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Spatio-temporal variations of aquatic weeds abundance and coverage in Lake Chivero, Zimbabwe
Physics and Chemistry of the Earth 33 (2008) 714–721
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Spatio-temporal variations of aquatic weeds abundance and coverage in Lake Chivero, Zimbabwe M.D. Shekede a, S. Kusangaya a,*, K. Schmidt b a b
Department of Geography and Environmental Science, University of Zimbabwe, P.O. Box MP167, MT Pleasant, Harare, Zimbabwe Scientific Industrial Research and Development Centre (SIRDC), Zimbabwe
a r t i c l e
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Available online 10 July 2008 Keywords: Lake Chivero NDVI Aquatic weeds Spatial extent Abundance
a b s t r a c t Information on the spatial distribution of aquatic weeds is required for understanding the evolution of weed invasion and propagation rates. Such information is also vital for identifying affected areas and relating weed abundance to probable changes in environmental conditions and human actions including management practices within the lake and its catchment. Information on aquatic weed distribution also assists in evaluating the effectiveness of control measures and management actions. In Zimbabwe, Lake Chivero has been characterised by aquatic weed proliferation since the 1970s. Field surveys done between December 2005 and March 2006 showed concentrations of 1.2 mg/l and 0.3 mg/l up from 0.3 mg/l and 0.03 mg/l in 2001 for phosphates and nitrates respectively. Proliferation of aquatic weeds will continue unless nutrient loadings to this lake are reduced. The aim of this paper was to assess the feasibility of mapping the spatial extent and abundance of aquatic weeds in Lake Chivero, Zimbabwe using Landsat images. Landsat images of 1976, 1989 and 2000 were used to calculate the normalised difference vegetation index (NDVI) which was used for estimating the spatial extent of aquatic weeds and weed biomass. Field data and actual biomass measurements were obtained between December 2005 and March 2006 by harvesting weeds from the lake. This was subsequently related to NDVI and used to estimate the abundance of the different weed species. The results indicate that the weed coverage in Lake Chivero declined from 42% in 1976, 36% in 1989 to 22% in 2000. The research also demonstrated that Typha capensis has more biomass, 11.1kg per square metre, than any other weed type and hence higher abundance in all the years. It was concluded that remote sensing is an invaluable asset for detection of invasions, assessment of infestation levels, monitoring rate of spread, and determining the efficacy of weed mitigation measures. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Aquatic vegetation forms an essential component of a lake ecosystem, influencing its physical and chemical processes as well as affecting human activities. It is therefore important to have reliable methods of investigating the past and present status of aquatic vegetation. At a large scale, lakes in the world represent a critical source of water for agriculture, industry, domestic consumption, fisheries and transport. These demands on lake resources are expected to increase rapidly over the 21st century, driven by rapid population growth and economic development. Unfortunately, many lakes and water bodies particularly in developing nations do not have reliable and consistent measurements (spatial as well as volumetric) of aquatic vegetation. Thus, for such lakes, satellite
* Corresponding author. E-mail address: [email protected] (S. Kusangaya). 1474-7065/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2008.06.052
remote sensing provides a means to detect and understand long term changes in aquatic vegetation species composition and abundance. Incidences of growth of aquatic weeds and widespread infestation in the water bodies of the Southern African Development Community (SADC) Region are increasing and constitute a great threat not only to the environment but also the socio economic conditions of the region. Pieterse (1990) defined an aquatic weed as an ‘‘aquatic plant (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 aquatic vegetation, often results in a reduction in water depth and undesirable overgrowth. Overgrowth can be substantially enhanced by human activities such as input of nutrients and organic matter into water bodies from the surrounding catchment areas, disposal of industrial and domestic effluent. Increased nutrient loading results in enhanced nutrient fixation leading to increased macrophytic biomass
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(Jeppesen and Sammalkorp, 2002), excess accumulations of phytoplankton biomass (Malone et al., 1986), episodes of noxious blooms, reductions in aquatic macrophyte communities (Duarte, 1995) and the depletion of dissolved oxygen in bottom waters (Malone et al., 1986) which subsequently leads to fish (and other aquatic animals) death. When occurring in abundance, aquatic vegetation may thus result in a decreased recreational value of a lake by impeding navigation and reducing access to shorelines. Information on the spatial distribution of aquatic weeds is needed in order to understand the evolution of weed invasion, determine affected areas and evaluate the efficiency of control measures and management actions. It is at this level that availability of automated, real time data becomes imperative. These needs are best addressed through the use of remote sensing. The advantages of remote sensing include its ability to capture and record earth surface details instantaneously, provides a synoptic view of a land surface, and the ability to store data efficiently and analyse data effectively using geographic information systems (GIS) (Risser and Treworgy, 1985). Since quantitative ground investigations on the scale of a whole lake are laborious, remote sensing methods are increasingly being used for mapping aquatic vegetation and estimating their distribution and biomass (Zhang, 1998). Remote sensing also offers advantages of multi-spectral data analysis, multi-temporal coverage and cost effectiveness (Soule and Kohm, 1989; Van der Meer et al., 2002). The multi-date nature of satellite imagery permits monitoring of dynamic landscape features and thus provides a means to detect major land cover (in this case aquatic weeds) changes and quantify rates of change (Chudamani et al., 2004). However, to date, for Lake Chivero there has been little research in this area, sentiments also echoed by Gurure (1999) and (Marshall, 2005). The development of techniques based on remote sensing and GIS to identify and map the spatial distribution of aquatic weed provides a practical and more reliable measure of the magnitude of the problem (Schouten et al., 1999; Shaw, 2004). Remote sensing imaging devices with high spectral and spatial resolution provide the potential to monitor ‘invasive species’ distribution and spread, thus enabling an assessment of areas of severe infestation and allow for timeous interventions. While the spectral information of high spectral resolution radiometers is used to differentiate between aquatic weed species, the spatial overview provided by remote sensing imagery is used to monitor the spread in relation to other environmental aspects. The capability of using remote sensing to differentiate weed species through the use of vegetation indices will also help formulating appropriate targeted aquatic weed control measures. It was therefore the thrust of this research to assess the potential of GIS and remote sensing to map the spatial extent and assess the abundance of aquatic weeds in Lake Chivero, Zimbabwe and evaluate the coverage changes that have taken place in this lake between the years 1976, 1989 and 2000.
2. Materials and methods Landsat images were analysed to identify areas occupied by aquatic weeds in Lake Chivero and GIS was used to map and examine the spatial extent of weed coverage. The Landsat images were downloaded from The Global Land Cover Facility (1997–2008) (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp) for the years 1976, 1989 and 2000. The 1999 landsat image could not be used because of excessive cloud cover. So the 1976, 1989 and 2000 images were the only available Landsat images for use in this study. The electromagnetic radiation signals collected by satellites are modified by scattering and absorption by gases and aerosols in the atmosphere. However, for quantitative image analysis, a
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common radiometric response is required from multiple-satellite images acquired on different dates with different sensors (Hall et al., 1991), and atmospheric effects have to be minimised or removed completely. As such in this study, relative atmospheric correction was achieved through normalization using the regression method based on reflectance of pseudo invariant objects. Multiple-date image normalization involves selecting a base image and then transforming the spectral characteristics of all other images obtained on different dates to have the same radiometric scale as the base image. This process aided the reduction of in-between scene variability as a result of potential differences in atmospheric conditions during satellite scene acquisition. Three 1989 bands (that is, Band 4, 3 and 2) were used as a reference bands for normalising the respective bands for 1976 and 2000 images. Normalization techniques depends on the principle that a mathematical model can be used to describe normalization of two images of the same scene, acquired at different times using linear regression, assuming that: (a) the sampled pixel value at time one is linearly related to the pixel of the same location sampled at a different time, and (b) There are a minimum number of pixels in the scene representing features of a surface with invariable reflectance though time. These are called pseudo-invariant objects (PIOs) or Radiometric Normalization Targets (RNTs). PIOs are spatially well defined and spectrally stable though time and are normally selected by visual inspection. These include surfaces such as water bodies and concrete/tarred surfaces as used for this study. This pre-processing of imagery (radiometric correction) thus enables the relation of results from different years.
