and blue colours in ILWIS, respectively. The bands were then opened as pseudo-natural colour composites so as to enhance visual interpretation of features. Band 3 represented visible electromagnetic radiation with wavelengths of 0.63–0.69 μm, band 4 represented near infrared with wavelengths of 0.76–0.90 μm. As a prerequisite

African Journal of Aquatic Science 2014, 39(1): 89–95

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ezi River MOZAMBIQUE mb Za 18° S

ti Rive r ya

Lake Chivero

Harare

ZIMBABWE Masvingo

20° S

Bulawayo

BOTSWANA 28° E

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6 20°06ƍ S Glenlivet

Point B 2

KEY

Sh a ng a ni Riv

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Livingstone

Hwange

20°10ƍ S

an

5

Point A

S

ZAMBIA

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1

Kariba

a

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rib Ka

he agas Mush

50 100 150 km

e ek ch Mu

0

AFRICA

Mu tirik wi

Masvingo

32° E

NDVI sampled points Area infested with surface floating weeds Settlement

5

Lake Mutirikwi 3 20°14ƍ S

Lake Mutirikwi Town

4

20° S

Lake Mutirikwi

Mutirikwi

Morgenster 32° E

30°52ƍ E

30°56ƍ E

31° E

31°04ƍ E

Figure 1: Map of Lake Mutirikwi showing locations of six points selected for NDVI analysis. Enlarged NDVI images of the area including Points A and B is shown in Figure 4

Table 1: Acquisition dates of selected Landsat images used for the detection of land cover changes, sourced from the online Landsat data archive web-link http://glovis.usgs.gov/ Image acquisition date 4 September 1984 18 September 1995 10 September 2001 14 September 2011

Landsat version TM TM ETM TM

for supervised classification in ILWIS, a sample set was developed with six classes. The sample set was created manually from Landsat TM colour composite images. The six classes were chosen based on background knowledge of the area under study, including settlements, forest and shrub, marsh, water, bare land and cultivated areas. The abundance of vegetation within the lake and river boundaries was an indication of the status of the surface floating aquatic weeds. Supervised classification was then used to map the images on a GIS framework based on the six classes generated using the sample set to demonstrate the patterns in land cover and land use change around Lake Mutirikwi. Classification was done using the maximum likelihood classifier algorithm. A maximum likelihood classifier algorithm was implemented in ILWIS during image classification. The maximum likelihood classification algorithm assumes that spectral values of training pixels are statistically distributed according to a multivariate normal (Gaussian) probability density function. Consequently, classification results were then assessed for accuracy using the 2012 Google Earth image of the study area combined with fieldbased ground control-points determined in the study area using a hand-held Garmin GPS (accuracy ±3 m). Google

Earth image domain is based on the 2.5 m SPOT panchromatic band (Guo et al. 2010). Validation with Google Earth and field control-points ensured that classes were properly assigned to the respective land cover feature on the ground. Mapping surface floating aquatic weeds using the normalised difference vegetation index Normalised difference vegetation index (NDVI) is a numerical indicator often used as a proxy for estimating plant biomass from remotely sensed data (Tucker 1979, Kromkamp and Morris 2006, Rulinda et al. 2010). In analysing remote sensing data the index uses the visible red band (VIS) (0.4–0.7 μm) and near-infrared (NIR) bands (0.75–1.1 μm) of the electromagnetic spectrum (Tucker 1979, Rulinda et al. 2010) as indicated in Equation 2. When the difference between the NIR and the red reflectance is large the concentration of surface floating aquatic weeds is very high and vice versa. The algorithm assumes a NDVI value range of −1 through 0 to 1, where negative values symbolise water, zero symbolises bare soil and positive values symbolise healthy vegetation. The algorithm was selected for mapping surface floating aquatic weeds in Lake Mutirikwi because of the inherent advantages, e.g. it has low sensitivity to soil differences, it is a function of a ratio, therefore, it is less sensitive to solar elevation, and it is very sensitive to the amount of green vegetation (plant vigour): NDVI

NIRband 4 R band 3 NIRband 4 Rband 3

(2)

where NIRband4 approximates the Chl a maximum reflectance in the near-infrared wavelength and Rband3 equates to the maximum absorption in the red band of Landsat. Figure 1 shows the area used for the detailed NDVI analysis of floating aquatic weed cover.



