Webster Gumindoga

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

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.

Hydrological Impacts of Urbanization of Two Catchments in Harare, Zimbabwe

By increased rural-urban migration in many African countries, the assessment of changes in catchment hydrologic responses due to urbanization is critical for water resource planning and management. This paper assesses hydrological impacts of urbanization on two medium-sized Zimbabwean catchments (Mukuvisi and Marimba) for which changes in land cover by urbanization were determined through Landsat Thematic Mapper (TM) images for the years 1986, 1994 and 2008. Impact assessments were done through hydrological modeling by a topographically driven rainfall-runoff model (TOPMODEL). A satellite remote sensing based ASTER 30 metre Digital Elevation Model (DEM) was used to compute the Topographic Index distribution, which is a key input to the model. Results of land cover classification indicated that urban areas increased by more than 600 % in the Mukuvisi catchment and by more than 200 % in the Marimba catchment between 1986 and 2008. Woodlands decreased by more than 40% with a greater decrease in Marimba than Mukuvisi catchment. Simulations using TOPMODEL in Marimba and Mukuvisi catchments indicated streamflow increases of 84.8 % and 73.6 %, respectively, from 1980 to 2010. These increases coincided with decreases in woodlands and increases in urban areas for the same period. The use of satellite remote sensing data to observe urbanization trends in semi-arid catchments and to represent catchment land surface characteristics proved to be effective for rainfall-runoff modeling. Findings of this study are of relevance for many African cities, which are experiencing rapid urbanization but often lack planning and design.

An analysis of changes in the urban landscape composition and configuration in the Sancaktepe District of Istanbul Metropolitan City, Turkey using landscape metrics and satellite data

Changes in landscape composition and configuration patterns of Sancaktepe Municipal District in the Asian side of Istanbul Metropolitan City of Turkey were analysed using landscape metrics. Class-level and landscape-level metrics were calculated from the land cover/land use data using Patch Analyst, an extension in the Arc View GIS. The land cover/land use data were derived from classified satellite images of Landsat Thematic Mapper of 2002 and 2009 for Sancaktepe District. There was evidence of increase in agglomeration process of built-up patches as indicated by the increases in mean patch size, decrease in total edge and number of patches between 2002 and 2009. The urban expansion pattern experienced overall was not fragmented but concentrated due to infilling around existing patches. Changes in Area-Weighted Mean Shape Index and Area-Weighted Patch Fractal Dimension Index indicated that the physical shapes within built-up, forest and bareland areas were relatively complex and irregular. A conclusion is made in this study that spatial metrics are useful tools to describe the urban landscape composition and configuration in its various aspects and certain decisions whether to approve a specific development in urban planning could, for example, be based on some measures of urban growth form or pattern in terms of uniformity and irregularity, attributable to the dynamic processes of agglomeration and fragmentation of land cover/land use patches caused by urban expansion.

Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing

Abstract The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa.

Heavy Metals Inflow into Lake Muhazi, Rwanda

Most tropical African lakes are facing pollution problems due to the rapid population growth and industrializa- tion in their riparian communities. Lake Muhazi in Rwanda is one of such lakes which has experienced a dramatic decrease in fish production since the eighties, with also reports of low water transparencies and high turbidities. The lakeshores are now being developed for ecotourism and this requires sound environmental management to make the planned activities viable. The purpose of this study was to assess heavy metal pollution in the tributary rivers of Lake Muhazi. The concentration of heavy metals in major tributaries was monitored for the period July to October 2007. The parameters studied are cadmium, chromium, copper, iron, lead, manganese, zinc, pH and temperature. Fourteen sampling stations on the Inflow Rivers and the spillway were monitored fortnightly. The samples collected for heavy metal analysis were preserved and stored in cooler boxes and analyzed in the laboratory using Standard Methods. Temperature and pH were measured in the field using HACH field testing kits. The concentration (mean ± standard deviation) of Zn was found to be 0.040±0.103 mg/L, Cd 0.031±0.007 mg/L, Pb 0.487±0.452 mg/L, Fe 7.53±13.34 mg/L, Mn 1.01±1.31 mg/L, chromium 0 mg/L and copper 0 mg/L. The concentrations of measured parameters deviate much from the recommended ones thus posing serious problems to aquatic life. It has also been shown that the high metal concentration levels in Lake Muhazi are related to landuse activities in the catchment. It is thus recommended that farming practices and erosion be controlled in the catchment to contain pollutant discharges into the lake. Lead is the main anthropogenic pollutant which has been found in the watershed of Lake Muhazi.