2.1. Study area Lake Chivero, formerly known as Lake McIlwaine, lies 37 km to the South West of Harare (17°540 ; 30°480 ) and was dammed in 1952 (Chikwenhere and Phiri, 1999). Fig. 1 shows the location of the lake within the Manyame catchment. The lake is the main source of water for domestic and industrial activities in Harare as well as agricultural irrigation within the catchment. The lake also supports recreational activities such as angling, boat fishing and sailing. Lake Chivero is 13.6 km long with an average width of about 2 km and with a catchment area of 2.136 km2. It has a capacity of 250,400,000 m3 and a mean depth of 9.4 m. Approximately 10% of the catchment can be classified as urban and 90% rural, mainly used for agricultural production and communal pasture. The rivers Manyame, Marimba and Mukuvisi are the main tributaries flowing into the lake. The city of Harare has two waste water treatment plants namely Crowbrough which discharge effluent into Marimba River, and Firle disposing effluent into Mukuvisi River. Poorly treated effluent which does not comply with the set environmental standards is sometimes discharged into these two rivers. The town of Chitungwiza located upstream of Lake Chivero also disposes effluent from its waste water treatment plant into Nyatsime River that eventually drains into Lake Chivero. According to Marshall (2005) run-off from surrounding commercial farms washes soil nutrients into the Manyame river system and thus contributes to the enrichment of the waters in Lake Chivero. Lake Chivero is a warm monolithic lake with water removal time of 8.82 years (Mitchell and Marshall, 1974). The lake became eutrophic in early 1960s and this resulted in the appearance of seasonal cynobacteria blooms in the 1960s and the establishment of permanent bloom in 1963 (Munro, 1966). Water samples collected from the lake in 1992 revealed unacceptably high levels of nitrates and phosphates (Moyo, 1997). Nutrient enrichment on the lake led to eutrophication and subsequently the proliferation of Eichhornia crassipes and other macrophytes. Since then aquatic weed proliferation in Lake Chivero has been a concern.
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Fig. 1. Location of Lake Chivero within the Manyame Upper Catchment.
2.2. Spatial distribution of aquatic weeds The area of Lake Chivero covered by aquatic weeds was determined from classification of Landsat satellite images resampled to 30 m spatial resolution. Other remote sensing studies have indicated that aquatic vegetation yields spectrally distinct signals governed by the density of the vegetation, the openness of the canopy and the amounts, forms and orientations of the leaves (Penuelas et al., 1993). Multi-temporal remote sensing data can give valuable information on vegetation changes taking place in any given area. Jensen et al. (1993) for example, detected changes in wetlands and areas of aquatic vegetation with the aid of remote sensing data. Biomass assessment is another successful area of application for remote sensing data (see Armstrong, 1993; and Zhang, 1998). For this study the supervised classification technique was used and training sites were selected based on the familiarity with the geographical area and knowledge of the actual surface cover types present, a feat which was achieved by the use of the aerial photographs and field surveys. The field data on aquatic vegetation, collected between December 2005 and March 2006 consisted of information on aquatic weed species, coverage and biomass. The point intercept method was used to acquire weed samples from Lake Chivero and measure their biomass in the field. The objective of point intercept is to make measurements at regularly spaced, pre-selected or defined locations and to avoid subjectively selecting locations in the field (Madsen, 1999). The points were generated at 200 m interval. A global positioning system (GPS) receiver was used to locate the points on the Lake. A polyvinyl chloride (PVC) pipe measuring 30 cm by 30 cm (could float on water) was used to establish small quadrants from which the weed samples were taken. The small size of the quadrant was meant to facilitate easy transportation
of the weeds to the measuring points. However, since some of the aquatic weeds were floating, they could not be established from some points, thus alternative points near the selected points were chosen in such cases. Wet weight of aquatic weeds was measured using a mass scale. One-way analysis of variance (ANOVA) was used to test whether biomass of the six aquatic weed species was significantly different. 2.3. Abundance estimation using biomass Vegetation indices (VIs) have been widely used to estimate biomass (see for example Haboudane et al., 2002, 2004; Zarco-Tejada et al., 2004, 2005). Vegetation Indices are combinations of reflectance at two or more wavelengths from the red and near infra red (NIR) region of the electro magnetic spectrum (EMS). It has been established that VIs are highly correlated with green-leaf biomass (Sellers, 1985; Mutanga, 2004). This physiological relationship has been used to estimate for example, photosynthetically active radiation (PAR) of plant canopies (Baret and Guyot, 1991; Sellers et al., 1992), percent canopy cover (Haboudane et al., 2004), chlorophyll content (Jayaraman and Srivastava, 2002; Broge and Leblanc, 2000) and leaf area index (LAI) (Asrar et al., 1984). The most widely used vegetation index is the ‘normalized difference vegetation index’ (NDVI). To estimate biomass from imagery, NDVI was therefore used because it is an established index for estimating vegetation quantity and can be used as an indicator of relative biomass (vegetation amount) and greenness. NDVI has also been found to correlate better with yield (biomass) than other vegetation indices and thus continues to be used as biomass indicator using remotely sensed image data (Singh et al., 2002). The index is calculated from the reflected solar radiation in the near-infrared (NIR) and red (R) wavelength bands (Eq. (1))
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NDVI ¼
NIR R NIR þ R
ð1Þ
where NIR is the Landsat TM band 4 and R is the Landsat TM band 3. The NDVI is a nonlinear function which varies between 1 and +1. The higher the positive values the higher the plant biomass such that the absence of green leaves gives a value close to zero and full vegetation cover gives values close to +1. Negative values result from non-vegetated features such as water and clouds. NDVI varies with absorption of red light by plant chlorophyll and the reflection of infrared radiation by water-filled leaf cells. Typical NDVI values for vegetation range between 0.1 and 0.7. NDVI was calculated for the respective images to ascertain changes in the quantity of aquatic weeds. Box plots based on histograms of the respective images were then used to show whether there were variations in the amount of biomass while a t-test was used to determine whether there were any significant changes (differences) in biomass as inferred from NDVI calculations. 3. Results and discussion 3.1. Area covered by aquatic weeds Fig. 2 shows the coverage of aquatic weed estimated from Landsat images for 1976, 1989 and 2000. Aquatic weeds covered almost half of the Lake in 1976 (42%), and declined in the subsequent years (36% in 1989 and 22% in 2000). In 1976, most of the weeds were concentrated near the spillway (Tiger Bay, circled) (Marshal, 2006) with few strands being found along the rest of the shorelines. The northeast winds tend to drive the floating weeds towards the southern shore especially in the Tiger Bay. The suffocation of Manyame River (the area shown by the rectangle) through weed clogging is clearly discernible in the 1989 and 2000 images. This was most likely water hyacinth, Eichhornia crassipes since it was the dominant weed during the 1970s (Magadza, 1997). E. crassipes is an invasive aquatic macrophyte associated with major negative economic and ecological impacts to Lake Chivero ecosystem since the plant’s establishment in the
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1970s. The clogging of Manyame River seems to have continued into the 1980s. In the 2000 image the extent of aquatic weeds in the lake had declined as compared to the previous two years. However, there was a notable increase of aquatic weeds in the years 1989 and 2000 at the ‘entrance’ of Manyame River to Lake Chivero. The concentration of aquatic weeds at the ‘entry point’ of Manyame River was attributable to significant amounts of nutrients coming from its drainage area. Field work in 2005/2006 for this study also included collection of water samples for laboratory analysis. It was found out that high amounts of nitrates (1.2 mg/l) and phosphates (2.81 mg/l) were observed from samples taken at the point where Manyame River enters Lake Chivero. This was a higher concentration of nutrients than was measured in the lake (0.3 mg/l nitrates and 1.2 mg/l phosphates). The results from the remote sensing images show that the aquatic weed coverage in the lake had declined significantly over the past three decades. In 1976, the area under weed occupancy was 967 ha that translate to 42% of the total Lake area. This declined to 863 ha (36%) and 606 ha (22%) in 1989 and 2000 respectively. Chemical spraying and mechanical weed control methods used during the 1960s through the 1970s coupled with reduced nutrient enrichment might have contributed to the decline (Chikwenhere and Phiri, 1999). Furthermore, weed management efforts particularly the introduction of biological control in 1990 using water-hyacinth weevils, N. eichhorniae and N. bruchi diminished most of the water hyacinth that had manifested itself in Lake Chivero (Chikwenhere and Phiri, 1999). 3.2. Aquatic weed abundance in Lake Chivero Aquatic weed biomass samples collected in the field in 2005/ 2006 showed variations in the amount of biomass per unit area. The results of the analysis of variance (ANOVA) test indicate that there is a significant (p < 0.05) difference between the biomass of all the six weed species identified from field surveys. Fig. 3 indicates the variation in biomass per square metre of the six weeds.