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To assess the occurrence of the surface floating aquatic weeds the lake boundary layer was overlaid on NDVI maps to delineate the lake area. Following this process, six points were selected (Figure 1) using a stratified random sampling technique within the lake and its feeder tributaries. This sampling technique helps to select points that reflect the different water quality state of the lake water. From the generated points we extracted NDVI values from each location and these were further compared over the years 1984, 1995, 2001 and 2011 to derive the patterns in spatial coverage and abundance of surface floating aquatic weeds.

the tributaries of the lake, in this case the Mucheke and Mushagashe rivers, was due to surface floating aquatic weeds. Figure 4 further demonstrates the inter-decadal variability in NDVI coverage in Lake Mutirikwi between 1984 and 2011. Between 1984 and 1995 the concentration of surface floating aquatic weeds in the Mucheke and Mushagashe rivers increased considerably to cover the whole channel water surface. However, in 2011 there was more open water at Point B (Figure 4), indicating the dynamic nature of weed growth and distribution.

Results

The spatio-temporal variation of NDVI at a pixel level The NDVI values for the six sampled points are shown in Table 3. Point 1, upstream of the confluence of the Mucheke and Mushagashe rivers, shows a higher NDVI than other selected locations. At the same location (Point 1), there was a general increase in NDVI over the years. At Points 2, 3 and 4, negative NDVI values were observed, indicating the absence of surface floating aquatic weeds at those times. However, Point 5 showed positive NDVI values for 1984, 1995 and 2011, highlighting the presence of surface floating aquatic weeds in the eastern region of the lake. For Point 6,

Image classification results Figure 2 illustrates land use and land cover changes in and around Lake Mutirikwi between 1984 and 2011. The area covered by natural vegetation was very high, approximately 310.8 km2, in 1984 (i.e. 31.07% of the mapped area), reducing to 77.3 km2 in 2011 as the natural vegetation was replaced by cultivation, which occupied 54.4% of the land area in 2011. The area covered by water increased by 9.23% between 1984 and 2011. Changes in land cover type between 1984 and 2011 around Lake Mutirikwi are shown in Table 2. Cultivation increased by 51.4% between 1984 and 2011 and, notably, grasslands decreased by 7.3% over the same period.

Table 2: Percentage land cover change in and around Lake Mutirikwi between 1984 and 2011

The spatial variation of NDVI in Lake Mutirikwi Figure 3a–d shows NDVI values computed for 1984, 1995, 2001 and 2011, respectively, for the six selected points. The lake aerial extent varied over the 27-year period of study but with an overall increase from 1995 to 2002. The distribution of the areas covered by surface floating aquatic weeds is better visualised in the expanded images of the upper section of the lake (Figure 4). The high NDVI along

September 1984 Landsat imagery

Bare land Cultivation Forests and shrub Grassland Settlement Water

Land cover class Bare land Cultivation Forest and shrub Grassland Marsh Settlement Water

Land cover change, 1984–2011 (%) −40.51 51.44 −20.08 −7.31 15.63 −6.44 5.31

September 2011 Landsat imagery

N E

W S

0

5

10 km

Figure 2: Land use and land cover change classification maps for 1984 and 2011 in and around Lake Mutirikwi, derived from bands 3, 4 and 5 of Landsat imagery



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(a) 1984

(b) 1995

(c) 2001

(d) 2011

N

NDVI í0.31

í0.18

0.07

í0.06

0.19

0.32

0

10 km

E

W S

Figure 3: Spatial variation of NDVI in and around Lake Mutirikwi in (a) 1984, (b) 1995, (c) 2001 and (d) 2011, derived from bands 3 and 4 of selected Landsat imagery

Lake area with less water weeds

Water

Point B

Moderate NDVI

Lake area with water weeds

Dense, healthy plants

Point B

Point A



Point B

Point A Lake area infested with water weeds

1984

Point A

Lake area infested with water weeds

1995 NDVI í0.31

0.20

0.33

2011

Figure 4: Enlarged NDVI maps of the upper weed-infested section of Lake Mutirikwi in 1984, 1995 and 2011, indicating areas covered with surface floating aquatic weeds