Characterization of the top sediment layer in coastal intertidal mudflats from medium-to-coarse resolution satellite imagery and field measurements

Intertidal sediments are critically important in controlling intertidal mudflat microphytobenthic primary productivity and the functioning of intertidal ecosystems. This paper demonstrates the possibility of deriving different intertidal sediment properties from coarse-to-medium resolution remote sensing imagery. Supervised and image based classification methods were used to map different substrate types based on the Spectral Angle Mapper (SAM) algorithm. The algorithm characterized different sediment properties from remote sensing data based on field collected and image-extracted endmembers. The results demonstrate that, different substrate types can be derived from coarse-to-medium resolution images using SAM algorithm. Supervised and image-based classification methods performed well in deriving intertidal sediment properties. From the results, sand sediments cover a wide area in extent than clay whereas Normalized Difference Vegetation Index (NDVI) validation results indicate that, clay sediments have higher NDVI values as compared to sand sediments. We conclude that, intertidal sediment properties can be successfully derived from coarse-to-medium resolution satellite imagery. Key words: Endmember, microphytobenthos, spectral signature, substrates, trios ramses, wadden sea.

Health Safety of Drinking Water Supplied in Africa: A Closer Look Using Applicable Water-Quality Standards as a Measure

The World Health Organization (WHO) is mandated to provide health information to member countries as one of its major objective. In water quality, it developed health-based guidelines to assist nations and other organisations involved, with a baseline for developing their own enforceable water-quality standards. The European Union (EU), United States (US) and China have standards developed for their regions and nations. Such regional standards are useful for sharing and transferring technology among member countries or states to meet the respective set quality standards. However, Africa has not developed such regional standards. This study investigates 21 water-quality parameters of 18 selected African countries, to assess if they are significantly different across countries and compare them to those of WHO, EU, US and China. There were significant statistical differences among twenty of the twenty-one studied parameters. The mean quality standards of Africa were generally higher (weaker) than those of WHO, EU and China but compared well with those of US. Nations were urged to frequently update without delays, their standards as new information become available, as there are emerging novel pollutants that are of health concern. The African Union or smaller groupings like the Economic Community of West African States (ECOWAS) and Southern African Development Community (SADC) should develop regional water-quality standards and benefit from knowledge and technological sharing, leading to improved supply of high-quality water in their individual countries.

Performance of bias correction schemes for CMORPH rainfall estimates in the Zambezi River Basin

Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal crossvalidation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p<0.05) in the daily means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc tests revealed that the STB and EZ bias-correction schemes are preferable. Bias is highest for very light rainfall (<2.5 mm d−1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile–quantile (q– q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by >28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance>10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.

In-depth analysis of the impacts of rural population growth on the natural environment: a GIS and remote sensing approach

The rural population of Zimbabwe has been growing rapidly since the country gained independence in April 1980. This increased pressure and degradation of the land; hence there was a need to sustain the growing population and its subsequent impacts on rural areas. This led to the implementation of the land reform programme in 2000, which intended to provide more land for settlement and crop production. Although this was successful to a certain extent, investigation is required to determine the changes in spatial and temporal variations in landcover for better management and allocation of natural resources to sustain the rural population and economic development. This study, therefore, used multispectral remote sensing Landsat images to determine landcover changes in one of the rural areas of Masvingo Province in Zimbabwe, using a maximum likelihood classifier. A post-classification change detection technique was also used to produce change through cross-tabulation. The findings of this study have demonstrated significant changes in landcover over the years (i.e. 1990–2010). For instance, it was observed that agricultural fields and human settlements increased dramatically during the period. Overall, the results of this study suggest that remotely sensed data provides an ideal platform for constant inventorying of natural resources.