Fig. 2. Area covered by aquatic weeds in 1976, 1989 and 2000 The rectangle shows where the Manyame River enters Lake Chivero and the circle shows area near the spill way (Tiger Bay).
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14 Mean
±SE
±SD
12
Biomass [kg/m2]
10
8
6
4
2
0 E.crassipes P.strattiotes T.capensis H.ranunculoides P.australis P.senegalensis Weed Name Fig. 3. Mean Biomass of Phragmites australis, Pistia stratiotes, Hydrocotyle ranunculoides. Typha capensis, Eichhornia crassipes and Persicaria senegalensis.
Typha capensis although found in limited localities, has more biomass per square metre (12 kg/m2) than any other weed type. Phragmites australis , Hydrocotyle ranunculoides and E. crassipes have relatively high biomass per square metre (over 8 kg/m2) while Persicaria senegalensis and Pistia stratiotes have little biomass (less than 6 kg/m2). The morphology of these aquatic weeds explains why such weeds have a higher biomass per unit area. P. australis and T. capensis are rooted plants and therefore can sustain a higher upward canopy. On the other hand P. stratiotes seems to grow in small blobs and from sampled locations was being outcompeted by E. crassipes. It can be concluded therefore that aquatic weed biomass is a function of not only the morphology of the plant but also the ability to effectively withstand competition for nutrients, floating space and light as well as the ability to resist the diminutive effects of the water currents. Weeds such as E. crassipes tend to out-compete and suppress the growth of P. stratiotes, only a few P. stratiotes plants could be observed enmeshed underneath E. crassipes. 3.3. Aquatic weed biomass variations An aerial estimation of weed biomass over the three decades was derived from the images by calculating NDVI which was related to actual biomass. Higher NDVI values corresponded to higher biomass. This relationship can indicate the difference in abundance of green aquatic weed in different locations. However, the biomass variations are not only dependent on the abundance of aquatic weed, but also on the aquatic weed species. NDVI as an estimate of weed abundance in Lake Chivero fluctuated over the years with a notable increase of aquatic weed biomass in 1989 and a decline in 2000 (Fig. 4). In order to test if the difference in NDVI over the three years (1976, 1989 and 2000) was significant, the NDVI data was first tested for normality. The test revealed that the data did not follow a normal distribution and as such the non-parametric Mann Whitney-U-test was performed on paired NDVI years as shown in Table 1. The null hypothesis of no significant difference in NDVI between the years was tested. The results indicated that there are significant differences in the variance of NDVI values across the lake between the three years
Fig. 4. NDVI changes over the last three decades. N is the number of pixels contributing to the total NDVI values for the respective years.
Table 1 Results of the Mann Whitney-U -test on NDVI Years
Z value
P value
Mean NDVI (biomass)
1976 and 1989 1989 and 2000 1976 and 2000
53.376 53.856 11.028
0.00 0.00 0.00
1976 = 0.348 1989 = 0.51 2000 = 0.3
(1976, 1989 and 2000). There was a statistically significant increase in NDVI (and hence biomass of aquatic weeds) from a mean of 0.348 in 1976 to 0.51 in 1989. Furthermore, there is a significant decline in NDVI for aquatic weed (mean of 0.3) in 2000. This decline is both significant when compared to the years 1998 and 1976. 3.3.1. Aquatic weeds spatial and temporal variation Between 1986 and 1991 an outbreak of aquatic weeds that led to massive fish kills was observed in the Lake, referred to as the third outbreak after the 1971/72 outbreak (Chikwenhere and Phiri, 1999). This third outbreak might explain the increase of NDVI
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values measured in 1989. However, since the total area covered by aquatic weeds was actually smaller than in 1976 the outbreak must have promoted the proliferation of aquatic weed species with higher biomass. Thus the average increase of NDVI values from 1976 to 1989 with the consecutive decrease of area covered by the weed over the same time can be explained by the difference in the canopy structure of the weeds and therefore different weed species distribution. As such, in 1976, some weeds were covering a large area of the lake, but those were the weed species having a thin canopy structure and therefore less biomass. The thin canopy structure of those species allows more transmission of light through the canopy as compared to species with a thick canopy
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structure. Hence, the species with a thin canopy display lower NDVI values when compared to those species with a thick canopy structure. By comparing differences in biomass of the different species with the NDVI histograms, it can be deduced that E. crassipes and T. capensis, which are the high biomass species (Fig. 5), are contributing to the pixels with high NDVI values. The other aquatic weeds with a comparatively low biomass (P. stratiotes and P. senegalensis) constitute the low values of the NDVI on the images. In the same thrust it can also be asserted that P. australis and H. ranunculoides constitute generally less biomass than E. crassipes and T. capensis thus should be contributing to the generally lower NDVI values which could be the (lower) peaks in the
Fig. 5. (A) Year 1976 NDVI coverage and NDVI histogram for Lake Chivero, (B) Year 1989 NDVI coverage and NDVI histogram for Lake Chivero, (C) Year 2000 NDVI coverage and NDVI histogram for Lake Chivero.
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1976, 1989 and 2000. P. senegalensis and P. stratiotes have low biomass and should necessarily contribute to the lower end of the NDVI histograms. These biomass variations are shown in Fig. 5 below. In 1976, high biomass weeds were concentrated around Tiger Bay, the dam wall and in the Manyame River. However, the low biomass aquatic weeds constituted the bulk of the aquatic weeds in the 1976. Thereafter, there was dramatic rise in the abundance of high biomass aquatic weeds in 1989 coupled with an equally striking decline in 2000. Subsequent to the water hyacinth outbreak in 1985 to around 1995, field observations made by Chikwenhere and Phiri (1999) found that there was a decline in E. crassipes and P. stratiotes that led to their succession by H. ranunculoides and Myriophyllum aquaticum (parrot’s feather). This is confirmed by the NDVI measurements (high frequency of pixels at NDVI value of 0.71 in the image (Fig. 5), where the histogram of 1989 has a large area covered with intermediate high NDVI values hypothesised to be the water hyacinth species based on the field measurements of biomass. Whereas the NDVI histogram of the 2000 image had flattened out, i.e. the peak around 0.71 NDVI of the 1989 histogram, it disappeared in the year 2000. However, evidence from the two satellite images of 1989 and 2000 confirmed that there was a decline in aquatic weed abundance in Lake Chivero. Six years later, field observations in 2005/2006 found that E. crassipes is again the most abundant aquatic weed in the lake and covers the largest area (Fig. 6). The reversal in abundance of these weeds can be explained by the fact that water hyacinth tends to out-compete all other weeds in the absence of its natural enemies (weevils) or other control measures. Weed control measures, if successful have the effect of reducing the amount of aquatic weeds in the Lake. Efforts to control aquatic weeds in Lake Chivero stretches back to 1995 when the initial outbreak of aquatic weeds was recognised as a potential problem (Chikwenhere and Phiri, 1999). Machinery to remove aquatic weed in the form of tractors, rakes and nets have been employed to rid Lake Chivero of its weeds since the first water hyacinth infestations in 1971. N. eichhorniae and N. Bruchi weevils were introduced in 1990 to initiate the first biological control in Lake Chivero (Chikwenhere and Phiri, 1999). Concurrent with the above mentioned control methods, the weeds were not only manually removed, but also spayed with 2–4D chemical and glyphosphate. Results from this study corroborate with those of Chikwenhere (2001) who, although monitoring E. crassipes only, observed that had been a general decrease in E. crassipes infestation in Lake
Fig. 6. Water Hyacinth in Lake Chivero (2005).
