Using Landsat satellite imagery to monitor the spatial and temporal dynamics of aquatic weed extent in Lakes Chivero and Manyame, located in an urban catchment of Zimbabwe

This study quantified the spatial and temporal variation of aquatic weeds in two lakes in an urban catchment of Zimbabwe using the automatic water extraction index (AWEI) and normalised difference vegetation index (NDVI) derived from Landsat satellite data from 1986 to 2020. Extent of aquatic weeds estimated using AWEI in Lake Chivero increased from less than 1 km2 (4%) in 1986 to 7 km2 (27%) in 2020. NDVI-based aquatic weed estimation gave the least spatial extent in the first few years. Similarly, in Lake Manyame aquatic weeds occupied ~62 ha (<1% in 1986) before reaching a peak extent of 60 km2 (~70%) in 1995, based on AWEI estimates. NDVI-derived aquatic weed extent ranged from less than 2 km2 in 1997 to a maximum of 56.12 km2 in 1994. Although AWEI and NDVI estimated similar extents, NDVI had higher estimates than AWEI. A non-significant positive trend in aquatic weed extent was detected for Lake Manyame based on AWEI (Mann-Kendal tau = 0.139, s = 69, p = 0.27) and NDVI (Mann-Kendal tau = 0.129, s = 64, p = 0.307). In Lake Chivero, a non-significant negative trend was observed in aquatic weed extent based on NDVI (Mann- Kendal tau = −0.06, s = −30, p = 0.6382), while a positive trend was detected using AWEI (tau = 0.0036, s = 18, p = 0.7827). Results of the regression analysis indicate that phosphorus (R2 = 0.7957, p = 0.00122) and nitrogen (R2 = 0.8992, p = 0.0011) significantly explained variations in aquatic weed infestation in Lake Chivero. These results suggest that phosphorus and nitrogen enrichment are key drivers of aquatic weed proliferation in the two lakes. Thus, sustainable management of water resources in the catchment hinges on reducing the amount of nutrients released into the lakes from sewage treatment plants and croplands.&nbsp

Using Landsat satellite imagery to monitor the spatial and temporal dynamics of aquatic weed extent in Lakes Chivero and Manyame, located in an urban catchment of Zimbabwe

This study quantified the spatial and temporal variation of aquatic weeds in two lakes in an urban catchment of Zimbabwe using the automatic water extraction index (AWEI) and normalised difference vegetation index (NDVI) derived from Landsat satellite data from 1986 to 2020. Extent of aquatic weeds estimated using AWEI in Lake Chivero increased from less than 1 km2 (4%) in 1986 to 7 km2 (27%) in 2020. NDVI-based aquatic weed estimation gave the least spatial extent in the first few years. Similarly, in Lake Manyame aquatic weeds occupied ~62 ha (<1% in 1986) before reaching a peak extent of 60 km2 (~70%) in 1995, based on AWEI estimates. NDVI-derived aquatic weed extent ranged from less than 2 km2 in 1997 to a maximum of 56.12 km2 in 1994. Although AWEI and NDVI estimated similar extents, NDVI had higher estimates than AWEI. A non-significant positive trend in aquatic weed extent was detected for Lake Manyame based on AWEI(Mann-Kendal tau = 0.139, s = 69, p = 0.27) and NDVI (Mann-Kendal tau = 0.129, s = 64, p = 0.307). In Lake Chivero, a non-significant negative trend was observed in aquatic weed extent based on NDVI (Mann- Kendal tau = −0.06, s = −30, p = 0.6382), while a positive trend was detected using AWEI (tau = 0.0036, s = 18, p = 0.7827). Results of the regression analysis indicate that phosphorus (R2 = 0.7957, p = 0.00122) and nitrogen (R2 = 0.8992, p = 0.0011) significantly explained variations in aquatic weed infestation in Lake Chivero. These results suggest that phosphorus and nitrogen enrichment are key drivers of aquatic weed proliferation in the two lakes. Thus, sustainable management of water resources in the catchment hinges on reducing the amount of nutrients released into the lakes from sewage treatment plants and croplands

Estimation and Web-GIS geovisualisation of a suitable solid waste disposal site: Case study of New City, Harare

With the ever-increasing human population, there is a need to develop new urban settlements for human habitation. For these new settlements, it is imperative to optimally site different land-use zones, including solid waste disposal sites. The aim of this article is to determine suitable sites for locating a landfill in a new developed city in Zimbabwe around Mt. Hampden, named the New City. The New City will have various residential, commercial, and industrial areas. This entails the need for a proper site selection of a landfill to reduce the negative social and environmental effects such as contamination of water bodies and proliferation of diseases such as malaria. GIS and remote sensing were the major methods used in mapping the suitable areas. Multi-criteria evaluation and weighted overlay analysis methods were used in the landfill site selection process. Factors used for landfill site selection were rivers, settlements, roads, protected areas, and soils. A suitability map was generated, showing five potential sites that are suitable for landfill siting in the New City. Moderately suitable areas cover approximately 8%. A further 73% of the total land area in the study area is highly unsuitable for siting a landfill. A real-time Web-GIS monitoring interface was developed to monitor land use on the selected area, because the New City is a new area under development. Using a Web-GIS interface makes data easily accessible to environment planners, ecologists, spatial land planners, and other decision makers

Using Landsat satellite imagery to monitor the spatial and temporal dynamics of aquatic weed extent in Lakes Chivero and Manyame, located in an urban catchment of Zimbabwe

This study quantified the spatial and temporal variation of aquatic weeds in two lakes in an urban catchment of Zimbabwe using the automatic water extraction index (AWEI) and normalised difference vegetation index (NDVI) derived from Landsat satellite data from 1986 to 2020. Extent of aquatic weeds estimated using AWEI in Lake Chivero increased from less than 1 km2 (4%) in 1986 to 7 km2 (27%) in 2020. NDVI-based aquatic weed estimation gave the least spatial extent in the first few years. Similarly, in Lake Manyame aquatic weeds occupied ~62 ha (<1% in 1986) before reaching a peak extent of 60 km2 (~70%) in 1995, based on AWEI estimates. NDVI-derived aquatic weed extent ranged from less than 2 km2 in 1997 to a maximum of 56.12 km2 in 1994. Although AWEI and NDVI estimated similar extents, NDVI had higher estimates than AWEI. A non-significant positive trend in aquatic weed extent was detected for Lake Manyame based on AWEI (Mann-Kendal tau = 0.139, s = 69, p = 0.27) and NDVI (Mann-Kendal tau = 0.129, s = 64, p = 0.307). In Lake Chivero, a non-significant negative trend was observed in aquatic weed extent based on NDVI (Mann-Kendal tau = −0.06, s = −30, p = 0.6382), while a positive trend was detected using AWEI (tau = 0.0036, s = 18, p = 0.7827). Results of the regression analysis indicate that phosphorus (R2 = 0.7957, p = 0.00122) and nitrogen (R2 = 0.8992, p = 0.0011) significantly explained variations in aquatic weed infestation in Lake Chivero. These results suggest that phosphorus and nitrogen enrichment are key drivers of aquatic weed proliferation in the two lakes. Thus, sustainable management of water resources in the catchment hinges on reducing the amount of nutrients released into the lakes from sewage treatment plants and croplands

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

Abstract Background In Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe. Methods Bioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics. Results The results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios. Conclusions The study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early