Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development. Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals (SDGs). Urban sprawl has resulted in unsustainable urban development patterns from social, environmental, and economic perspectives. This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality, Tanzania. Random Forest (RF) method was applied to accomplish imagery classification and location-based social media (Twitter usage) data were obtained through a Twitter Application Programming Interface (API). Morogoro urban municipality was classified into built-up, vegetation, agriculture, and water land cover classes while the classification results were validated by the generation of 480 random points. Using the Kernel function, the study measured the location of Twitter users within a 1 km buffer from the center of the city. The results indicate that, expansion of the city (built-up land use), which is primarily driven by population expansion, has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover. In addition, social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area. The outcome of the study suggests that the combination of remote sensing, social sensing, and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro, and Africa city where data for urban planning is often unavailable, inaccurate, or stale.

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The study examines the impact of variability in rainfall characteristics on maize yield in a tropical setting. The study design involves the collection and analyses of data on rainfall characteristics and maize yield at Gboko LGA in Benue State, Nigeria. The methodology adopted is the use of archival data on rainfall and maize yield for 30 years, collected from the Agro-Meteorological Unit and Farm Department of Akperan Orshi College of Agriculture, Yandev (AOCAY). The data was analyzed using mean, correlation and regression analysis to establish cause and effect relationship between rainfall characteristics and maize yield at the study area. The result of the correlation analysis showed that rain days and rainfall amount had strong positive relationship (r = 0.747 and r = 0.599, respectively) with maize yield. It was also observed that the rainfall characteristics jointly contributed 67.4% in explaining the variations in the yield of maize per hectare. The study concludes with the development of a model for predicting maize yield in Gboko LGA. The study also recommended the application of irrigation technology, use of appropriate management practices that ensured moisture conservation and improved crop species with shorter growing periods/less moisture consumption as adaptive measures to the changing rainfall pattern within the study area.

With rising population, decline in soil productivity and land-based conflicts, the per-capita land availability for cultivation is rapidly decreasing within Benue State, a largely agrarian and smallholder setting. This study attempts a local-level support for the actualisation of Sustainable Development Goal Number 2 ("end hunger, achieve food security and improved nutrition, and promote sustainable agriculture") by 2030. Using Multi-Criteria Decision Making (MCDM) method, remote sensing data from Climate Research Unit (CRU) and in-situ data from Nigeria Meteorological Agency (NIMET) were analyzed by GIS techniques to map the suitability of rice cultivation in the study area, with the integration of Normalized Difference Vegetation Index (NDVI), land cover, slope, temperature, precipitation and soil parameters (cation exchange capacity, pH, bulk density, organic carbon). We apply the various statistical parameters that include mean spatial NDVI; correlation coefficient, standard deviation and Root Mean Square (RMS) between CRU and NIMET data. Spatial regression trend analysis is conducted between CRU precipitation and NDVI and between CRU temperature and NDVI from 1985 to 2015. The results reveal that NDVI in highly suitable rice planting regions is higher than marginally suitable regions except in the months of October and November, which shows that the highly suitable regions will yield better than the marginally suitable regions during the dry season. Additionally, NDVI is seasonally bimodal in response to precipitation, meaning that vegetation vigor is more dependent on precipitation than temperature. Finally, the correlation coefficient, standard deviation and RMS between CRU and NIMET precipitation data shows 0.42, 108, and 110, respectively, while these three factors between CRU and NIMET temperature data shows 0.88, 1.60, and 0.86, respectively. In conclusion, the MCDM approach reveals that upland is more suitable for rice cultivation in Benue State when comparing with the area provided by the Global Land Cover and National Mappings Organization (GLCNMO) data.

Sustainable urban planning is essential in mediating the natural and built environments globally, yet, there is little progress as regards its attainment in developing countries. Rapid and unplanned urbanization continue to threaten the sustainability of many cities in Africa. By selecting Morogoro Municipal Council (MMC) in Tanzania as an example, this study applied well-known remote sensing techniques to understand the dynamics of urban growth and the implications for sustainable urban planning. The study analyzes spatio-temporal characteristics for eighteen years (2000–2018) based on urban land density using gradient and grid-based analysis to further examine land use and urban land density nexus. The results indicate declining urban land densities with distance to the city center, indicating a less compact and fragmented development at the urban fringes; and northward development with limited development to the south of MCC. The knowledge and understanding of the patterns of spatio-temporal conditions, land use planning, and management interventions in MMC are necessary for addressing the inadequacies associated with rapid urbanization within the study area. On this basis, we propose a shift from the modernist to the communicative planning strategy that strongly integrates the urban social, economic, and environmental imperatives, while being adaptable to evolving realities. This plan should also aim to curtail urban sprawl and create a viable city system and economically prosperous city structure for MMC.