Chivero since 1991. This research established that the total area covered with aquatic weed declined since 1976 from 42% to 36% in 1989 and 22% in 2000. Detailed analysis of the biomass distribution of the aquatic weeds in the lake, deduced from the spectral characteristics, also showed an apparent reduction in the area of weed species of intermediate high biomass. The decline in E. crassipes is attributable to two main factors which are flooding of the Lake, and biological control. The flooding that took place in 1996/97 is said to have flushed the aquatic weed over the dam wall of the Lake. Evidence of this is also visible in the satellite images where the 1998 image shows little aquatic weed in the lake Manyame which is fed by the water from Lake Chivero, whereas the 2000 image shows increased presence of aquatic weed in lake Manyame. In addition to the flooding of the dam wall congruent biological control reduced the vigour and flower production of water hyacinth. 4. Conclusions Remote sensing and GIS techniques provided a means of obtaining useful information on spatial and temporal changes in aquatic vegetation in Lake Chivero. The results show the applicability of these tools for detecting changes in emergent and floating leaved aquatic vegetation. The use of historical images enables the quantification of long-term changes of aquatic vegetation even in cases where no field data prior to a lake management project exist. The ability to relate NDVI to biomass using satellite imagery offers further prospects of quantifying the abundance of different aquatic weed species in the lake and hence opens up opportunities for applying targeted management efforts on areas of interest. Overall, relating NDVI histograms, area covered by weeds and aquatic weed abundance helps in comprehending the aquatic weed dynamics within the lake and can be applied to any other aquatic system. In addition, GIS and remote sensing can be invaluable assets for detection of invasions (like the entrance of Manyame River into Lake Chivero in 1989 and 2000 images), assessment of infestation levels, monitoring rate of spread, and determining the efficiency of mitigation efforts for weed management as shown by this research. References Armstrong, R.A., 1993. Remote sensing of submerged vegetation canopies for biomass estimation. International Journal of Remote Sensing 14 (3), 621–627. Asrar, G., Fuchs, M., Kanemasu, E.T., Yoshida, M., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal 76, 300–306. Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173. Broge, N.H., Leblanc, E., 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172. Chikwenhere, G.P., 2001. Biological and Integrated Control of Water Hyacinth, Eichhornia Crassipes. Proceedings of the Second Meeting of the Global Working Group for the Biological and Integrated Control of Water Hyacinth, Beijing, China, 9–12 October 2000. Australian Centre for International Agricultural Research, Canberra. Chikwenhere, G.P., Phiri, G., 1999. History and Control Effort of Water Hyacinth, Eichhornia Crassipes on Lake Chivero in Zimbabwe. In: Hill M.P., Center T.D., Julien M.H. In: Proceedings of the First IOBC GLOBAL Working Group Meeting for the Biological and Integrated Control of Water Hyacinth. November 199891-100 Harare, Zimbabwe. Chudamani, J.B., de Leeuwa, J., van Durenaa, I.C., 2004. Remote Sensing and GIS Applications for Mapping and Spatial Modelling of Invasive Species. Department of Natural Resources, International Institute for Geo-information Science and Earth Observation (ITC). Duarte, C.M., 1995. Submerged aquatic vegetation in relation to different nutrient regimes. Ophelia 41, 87–112. Gurure, R., 1999. Water Hyacinth: Searching for Lasting Solutions to Control This Weed Menace in Zimbabwe. Paper presented at the Workshop on Sustainable Management of the Lakes of Zimbabwe, 24–25 February 1999, Holiday Inn Harare.
M.D. Shekede et al. / Physics and Chemistry of the Earth 33 (2008) 714–721 Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416–426. Haboudane, D., Miller, J.R., Pattey, E., Pablo, J., Zarco-Tejada, P., Strachane, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352. Hall, F.G., Stebel, D.E., Nickenson, J.E., Goetz, S.J., 1991. Radiometric rectification: toward a common radiometric response among multi-date, multi-sensor images. Remote Sensing of Environment. 35, 11–27. Jayaraman, V., Srivastava, S.K., 2002. The invariance of red edge inflexion wavelengths derived from ground based spectro-radiometer and space borne IRS-P3: MOS-B data. International Journal of Remote Sensing 23, 14. Jensen, J.R., Cowen, D.J., Althausen, J.D., Narumalani, S., Weatherbee, O., 1993. An evaluation of the coast watch change detection protocol in South Carolina, photogramm. Engineering of Remote Sensing 59 (6), 1039–1046. Jeppesen, E., Sammalkorp, I., 2002. Lakes. In: Handbook of Restoration Ecology. In: Perrow, M., Davy, T. (Eds.), Restoration in Practice, vol. II. Cambridge University Press, Cambridge, UK, pp. 1–32. Madsen, J., 1999. Point Intercept and Line Intercept Methods for Aquatic Plant Management. Aquatic Plant Control Technical Note M1-02. Magadza, C.H.D., 1997. Water pollution and catchment management. In: Moyo, N.A.G. (Ed.), Lake Chivero a Polluted Lake. University of Zimbabwe Publications, Harare. Malone, T.C., Kemp, W.M., Ducklow, H.W., Boynton, W.R., Tuttle, J.H., Jonas, R.B., 1986. Lateral variations in the production and fate of phytoplankton in a partially stratified estuary. Marine Ecology Progress Series 32, 149–160. Marshal, B.E., 2006. Personal communication. Marshall, B.E., 2005. The impact of eutrophication on Lake Chivero, Zimbabwe: a tropical African reservoir. In: Reddy, M.V. (Ed.), Restoration and Management of Tropical Eutrophic Lakes 2005. Science Publishers, Inc., UK. Mitchell, D.S., Marshall, B.E., 1974. Hydrobiological observations on three Rhodesian reservoirs. Freshwater Biology 4, 61–72. Moyo, N.A.G. (Ed.), 1997. Lake Chivero: A Polluted Lake. University of Zimbabwe, Harare. Munro, J.L., 1966. A limnological survey of Lake Mcllwaine, Rhodesia. Hydrobiologia 28, 281–308. Mutanga, O., 2004. Hyperspectral remote sensing of tropical grass quality and quantity. PhD Thesis. WU, The Netherlands.
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Penuelas, I., Fiella, C., Serrano, B.L., Save, R., 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote sensing 14, 1887–1905. Pieterse, A.H., 1990. Introduction. In: Pieterse, A.H., Murphy, K. (Eds.), Aquatic Weeds: The Ecology and Management of Nuisance Aquatic Vegetation. Oxford University Press, Oxford, England, pp. 1–16. Risser, P.G., Treworgy, C.G., 1985. Overview of Ecological Research Data Management. – Research Data Management in the Ecological Sciences. University of South Carolina Press, Columbia. Schouten, L.S.M., van Leeuwen, H.J.C., Bakker, J.G.M., Twongo, T., 1999. Water Hyacinth Detection in Lake Victoria by Means of Satellite SAR: Netherlands Remote Sensing Board USP-2 98-28. Sellers, P.J., 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6 (8), 1335–1372. Sellers, P., Hall, F.G., Asrar, G., Strebel, D.E., Murphy, R.E., 1992. An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). Journal of Geophysics Research 97, 18345–18371. Shaw, D.R., 2004. Translation of remote sensing data into weed management decisions. Spring 53 (2), 264–273. Singh, B., Balasubramanian, V., Singh, J., Khind, C.S., Singh, Y., Ladha, J.K., Bronson, K.F., 2002. Chlorophyll meter and leaf colour – chart-based nitrogen management for rice and wheat in north-western India. Agron. J. 94, 821– 829. Soule, M.E., Kohm, K.A., 1989. Research Priorities for Conservation Biology. Island Press, Washington DC. Van der Meer, F., Schmidt, K.S., Bakker, A., Bijker, W., 2002. New environmental RS systems. In: Skidmore, A.K. (Ed.), Environmental Modelling With GIS and RS. Taylor & Francis, London, pp. 26–51. Zarco-Tejada, P.J., Miller, J.R., Morales, A., Berjo´n, A., Agüera, J., 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment 90 (4), 463–476. Zarco-Tejada, P.J., Whiting, M., Ustin, S.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high spatial hyperspectral remote sensing imagery. Agronomy Journal 97 (3), 641–653. Zhang, X., 1998. On the estimation of biomass of submerged vegetation using satellite thematic mapper (TM) imagery: a case study of the Honghu, PR China. International Journal of Remote Sensing 19 (1), 11–20. The Global Land Cover Facility (1997–2008) University of Maryland. (accessed 02.12.05.).
