Normal 0 false false false EN-ZA X-NONE X-NONE This study utilized remote sensing and spatial-statistical geostatistics to model future land degradation in the mud-beach coast of southwest Nigeria. Current landcover was derived from Landsat ETM + data. Model input data consists of 12 predictor variables. Attribution of weights to variables was done through multi-criteria evaluation. These weights were used to develop logistic regression function for simulating probability surface maps. Degraded lands accounted for about 30.2% of the total landcover with permanently inundated lands and bare surfaces contributing 22.4%. The results suggest soil, geology, elevation, distance to ocean, and location of old bitumen wells as the most important predictor variables. Simulated composite probabilities for transiting into degraded lands range between 0.4184 and 0.4871 in the next 20 years (from 2001) to between 0.4284 and 0.4973 in the next 100 years. Mangrove, scrub/coastal grassland, farmland/fallow and built-up areas appear to have higher probabilities, while the palm swamp ecosystems have the least.

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

The surface water of Nigeria is argued to be under threat in most parts of the country, especially in the rural environment where it is a main life support system. The level of surface water recession within the Sokoto-Rima River Basin in the northwest Sudano-Sahelian environment of Nigeria is assessed in the study. Interactive remote sensing and Geographic Information System (GIS) techniques are used to map and analyze the hydrology of the basin over a 40-year period (1965 to 2005). The results show alarming reductions in the floodplain area, as well as the lengths, orders, and densities of the river segments and tributaries. The floodplain was reduced by about 40% in areal extent and the length of stream channels by 90%. More recessions are identified around stream headways. The implication for water security and sustainability in the close settled and desert encroached rural areas are reviewed in the conclusion.