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Coastal erosion and accretion are major challenges that affect socio-economic activities and threatened hotel industries and fisheries which contribute 36% of the GDP in The Gambia. This research aimed to apply geospatial and remote sensing techniques in modelling erosion and accretion on the coastline of The Gambia, West Africa. Multi-temporal satellite data of the years 1989, 2009 and 2019, respectively were collected from Landsat 5 Thematic Mapper (Landsat 5 TM) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI TIRS). The collected images were pre-processed and post-processed using ERDAS Imagine 1.2 and ArcGIS 10.3 software. The Modified Normalised Difference Water Index (MNDWI) change detection algorithm was utilised on the images to classify land and water interfaces. Boolean operation classified the images into 0 and 1 as land and water. They were subsequently digitized to create vector layers. Digital Shoreline Analysis System (DSAS) was used to analyse the Spatio-temporal variations on the coastline from 1989 to 2019 and rate of erosion and accretion. The Spatio-temporal analysis for the period 1989 to 2019 found that Cell 1 had an End Point Rate (EPR) of -3.5 ± 3.0 m y−1. Cells 2, 3, 4 and 5 had EPR of -4.7 ± 3.3 m y−1. The remaining cells 5, 6, 7 and 8 shared EPR rate of -1.2 ± 1.1 m y − 1. The rate of erosion in the whole coastline peaked at -2.7 m y−1 and is dominant in Cells 1, 2, 3, 4 and 5, respectively. Accretion also in the whole coastline climaxed to more than +4.5 m y−1 and was mostly dominant in some parts of Cell 2. The inconsistent coastline migration has led to the collapse of some infrastructures in the Tourist Development Area (TDA), loss of agricultural land through salt intrusion and siltation at The Gambia Ports Authority. Hence, this research recommends soft engineering measures in the TDA to avoid disfiguring the recreational sandy beaches and hard engineering measures only between Banjul to Cape Point to protect the Banjul-Serekunda Highway.

This paper presents an introductory synthesis for mapping potential habitats of arthropod vectors responsible for animal trypanosomiasis diseases in Northern Nigeria, where there is high production of livestock. Animal trypanosomiasis is considered an arthropod-borne viral disease which is endemic in 36 countries of sub-Saharan Africa and particularly in northern Nigeria. The disease which is transmitted by the vector tsetse fly remains a threat to both humans and livestock in many rural communities of Nigeria. The outbreak of the disease is known to occur as a result of the changing climate which relates to changes in sea surface temperatures otherwise known as “El Niño Southern Oscillations” (ENSO). Trypanosomiasis is mainly experienced whenever there are changes in global precipitation as a result of the changing climate. Monthly Satellite data of Normalized Difference Vegetation Index (NDVI) at 2.5° spatial resolution was sourced from NASA-MODIS/CMD and subjected to principal component analysis using standardized principal components of GIS with a digital elevation model (DEM) supplemented in the analysis. Results revealed pockets of probable habitats of arthropod vectors to be around forest islands characterized by dry woodland and savanna, and in other cases around gallery forests and few lowland and riverine areas. This study demonstrates that geospatial technology is a cost effective tool in mapping of the arthropod vector habitats for Northern Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)