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Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce
Spatio-temporal variations of aquatic weeds abundance and coverage in Lake Chivero, Zimbabwe M.D. Shekede a, S. Kusangaya a,*, K. Schmidt b a b
Department of Geography and Environmental Science, University of Zimbabwe, P.O. Box MP167, MT Pleasant, Harare, Zimbabwe Scientific Industrial Research and Development Centre (SIRDC), Zimbabwe
a r t i c l e
i n f o
Available online 10 July 2008 Keywords: Lake Chivero NDVI Aquatic weeds Spatial extent Abundance
a b s t r a c t Information on the spatial distribution of aquatic weeds is required for understanding the evolution of weed invasion and propagation rates. Such information is also vital for identifying affected areas and relating weed abundance to probable changes in environmental conditions and human actions including management practices within the lake and its catchment. Information on aquatic weed distribution also assists in evaluating the effectiveness of control measures and management actions. In Zimbabwe, Lake Chivero has been characterised by aquatic weed proliferation since the 1970s. Field surveys done between December 2005 and March 2006 showed concentrations of 1.2 mg/l and 0.3 mg/l up from 0.3 mg/l and 0.03 mg/l in 2001 for phosphates and nitrates respectively. Proliferation of aquatic weeds will continue unless nutrient loadings to this lake are reduced. The aim of this paper was to assess the feasibility of mapping the spatial extent and abundance of aquatic weeds in Lake Chivero, Zimbabwe using Landsat images. Landsat images of 1976, 1989 and 2000 were used to calculate the normalised difference vegetation index (NDVI) which was used for estimating the spatial extent of aquatic weeds and weed biomass. Field data and actual biomass measurements were obtained between December 2005 and March 2006 by harvesting weeds from the lake. This was subsequently related to NDVI and used to estimate the abundance of the different weed species. The results indicate that the weed coverage in Lake Chivero declined from 42% in 1976, 36% in 1989 to 22% in 2000. The research also demonstrated that Typha capensis has more biomass, 11.1kg per square metre, than any other weed type and hence higher abundance in all the years. It was concluded that remote sensing is an invaluable asset for detection of invasions, assessment of infestation levels, monitoring rate of spread, and determining the efficacy of weed mitigation measures. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction Aquatic vegetation forms an essential component of a lake ecosystem, influencing its physical and chemical processes as well as affecting human activities. It is therefore important to have reliable methods of investigating the past and present status of aquatic vegetation. At a large scale, lakes in the world represent a critical source of water for agriculture, industry, domestic consumption, fisheries and transport. These demands on lake resources are expected to increase rapidly over the 21st century, driven by rapid population growth and economic development. Unfortunately, many lakes and water bodies particularly in developing nations do not have reliable and consistent measurements (spatial as well as volumetric) of aquatic vegetation. Thus, for such lakes, satellite
* Corresponding author. E-mail address: [email protected] (S. Kusangaya). 1474-7065/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2008.06.052
remote sensing provides a means to detect and understand long term changes in aquatic vegetation species composition and abundance. Incidences of growth of aquatic weeds and widespread infestation in the water bodies of the Southern African Development Community (SADC) Region are increasing and constitute a great threat not only to the environment but also the socio economic conditions of the region. Pieterse (1990) defined an aquatic weed as an ‘‘aquatic plant (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 aquatic vegetation, often results in a reduction in water depth and undesirable overgrowth. Overgrowth can be substantially enhanced by human activities such as input of nutrients and organic matter into water bodies from the surrounding catchment areas, disposal of industrial and domestic effluent. Increased nutrient loading results in enhanced nutrient fixation leading to increased macrophytic biomass
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(Jeppesen and Sammalkorp, 2002), excess accumulations of phytoplankton biomass (Malone et al., 1986), episodes of noxious blooms, reductions in aquatic macrophyte communities (Duarte, 1995) and the depletion of dissolved oxygen in bottom waters (Malone et al., 1986) which subsequently leads to fish (and other aquatic animals) death. When occurring in abundance, aquatic vegetation may thus result in a decreased recreational value of a lake by impeding navigation and reducing access to shorelines. Information on the spatial distribution of aquatic weeds is needed in order to understand the evolution of weed invasion, determine affected areas and evaluate the efficiency of control measures and management actions. It is at this level that availability of automated, real time data becomes imperative. These needs are best addressed through the use of remote sensing. The advantages of remote sensing include its ability to capture and record earth surface details instantaneously, provides a synoptic view of a land surface, and the ability to store data efficiently and analyse data effectively using geographic information systems (GIS) (Risser and Treworgy, 1985). Since quantitative ground investigations on the scale of a whole lake are laborious, remote sensing methods are increasingly being used for mapping aquatic vegetation and estimating their distribution and biomass (Zhang, 1998). Remote sensing also offers advantages of multi-spectral data analysis, multi-temporal coverage and cost effectiveness (Soule and Kohm, 1989; Van der Meer et al., 2002). The multi-date nature of satellite imagery permits monitoring of dynamic landscape features and thus provides a means to detect major land cover (in this case aquatic weeds) changes and quantify rates of change (Chudamani et al., 2004). However, to date, for Lake Chivero there has been little research in this area, sentiments also echoed by Gurure (1999) and (Marshall, 2005). The development of techniques based on remote sensing and GIS to identify and map the spatial distribution of aquatic weed provides a practical and more reliable measure of the magnitude of the problem (Schouten et al., 1999; Shaw, 2004). Remote sensing imaging devices with high spectral and spatial resolution provide the potential to monitor ‘invasive species’ distribution and spread, thus enabling an assessment of areas of severe infestation and allow for timeous interventions. While the spectral information of high spectral resolution radiometers is used to differentiate between aquatic weed species, the spatial overview provided by remote sensing imagery is used to monitor the spread in relation to other environmental aspects. The capability of using remote sensing to differentiate weed species through the use of vegetation indices will also help formulating appropriate targeted aquatic weed control measures. It was therefore the thrust of this research to assess the potential of GIS and remote sensing to map the spatial extent and assess the abundance of aquatic weeds in Lake Chivero, Zimbabwe and evaluate the coverage changes that have taken place in this lake between the years 1976, 1989 and 2000.
2. Materials and methods Landsat images were analysed to identify areas occupied by aquatic weeds in Lake Chivero and GIS was used to map and examine the spatial extent of weed coverage. The Landsat images were downloaded from The Global Land Cover Facility (1997–2008) (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp) for the years 1976, 1989 and 2000. The 1999 landsat image could not be used because of excessive cloud cover. So the 1976, 1989 and 2000 images were the only available Landsat images for use in this study. The electromagnetic radiation signals collected by satellites are modified by scattering and absorption by gases and aerosols in the atmosphere. However, for quantitative image analysis, a
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common radiometric response is required from multiple-satellite images acquired on different dates with different sensors (Hall et al., 1991), and atmospheric effects have to be minimised or removed completely. As such in this study, relative atmospheric correction was achieved through normalization using the regression method based on reflectance of pseudo invariant objects. Multiple-date image normalization involves selecting a base image and then transforming the spectral characteristics of all other images obtained on different dates to have the same radiometric scale as the base image. This process aided the reduction of in-between scene variability as a result of potential differences in atmospheric conditions during satellite scene acquisition. Three 1989 bands (that is, Band 4, 3 and 2) were used as a reference bands for normalising the respective bands for 1976 and 2000 images. Normalization techniques depends on the principle that a mathematical model can be used to describe normalization of two images of the same scene, acquired at different times using linear regression, assuming that: (a) the sampled pixel value at time one is linearly related to the pixel of the same location sampled at a different time, and (b) There are a minimum number of pixels in the scene representing features of a surface with invariable reflectance though time. These are called pseudo-invariant objects (PIOs) or Radiometric Normalization Targets (RNTs). PIOs are spatially well defined and spectrally stable though time and are normally selected by visual inspection. These include surfaces such as water bodies and concrete/tarred surfaces as used for this study. This pre-processing of imagery (radiometric correction) thus enables the relation of results from different years.
