Journal of Spatial Information Sciences is a quarterly publication of the Department of Surveying and Geoinformatics, Faculty of Environmental Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. The Journal focuses on the following areas of Spatial Information Sciences: Geographic Information Sciences, Remote Sensing, Photogrammetry, Geodesy and Geodynamics, Hydrography, and hydrometry. The Journal publishes high-quality research articles and review papers in all areas of Surveying and Geoinformatics
Department of Surveying and Geoinformatics, Faculty of Environmental Sciences,Nnamdi Azikiwe University Awka, Anambra State, Nigeria
Modelling Vegetation Patterns in Southern Kaduna Nigeria using XGBoost and SHAP
Understanding the drivers of vegetation dynamics is critical for sustainable land management and ecological conservation. This study applied the XGBoost machine learning algorithm to model NDVI-based vegetation patterns in Southern Kaduna, Nigeria, using hydrological, climatic, topographic, and anthropogenic predictors within the R statistical computing environment. The model demonstrated excellent predictive performance, with training R² = 0.9978, RMSE = 0.0022, MAE = 0.0016; spatial cross-validation R² = 0.9054, RMSE = 0.0142, MAE = 0.0100; and independent testing R² = 0.9523, RMSE = 0.0101, MAE = 0.0076, highlighting its ability to capture complex nonlinear interactions. Global and local SHAP analyses revealed that NDWI, representing surface moisture and hydrological conditions, was the most influential predictor, followed by NDBI, reflecting urbanization and land-use change. Climatic variables, slope, elevation, evapotranspiration, and population density exerted secondary but meaningful effects. SHAP dependence plots further illustrated the directional relationships and interactions among predictors, confirming that vegetation greenness is highly sensitive to moisture availability and urban development pressures. These findings emphasize the dominant role of hydrological and anthropogenic factors in controlling vegetation variability in the region. The study provides actionable insights for ecological management, urban planning, and sustainable development in Southern Kaduna.
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
Machine Learning-Based Prediction and SHAP Interpretation of NO2 Concentrations in Kaduna Metropolis, Nigeria
Nitrogen dioxide (NO₂) pollution remains a major environmental concern in rapidly urbanizing cities, particularly where ground-based air quality monitoring is limited. This study modelled and interpreted the spatial distribution of NO₂ concentrations across Kaduna Metropolis, Nigeria, using a Random Forest (RF) machine learning approach. Predictor variables, including proximity to industrial sites, proximity to infrastructure, proximity to roads, NDWI, NDVI, land surface temperature, NDBAI, and NDBI, were processed and partitioned into 70% training and 30% independent testing subsets. The RF model showed strong predictive performance, with R2 values of 0.964 for training, 0.773 for spatial cross-validation, and 0.817 for independent testing, indicating good model robustness and generalization. SHAP analysis showed that proximity to industrial sites, infrastructure, and roads were the main drivers of NO₂ concentrations in Kaduna Metropolis, highlighting the strong influence of anthropogenic activities, while environmental variables such as NDWI, NDVI, LST, NDBAI, and NDBI had comparatively smaller effects. SHAP dependence plots showed that areas closer to industrial sites and roads had higher NO₂ contributions, while NDVI generally reduced predicted NO₂. LST showed a positive influence. The study offers useful evidence for air quality monitoring, industrial emission control, transport planning, and sustainable urban environmental management in Kaduna Metropolis.