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Table 3: Pixel-based spatio-temporal variation of NDVI values obtained from bands 3 and 4 of Landsat images for 1984, 1995, 2001 and 2011. Positive (shaded) values = presence of surface floating aquatic weeds, negative values = presence of water Year NDVI 1984 NDVI 1995 NDVI 2001 NDVI 2011

Point 1 0.03 0.11 0.16 0.27

Point 2 −0.33 −0.07 −0.44 −0.17

Point 3 −0.30 −0.21 −0.42 −0.19


positive NDVI values, and hence weed presence, were recorded only in 1995, with the other years showing negative values. Discussion Traditional image classification for land use change assessment The decrease in forested area observed can be linked to the increase in population in Zimbabwe in the past decades, resulting in massive fragmentation of virgin forested lands. For example, between 2001 and 2011 large areas which were previously forests were converted into settlements and croplands. Kaliraj and Muthu (2012) pointed out that the growth of human and cattle populations, and widespread rural and urban poverty has resulted in a loss of forest cover throughout the world, with consequent ecological problems such as biodiversity loss and soil erosion (Roy and Joshi 2002). The increase in the area covered by water between 1984 and 1995, as well as between 1995 and 2011, can be linked to the distribution of rainfall in Zimbabwe around 2000, when the country was affected by successive cyclones that resulted in flooding and the overflow of major rivers, like the Mutirikwi in Masvingo, combined with a rise in the water table. Whilst the average total annual rainfall in Masvingo Province is approximately 622 mm, between 2000 and 2001, rainfall doubled to 1 201 mm (Mudzengi et al. 2013). Moreover, the effects of intensive river-bank cultivation and increased land area harnessed for cultivation may have resulted in the deposition of eroded sediments in the lake, reducing the lake depth and enhancing the lake area (Mapira 2011). Consequently, increased farming activities around Lake Mutirikwi provide a perennial source of nutrients for the lake. Marshall (2005) stated that runoff from Lake Mutirikwi’s surrounding commercial farms washes soil nutrients into the lake, contributing to the enrichment of lake waters and creating favourable conditions for the growth of surface floating aquatic weeds. Using NDVI to explain the coverage of surface floating aquatic weeds in Lake Mutirikwi The variation in lake aerial extent that occurred over the three decades of this study can be attributed to the reduction in depth of the lake due to siltation and subsequent expansion of its surface area. The higher NDVI values along the tributaries of the lake, especially the Mucheke and Mushagashe rivers (Figure 4), illustrate the growth of surface floating aquatic weeds (Mapira 2011) in those rivers that drain Masvingo city and the surrounding farms.

Point 4 −0.25 −0.17 −0.41 −0.16

Point 5 0.11 0.03 −0.42 0.08

Point 6 −0.19 0.04 −0.45 −0.19

While there was a reduction in the area of surface floating aquatic weeds between 1995 and 2011 (Figure 4), the higher NDVI values in 2011 suggest more vigorous plant growth and or higher biomass in 2011. This could be due to the increased dumping of raw sewage in recent years in certain portions of streams, coupled with increasing and unmonitored agricultural activities upstream. Conclusions The spectral vegetation index NDVI was successfully used as a proxy for mapping the coverage and abundance of surface floating aquatic weeds in Lake Mutirikwi. In addition, the index suggested an increase in intensity of the weed infestation over one time period. It was demonstrated that remote sensing can be used for monitoring and mapping surface floating aquatic weeds on open water bodies for water resource management where there are no readily available field-based measurements. Results obtained in this study also contribute to the methods of identifying sources of pollution in Lake Mutirikwi, thus demanding a multi-sectoral approach in finding a permanent solution to continued discharge of raw sewage and industrial effluent into Lake Mutirikwi and its tributaries. Acknowledgements — The authors would like to thank NASA, through GloVis link, for providing Landsat images of the study area, and they also thank the anonymous reviewers.

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Manuscript received 8 March 2013, revised 18 October 2013, accepted 25 November 2013 Associate Editor: C Howard-Williams