2.1. Study area Lake Chivero, formerly known as Lake McIlwaine, lies 37 km to the South West of Harare (17°540 ; 30°480 ) and was dammed in 1952 (Chikwenhere and Phiri, 1999). Fig. 1 shows the location of the lake within the Manyame catchment. The lake is the main source of water for domestic and industrial activities in Harare as well as agricultural irrigation within the catchment. The lake also supports recreational activities such as angling, boat fishing and sailing. Lake Chivero is 13.6 km long with an average width of about 2 km and with a catchment area of 2.136 km2. It has a capacity of 250,400,000 m3 and a mean depth of 9.4 m. Approximately 10% of the catchment can be classified as urban and 90% rural, mainly used for agricultural production and communal pasture. The rivers Manyame, Marimba and Mukuvisi are the main tributaries flowing into the lake. The city of Harare has two waste water treatment plants namely Crowbrough which discharge effluent into Marimba River, and Firle disposing effluent into Mukuvisi River. Poorly treated effluent which does not comply with the set environmental standards is sometimes discharged into these two rivers. The town of Chitungwiza located upstream of Lake Chivero also disposes effluent from its waste water treatment plant into Nyatsime River that eventually drains into Lake Chivero. According to Marshall (2005) run-off from surrounding commercial farms washes soil nutrients into the Manyame river system and thus contributes to the enrichment of the waters in Lake Chivero. Lake Chivero is a warm monolithic lake with water removal time of 8.82 years (Mitchell and Marshall, 1974). The lake became eutrophic in early 1960s and this resulted in the appearance of seasonal cynobacteria blooms in the 1960s and the establishment of permanent bloom in 1963 (Munro, 1966). Water samples collected from the lake in 1992 revealed unacceptably high levels of nitrates and phosphates (Moyo, 1997). Nutrient enrichment on the lake led to eutrophication and subsequently the proliferation of Eichhornia crassipes and other macrophytes. Since then aquatic weed proliferation in Lake Chivero has been a concern.
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Fig. 1. Location of Lake Chivero within the Manyame Upper Catchment.
2.2. Spatial distribution of aquatic weeds The area of Lake Chivero covered by aquatic weeds was determined from classification of Landsat satellite images resampled to 30 m spatial resolution. Other remote sensing studies have indicated that aquatic vegetation yields spectrally distinct signals governed by the density of the vegetation, the openness of the canopy and the amounts, forms and orientations of the leaves (Penuelas et al., 1993). Multi-temporal remote sensing data can give valuable information on vegetation changes taking place in any given area. Jensen et al. (1993) for example, detected changes in wetlands and areas of aquatic vegetation with the aid of remote sensing data. Biomass assessment is another successful area of application for remote sensing data (see Armstrong, 1993; and Zhang, 1998). For this study the supervised classification technique was used and training sites were selected based on the familiarity with the geographical area and knowledge of the actual surface cover types present, a feat which was achieved by the use of the aerial photographs and field surveys. The field data on aquatic vegetation, collected between December 2005 and March 2006 consisted of information on aquatic weed species, coverage and biomass. The point intercept method was used to acquire weed samples from Lake Chivero and measure their biomass in the field. The objective of point intercept is to make measurements at regularly spaced, pre-selected or defined locations and to avoid subjectively selecting locations in the field (Madsen, 1999). The points were generated at 200 m interval. A global positioning system (GPS) receiver was used to locate the points on the Lake. A polyvinyl chloride (PVC) pipe measuring 30 cm by 30 cm (could float on water) was used to establish small quadrants from which the weed samples were taken. The small size of the quadrant was meant to facilitate easy transportation
of the weeds to the measuring points. However, since some of the aquatic weeds were floating, they could not be established from some points, thus alternative points near the selected points were chosen in such cases. Wet weight of aquatic weeds was measured using a mass scale. One-way analysis of variance (ANOVA) was used to test whether biomass of the six aquatic weed species was significantly different. 2.3. Abundance estimation using biomass Vegetation indices (VIs) have been widely used to estimate biomass (see for example Haboudane et al., 2002, 2004; Zarco-Tejada et al., 2004, 2005). Vegetation Indices are combinations of reflectance at two or more wavelengths from the red and near infra red (NIR) region of the electro magnetic spectrum (EMS). It has been established that VIs are highly correlated with green-leaf biomass (Sellers, 1985; Mutanga, 2004). This physiological relationship has been used to estimate for example, photosynthetically active radiation (PAR) of plant canopies (Baret and Guyot, 1991; Sellers et al., 1992), percent canopy cover (Haboudane et al., 2004), chlorophyll content (Jayaraman and Srivastava, 2002; Broge and Leblanc, 2000) and leaf area index (LAI) (Asrar et al., 1984). The most widely used vegetation index is the ‘normalized difference vegetation index’ (NDVI). To estimate biomass from imagery, NDVI was therefore used because it is an established index for estimating vegetation quantity and can be used as an indicator of relative biomass (vegetation amount) and greenness. NDVI has also been found to correlate better with yield (biomass) than other vegetation indices and thus continues to be used as biomass indicator using remotely sensed image data (Singh et al., 2002). The index is calculated from the reflected solar radiation in the near-infrared (NIR) and red (R) wavelength bands (Eq. (1))
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NDVI ¼
NIR R NIR þ R
ð1Þ
where NIR is the Landsat TM band 4 and R is the Landsat TM band 3. The NDVI is a nonlinear function which varies between 1 and +1. The higher the positive values the higher the plant biomass such that the absence of green leaves gives a value close to zero and full vegetation cover gives values close to +1. Negative values result from non-vegetated features such as water and clouds. NDVI varies with absorption of red light by plant chlorophyll and the reflection of infrared radiation by water-filled leaf cells. Typical NDVI values for vegetation range between 0.1 and 0.7. NDVI was calculated for the respective images to ascertain changes in the quantity of aquatic weeds. Box plots based on histograms of the respective images were then used to show whether there were variations in the amount of biomass while a t-test was used to determine whether there were any significant changes (differences) in biomass as inferred from NDVI calculations. 3. Results and discussion 3.1. Area covered by aquatic weeds Fig. 2 shows the coverage of aquatic weed estimated from Landsat images for 1976, 1989 and 2000. Aquatic weeds covered almost half of the Lake in 1976 (42%), and declined in the subsequent years (36% in 1989 and 22% in 2000). In 1976, most of the weeds were concentrated near the spillway (Tiger Bay, circled) (Marshal, 2006) with few strands being found along the rest of the shorelines. The northeast winds tend to drive the floating weeds towards the southern shore especially in the Tiger Bay. The suffocation of Manyame River (the area shown by the rectangle) through weed clogging is clearly discernible in the 1989 and 2000 images. This was most likely water hyacinth, Eichhornia crassipes since it was the dominant weed during the 1970s (Magadza, 1997). E. crassipes is an invasive aquatic macrophyte associated with major negative economic and ecological impacts to Lake Chivero ecosystem since the plant’s establishment in the
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1970s. The clogging of Manyame River seems to have continued into the 1980s. In the 2000 image the extent of aquatic weeds in the lake had declined as compared to the previous two years. However, there was a notable increase of aquatic weeds in the years 1989 and 2000 at the ‘entrance’ of Manyame River to Lake Chivero. The concentration of aquatic weeds at the ‘entry point’ of Manyame River was attributable to significant amounts of nutrients coming from its drainage area. Field work in 2005/2006 for this study also included collection of water samples for laboratory analysis. It was found out that high amounts of nitrates (1.2 mg/l) and phosphates (2.81 mg/l) were observed from samples taken at the point where Manyame River enters Lake Chivero. This was a higher concentration of nutrients than was measured in the lake (0.3 mg/l nitrates and 1.2 mg/l phosphates). The results from the remote sensing images show that the aquatic weed coverage in the lake had declined significantly over the past three decades. In 1976, the area under weed occupancy was 967 ha that translate to 42% of the total Lake area. This declined to 863 ha (36%) and 606 ha (22%) in 1989 and 2000 respectively. Chemical spraying and mechanical weed control methods used during the 1960s through the 1970s coupled with reduced nutrient enrichment might have contributed to the decline (Chikwenhere and Phiri, 1999). Furthermore, weed management efforts particularly the introduction of biological control in 1990 using water-hyacinth weevils, N. eichhorniae and N. bruchi diminished most of the water hyacinth that had manifested itself in Lake Chivero (Chikwenhere and Phiri, 1999). 3.2. Aquatic weed abundance in Lake Chivero Aquatic weed biomass samples collected in the field in 2005/ 2006 showed variations in the amount of biomass per unit area. The results of the analysis of variance (ANOVA) test indicate that there is a significant (p < 0.05) difference between the biomass of all the six weed species identified from field surveys. Fig. 3 indicates the variation in biomass per square metre of the six weeds.
