Abiodun M. Adeola

Detecting changes in surface water area of Lake Kyoga sub-basin using remotely sensed imagery in a changing climate

Detection of changes in Earth surface features, for example lakes, is important for understanding the relationships between human and natural phenomena in order to manage better the increasingly scarce natural resources. This work presents a procedure of using modified normalised difference water index (MNDWI) to detect fluctuations of lake surface water area and relate it to a changing climate. The study used radiometrically and geometrically rectified Landsat images for 1986, 1995 and 2010 encompassing the Kyoga Basin lakes of Uganda, in order to investigate the changes in surface water area between the respective years. The standard precipitation index (SPI) and drought severity index (DSI) are applied to show the relationship between variability of surface water area and climate parameters. The present analysis reveals that surface water area fluctuation is linked to rainfall variability. In particular, Lake Kyoga sub-basin lakes experienced an increase in surface water area in 2010 compared to 1986. This work has important implications to water resources management for Lake Kyoga and could be vital to water resource managers across Ugandan lakes.

Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa

Abstract The advancement, availability and high level of accuracy of satellite data provide a unique opportunity to conduct environmental and epidemiological studies using remotely sensed measurements. In this study, information derived from remote sensing data is used to determine breeding habitats for Anopheles arabiensis which is the prevalent mosquito species over Nkomazi municipality. In particular, we have utilized the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) coupled with land surface temperature (LST) derived from Landsat 5 TM satellite data. NDVI, NDWI and LST are considered as key environmental factors that influence the mosquito habitation. The breeding habitat was derived using multi-criteria evaluation (MCE) within ArcGIS using the derived environmental metric with appropriate weight assigned to them. Additionally, notified malaria cases were analysed and spatial data layers of water bodies, including rivers and dams, were buffered to further illustrate areas at risk of malaria. The output map from the MCE was then classified into three classes which are low, medium and high areas. The resulting malaria risk map depicts that areas of Komatieport, Malelane, Madadeni and Tonga of the district are subjected to high malaria incidence. The time series analysis of environmental metrics and malaria cases can help to provide an adequate mechanism for monitoring, control and early warning for malaria incidence.

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa

Nkomazi local municipality of South Africa is a high‐risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R2 = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Application of geographical information system and remote sensing in malaria research and control in South Africa: a review

This paper presents a review of numerous items of published literature on the use of spatial technology for malaria epidemiology in South Africa between 1930 and 2013. In particular, focus is on the use of statistical and mathematical models as well as geographic information science (GIS) and remote sensing (RS) technology for malaria research. First, the review takes cognisance of the use of predictive models to determine the association between climatic factors and malaria epidemics only in KwaZulu-Natal province. Similar studies in other endemic regions such as Limpopo and Mpumalanga provinces have not been reported in the literature. While the integration of GIS with remote sensing has the potential of identifying, characterising, and monitoring breeding habitats and mapping malaria risk areas in South Africa, studies on the application of spatial technology in malaria research and control in South Africa are inexhaustive and have not been reported in the literature. As a result, a critical robust mal...

Lake Sibayi variations in response to climate variability in northern KwaZulu-Natal, South Africa

The optimal management of natural resources like lakes requires understanding the relationship with other environmental elements. Remote sensing techniques using multi-temporal and multi-sensor images for change detection purposes are important in this regard. This study used a combination of approaches to detect changes in the lake surface area of Lake Sibayi in relation to changes in past climates. Delineation of the study area is achieved using WR2012 endoreic maps and Landsat satellite images from 1992 to 2016. Using data from eight meteorological stations, past climate of the lake catchment was investigated. Thereafter, a multivariate correlation analysis is conducted to examine the relationship between the changes in the lake surface area and the changes in climatic (precipitation, temperature) variables and water levels. Results suggest that the lake surface area has decreased by 20% since 1992. There are significant changes in temperatures, while the annual rainfall totals declined significantly. The correlation between precipitation in the catchment and annual water level changes is 0.88. Statistically, significant increases in the water level and precipitation were experienced in 1993 and 2001. SPI analysis reveals that the study area is getting drier and the probability of recurrence of moderate dryness is 10%. The rate at which the lake is shrinking is not solely climatic related, as anthropogenic aspects are also responsible. To prevent further shrinkage of Lake Sibayi, it will be necessary to develop aggressive restoration policies and action plans aimed at maintaining inflows in the face of compounding climate change and water demand. Recommendations of the nature of further studies that can increase our understanding are included.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998–2002, 2004–2006, and 2010–2013 and a noticeable periodicity value of 256–512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.

The Hydrometeorology of the Kariba Catchment Area Based on the Probability Distributions

In this paper, monthly, maximum seasonal, and maximum an- nual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribu- tion models of the underlying climatology from which the data were