Assessing industrial development influence on land use/cover drivers and change detection for West Bank East London, South Africa

South Africa’s nationwide socio-economic industrial development zone drive focuses on alleviating of the apartheid social ills legacy. To ensure sustainable industrial ecological development, land-cover monitoring is needed though limited attention has been accorded. This study, aimed at assessing the influence of East London Industrial Development Zone (ELIDZ) on land-use/land-cover (LULC) drivers and detecting LULC changes for 15 years over the West Bank East London. An integration of remote sensing with qualitative approaches was adopted to provide robust temporal and spatial LULC change analysis. Object-based classification was performed on the satellite images for 1998, 2007 and 2013. Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) complemented and validated observed land cover changes. The study reveals that industrial development has been a key driver for land-use changes in West Bank. The classification indicated that vegetation (5.97%) and bare land (-9.06%) classes had the highest percentage increase and decrease respectively. Water (0.02%) and bare land (-0.6%) classes had the lowest annual rate of change. Built-up and bare land classes varied considerably. An overall land-cover classification mean accuracy assessment of 97.24% and a mean Kappa coefficient of 0.95 were attained for the entire study period. This study offers the value of integrated methods in monitoring land-cover change to enhance informed decision-making especially in rapidly changing landscapes for conservation purposes.This manuscript stems from the corresponding authors’ postgraduate study and who performed most of the experiments.The University of Pretoria and the United State Geological Survey (USCS).http://www.ripublication.comam2019Geography, Geoinformatics and Meteorolog

Evaluating efficacy of landsat-derived environmental covariates for predicting malaria distribution in rural villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5–20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D2) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a1 index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.The South African National Space Agency under the Human Capital Development.http://link.springer.com/journal/103932019-03-01hj2018Geography, Geoinformatics and Meteorolog