Fig. 2. Area covered by aquatic weeds in 1976, 1989 and 2000 The rectangle shows where the Manyame River enters Lake Chivero and the circle shows area near the spill way (Tiger Bay).
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14 Mean
±SE
±SD
12
Biomass [kg/m2]
10
8
6
4
2
0 E.crassipes P.strattiotes T.capensis H.ranunculoides P.australis P.senegalensis Weed Name Fig. 3. Mean Biomass of Phragmites australis, Pistia stratiotes, Hydrocotyle ranunculoides. Typha capensis, Eichhornia crassipes and Persicaria senegalensis.
Typha capensis although found in limited localities, has more biomass per square metre (12 kg/m2) than any other weed type. Phragmites australis , Hydrocotyle ranunculoides and E. crassipes have relatively high biomass per square metre (over 8 kg/m2) while Persicaria senegalensis and Pistia stratiotes have little biomass (less than 6 kg/m2). The morphology of these aquatic weeds explains why such weeds have a higher biomass per unit area. P. australis and T. capensis are rooted plants and therefore can sustain a higher upward canopy. On the other hand P. stratiotes seems to grow in small blobs and from sampled locations was being outcompeted by E. crassipes. It can be concluded therefore that aquatic weed biomass is a function of not only the morphology of the plant but also the ability to effectively withstand competition for nutrients, floating space and light as well as the ability to resist the diminutive effects of the water currents. Weeds such as E. crassipes tend to out-compete and suppress the growth of P. stratiotes, only a few P. stratiotes plants could be observed enmeshed underneath E. crassipes. 3.3. Aquatic weed biomass variations An aerial estimation of weed biomass over the three decades was derived from the images by calculating NDVI which was related to actual biomass. Higher NDVI values corresponded to higher biomass. This relationship can indicate the difference in abundance of green aquatic weed in different locations. However, the biomass variations are not only dependent on the abundance of aquatic weed, but also on the aquatic weed species. NDVI as an estimate of weed abundance in Lake Chivero fluctuated over the years with a notable increase of aquatic weed biomass in 1989 and a decline in 2000 (Fig. 4). In order to test if the difference in NDVI over the three years (1976, 1989 and 2000) was significant, the NDVI data was first tested for normality. The test revealed that the data did not follow a normal distribution and as such the non-parametric Mann Whitney-U-test was performed on paired NDVI years as shown in Table 1. The null hypothesis of no significant difference in NDVI between the years was tested. The results indicated that there are significant differences in the variance of NDVI values across the lake between the three years
Fig. 4. NDVI changes over the last three decades. N is the number of pixels contributing to the total NDVI values for the respective years.
Table 1 Results of the Mann Whitney-U -test on NDVI Years
Z value
P value
Mean NDVI (biomass)
1976 and 1989 1989 and 2000 1976 and 2000
53.376 53.856 11.028
0.00 0.00 0.00
1976 = 0.348 1989 = 0.51 2000 = 0.3
(1976, 1989 and 2000). There was a statistically significant increase in NDVI (and hence biomass of aquatic weeds) from a mean of 0.348 in 1976 to 0.51 in 1989. Furthermore, there is a significant decline in NDVI for aquatic weed (mean of 0.3) in 2000. This decline is both significant when compared to the years 1998 and 1976. 3.3.1. Aquatic weeds spatial and temporal variation Between 1986 and 1991 an outbreak of aquatic weeds that led to massive fish kills was observed in the Lake, referred to as the third outbreak after the 1971/72 outbreak (Chikwenhere and Phiri, 1999). This third outbreak might explain the increase of NDVI
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values measured in 1989. However, since the total area covered by aquatic weeds was actually smaller than in 1976 the outbreak must have promoted the proliferation of aquatic weed species with higher biomass. Thus the average increase of NDVI values from 1976 to 1989 with the consecutive decrease of area covered by the weed over the same time can be explained by the difference in the canopy structure of the weeds and therefore different weed species distribution. As such, in 1976, some weeds were covering a large area of the lake, but those were the weed species having a thin canopy structure and therefore less biomass. The thin canopy structure of those species allows more transmission of light through the canopy as compared to species with a thick canopy
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structure. Hence, the species with a thin canopy display lower NDVI values when compared to those species with a thick canopy structure. By comparing differences in biomass of the different species with the NDVI histograms, it can be deduced that E. crassipes and T. capensis, which are the high biomass species (Fig. 5), are contributing to the pixels with high NDVI values. The other aquatic weeds with a comparatively low biomass (P. stratiotes and P. senegalensis) constitute the low values of the NDVI on the images. In the same thrust it can also be asserted that P. australis and H. ranunculoides constitute generally less biomass than E. crassipes and T. capensis thus should be contributing to the generally lower NDVI values which could be the (lower) peaks in the
Fig. 5. (A) Year 1976 NDVI coverage and NDVI histogram for Lake Chivero, (B) Year 1989 NDVI coverage and NDVI histogram for Lake Chivero, (C) Year 2000 NDVI coverage and NDVI histogram for Lake Chivero.
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1976, 1989 and 2000. P. senegalensis and P. stratiotes have low biomass and should necessarily contribute to the lower end of the NDVI histograms. These biomass variations are shown in Fig. 5 below. In 1976, high biomass weeds were concentrated around Tiger Bay, the dam wall and in the Manyame River. However, the low biomass aquatic weeds constituted the bulk of the aquatic weeds in the 1976. Thereafter, there was dramatic rise in the abundance of high biomass aquatic weeds in 1989 coupled with an equally striking decline in 2000. Subsequent to the water hyacinth outbreak in 1985 to around 1995, field observations made by Chikwenhere and Phiri (1999) found that there was a decline in E. crassipes and P. stratiotes that led to their succession by H. ranunculoides and Myriophyllum aquaticum (parrot’s feather). This is confirmed by the NDVI measurements (high frequency of pixels at NDVI value of 0.71 in the image (Fig. 5), where the histogram of 1989 has a large area covered with intermediate high NDVI values hypothesised to be the water hyacinth species based on the field measurements of biomass. Whereas the NDVI histogram of the 2000 image had flattened out, i.e. the peak around 0.71 NDVI of the 1989 histogram, it disappeared in the year 2000. However, evidence from the two satellite images of 1989 and 2000 confirmed that there was a decline in aquatic weed abundance in Lake Chivero. Six years later, field observations in 2005/2006 found that E. crassipes is again the most abundant aquatic weed in the lake and covers the largest area (Fig. 6). The reversal in abundance of these weeds can be explained by the fact that water hyacinth tends to out-compete all other weeds in the absence of its natural enemies (weevils) or other control measures. Weed control measures, if successful have the effect of reducing the amount of aquatic weeds in the Lake. Efforts to control aquatic weeds in Lake Chivero stretches back to 1995 when the initial outbreak of aquatic weeds was recognised as a potential problem (Chikwenhere and Phiri, 1999). Machinery to remove aquatic weed in the form of tractors, rakes and nets have been employed to rid Lake Chivero of its weeds since the first water hyacinth infestations in 1971. N. eichhorniae and N. Bruchi weevils were introduced in 1990 to initiate the first biological control in Lake Chivero (Chikwenhere and Phiri, 1999). Concurrent with the above mentioned control methods, the weeds were not only manually removed, but also spayed with 2–4D chemical and glyphosphate. Results from this study corroborate with those of Chikwenhere (2001) who, although monitoring E. crassipes only, observed that had been a general decrease in E. crassipes infestation in Lake
Fig. 6. Water Hyacinth in Lake Chivero (2005).
Chivero since 1991. This research established that the total area covered with aquatic weed declined since 1976 from 42% to 36% in 1989 and 22% in 2000. Detailed analysis of the biomass distribution of the aquatic weeds in the lake, deduced from the spectral characteristics, also showed an apparent reduction in the area of weed species of intermediate high biomass. The decline in E. crassipes is attributable to two main factors which are flooding of the Lake, and biological control. The flooding that took place in 1996/97 is said to have flushed the aquatic weed over the dam wall of the Lake. Evidence of this is also visible in the satellite images where the 1998 image shows little aquatic weed in the lake Manyame which is fed by the water from Lake Chivero, whereas the 2000 image shows increased presence of aquatic weed in lake Manyame. In addition to the flooding of the dam wall congruent biological control reduced the vigour and flower production of water hyacinth. 4. Conclusions Remote sensing and GIS techniques provided a means of obtaining useful information on spatial and temporal changes in aquatic vegetation in Lake Chivero. The results show the applicability of these tools for detecting changes in emergent and floating leaved aquatic vegetation. The use of historical images enables the quantification of long-term changes of aquatic vegetation even in cases where no field data prior to a lake management project exist. The ability to relate NDVI to biomass using satellite imagery offers further prospects of quantifying the abundance of different aquatic weed species in the lake and hence opens up opportunities for applying targeted management efforts on areas of interest. Overall, relating NDVI histograms, area covered by weeds and aquatic weed abundance helps in comprehending the aquatic weed dynamics within the lake and can be applied to any other aquatic system. In addition, GIS and remote sensing can be invaluable assets for detection of invasions (like the entrance of Manyame River into Lake Chivero in 1989 and 2000 images), assessment of infestation levels, monitoring rate of spread, and determining the efficiency of mitigation efforts for weed management as shown by this research. References Armstrong, R.A., 1993. Remote sensing of submerged vegetation canopies for biomass estimation. International Journal of Remote Sensing 14 (3), 621–627. Asrar, G., Fuchs, M., Kanemasu, E.T., Yoshida, M., 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agronomy Journal 76, 300–306. Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161–173. Broge, N.H., Leblanc, E., 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172. Chikwenhere, G.P., 2001. Biological and Integrated Control of Water Hyacinth, Eichhornia Crassipes. Proceedings of the Second Meeting of the Global Working Group for the Biological and Integrated Control of Water Hyacinth, Beijing, China, 9–12 October 2000. Australian Centre for International Agricultural Research, Canberra. Chikwenhere, G.P., Phiri, G., 1999. History and Control Effort of Water Hyacinth, Eichhornia Crassipes on Lake Chivero in Zimbabwe. In: Hill M.P., Center T.D., Julien M.H. In: Proceedings of the First IOBC GLOBAL Working Group Meeting for the Biological and Integrated Control of Water Hyacinth. November 199891-100 Harare, Zimbabwe. Chudamani, J.B., de Leeuwa, J., van Durenaa, I.C., 2004. Remote Sensing and GIS Applications for Mapping and Spatial Modelling of Invasive Species. Department of Natural Resources, International Institute for Geo-information Science and Earth Observation (ITC). Duarte, C.M., 1995. Submerged aquatic vegetation in relation to different nutrient regimes. Ophelia 41, 87–112. Gurure, R., 1999. Water Hyacinth: Searching for Lasting Solutions to Control This Weed Menace in Zimbabwe. Paper presented at the Workshop on Sustainable Management of the Lakes of Zimbabwe, 24–25 February 1999, Holiday Inn Harare.
M.D. Shekede et al. / Physics and Chemistry of the Earth 33 (2008) 714–721 Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416–426. Haboudane, D., Miller, J.R., Pattey, E., Pablo, J., Zarco-Tejada, P., Strachane, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352. Hall, F.G., Stebel, D.E., Nickenson, J.E., Goetz, S.J., 1991. Radiometric rectification: toward a common radiometric response among multi-date, multi-sensor images. Remote Sensing of Environment. 35, 11–27. Jayaraman, V., Srivastava, S.K., 2002. The invariance of red edge inflexion wavelengths derived from ground based spectro-radiometer and space borne IRS-P3: MOS-B data. International Journal of Remote Sensing 23, 14. Jensen, J.R., Cowen, D.J., Althausen, J.D., Narumalani, S., Weatherbee, O., 1993. An evaluation of the coast watch change detection protocol in South Carolina, photogramm. Engineering of Remote Sensing 59 (6), 1039–1046. Jeppesen, E., Sammalkorp, I., 2002. Lakes. In: Handbook of Restoration Ecology. In: Perrow, M., Davy, T. (Eds.), Restoration in Practice, vol. II. Cambridge University Press, Cambridge, UK, pp. 1–32. Madsen, J., 1999. Point Intercept and Line Intercept Methods for Aquatic Plant Management. Aquatic Plant Control Technical Note M1-02. Magadza, C.H.D., 1997. Water pollution and catchment management. In: Moyo, N.A.G. (Ed.), Lake Chivero a Polluted Lake. University of Zimbabwe Publications, Harare. Malone, T.C., Kemp, W.M., Ducklow, H.W., Boynton, W.R., Tuttle, J.H., Jonas, R.B., 1986. Lateral variations in the production and fate of phytoplankton in a partially stratified estuary. Marine Ecology Progress Series 32, 149–160. Marshal, B.E., 2006. Personal communication. Marshall, B.E., 2005. The impact of eutrophication on Lake Chivero, Zimbabwe: a tropical African reservoir. In: Reddy, M.V. (Ed.), Restoration and Management of Tropical Eutrophic Lakes 2005. Science Publishers, Inc., UK. Mitchell, D.S., Marshall, B.E., 1974. Hydrobiological observations on three Rhodesian reservoirs. Freshwater Biology 4, 61–72. Moyo, N.A.G. (Ed.), 1997. Lake Chivero: A Polluted Lake. University of Zimbabwe, Harare. Munro, J.L., 1966. A limnological survey of Lake Mcllwaine, Rhodesia. Hydrobiologia 28, 281–308. Mutanga, O., 2004. Hyperspectral remote sensing of tropical grass quality and quantity. PhD Thesis. WU, The Netherlands.
721
Penuelas, I., Fiella, C., Serrano, B.L., Save, R., 1993. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote sensing 14, 1887–1905. Pieterse, A.H., 1990. Introduction. In: Pieterse, A.H., Murphy, K. (Eds.), Aquatic Weeds: The Ecology and Management of Nuisance Aquatic Vegetation. Oxford University Press, Oxford, England, pp. 1–16. Risser, P.G., Treworgy, C.G., 1985. Overview of Ecological Research Data Management. – Research Data Management in the Ecological Sciences. University of South Carolina Press, Columbia. Schouten, L.S.M., van Leeuwen, H.J.C., Bakker, J.G.M., Twongo, T., 1999. Water Hyacinth Detection in Lake Victoria by Means of Satellite SAR: Netherlands Remote Sensing Board USP-2 98-28. Sellers, P.J., 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 6 (8), 1335–1372. Sellers, P., Hall, F.G., Asrar, G., Strebel, D.E., Murphy, R.E., 1992. An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). Journal of Geophysics Research 97, 18345–18371. Shaw, D.R., 2004. Translation of remote sensing data into weed management decisions. Spring 53 (2), 264–273. Singh, B., Balasubramanian, V., Singh, J., Khind, C.S., Singh, Y., Ladha, J.K., Bronson, K.F., 2002. Chlorophyll meter and leaf colour – chart-based nitrogen management for rice and wheat in north-western India. Agron. J. 94, 821– 829. Soule, M.E., Kohm, K.A., 1989. Research Priorities for Conservation Biology. Island Press, Washington DC. Van der Meer, F., Schmidt, K.S., Bakker, A., Bijker, W., 2002. New environmental RS systems. In: Skidmore, A.K. (Ed.), Environmental Modelling With GIS and RS. Taylor & Francis, London, pp. 26–51. Zarco-Tejada, P.J., Miller, J.R., Morales, A., Berjo´n, A., Agüera, J., 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment 90 (4), 463–476. Zarco-Tejada, P.J., Whiting, M., Ustin, S.L., 2005. Temporal and spatial relationships between within-field yield variability in cotton and high spatial hyperspectral remote sensing imagery. Agronomy Journal 97 (3), 641–653. Zhang, X., 1998. On the estimation of biomass of submerged vegetation using satellite thematic mapper (TM) imagery: a case study of the Honghu, PR China. International Journal of Remote Sensing 19 (1), 11–20. The Global Land Cover Facility (1997–2008) University of Maryland.