A Reliability Framework for Sentinel‑2 Water‑Quality Retrieval in Optically Complex, Data‑Scarce Tropical Inland Water: Evidence from Oguta Lake (Nigeria)
A reliability-oriented Sentinel-2 workflow was implemented for Oguta Lake, Nigeria, to retrieve Coloured Dissolved Organic Matter (CDOM), turbidity, chlorophyll a (Chl-a), Secchi disk depth (SDD), and total suspended solids (TSS). Sentinel-2 MSI Level-2A imagery spanning 2020 to 2025 was processed in Google Earth Engine using cloud and shadow masking, NDWI water masking, and radiometric normalisation. Field and laboratory measurements were collected at six sites on 6 February 2025; one site (GT6) was excluded after NDWI water‑domain screening, so model calibration and LOOCV used N = 5 match-ups. Given the extreme calibration scarcity, high capacity regressors were avoided and final models were restricted to low capacity, positivity by design log linear forms, with fixed pipeline leave one out cross validation used for internal stability assessment (MAE and MdAPE). Ground-truth representativeness across optical regimes was evaluated by sampling a K-Means optical stratification (k = 8) at the sampling sites, and mapping-domain coherence was examined using Pearson and Spearman plausibility diagnostics across a stratified auxiliary time series. Results were parameter dependent. CDOM and turbidity supported cautious semi‑quantitative/relative interpretation, while SDD was best treated as relative‑only; TSS and Chl‑a remained exploratory under N = 5.
Geospatial Analysis of Flood Drivers and Flood Risk in Ayamelum Local Government Area, Anambra State, Nigeria
Flooding remains one of the most recurrent environmental hazards affecting communities, infrastructure, and agricultural activities in many parts of southeastern Nigeria. Ayamelum Local Government Area of Anambra State experiences frequent flood events due to its low-lying terrain, extensive drainage networks, and floodplain characteristics. This study employed geospatial techniques to analyze the drivers of flooding and assess flood risk within the study area. Five flood-influencing factors, namely elevation, slope, flow accumulation, proximity to natural drainage channels, and watershed characteristics, were derived from a 30 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model. The factors were standardized using fuzzy membership functions to establish a common suitability scale and subsequently weighted using the Entropy Weighting Method to determine their relative contributions to flood occurrence. The weighted factors were integrated using a Weighted Linear Combination approach to generate a Flood Risk Index and delineate flood risk zones. The results showed that flow accumulation (0.313), proximity to drainage channels (0.267), and elevation (0.231) were the most influential flood drivers, while slope (0.145) and watershed characteristics (0.044) exerted relatively lower influence. Flood risk assessment revealed that 422.92 km² (72.26%) of the study area falls within the Very Low Risk category, 74.91 km² (12.80%) within the Low Risk category, 70.08 km² (11.97%) within the Moderate Risk category, and 17.40 km² (2.97%) within the High Risk category. High-risk zones were concentrated mainly within Umuerum and Anaku wards, while moderate-risk conditions extended across Omor 1, Omor 2, Omor 3, Umuerum, Igbakwu, and Anaku wards. The study demonstrates that flood susceptibility in Ayamelum Local Government Area is controlled largely by the interaction of topographic and hydrological factors. The resulting flood risk map provides a valuable decision-support tool for flood management, land-use planning, disaster preparedness, and sustainable environmental management within the study area.
Spatial Analysis of Health Care Facilities for Equitable Access: A Case Study of Abakaliki Local Government Area, Ebonyi State
This study presents the development of a comprehensive geospatial database and digital mapping of healthcare facilities within Abakaliki Local Government Area, Ebonyi State, utilizing Geographic Information System (GIS) techniques. Primary geospatial data were collected through Global Positioning System (GPS) field surveys, complemented by secondary data obtained from the archival resources of the GIS department within the Office of the Surveyor General of Ebonyi State. Spatial data processing and analysis were conducted using QGIS version 2.4.0. The spatial analysis of health care facilities in Abakaliki Local Government Area revealed significant disparities in their distribution, underscoring challenges in equitable access to healthcare services across the metropolis. A total of 151 health care facilities were identified and categorized into clinics (130), hospitals (8), primary health centers (7), and health laboratories (6). This composition highlights a predominance of clinics with relatively fewer hospitals and other specialized facilities, reflecting the general structure of health service provision in the area. These findings highlight critical gaps in healthcare accessibility, underscoring the need for strategic planning and policy interventions aimed at enhancing the spatial equity of healthcare service delivery within Abakaliki LGA.
Assessment of Flood Vulnerability Zones in Esan South East LGA, Edo State, Nigeria
Flooding remains a major environmental hazard that threatens lives, property, infrastructure, and agricultural activities in many parts of Nigeria. Effective flood risk management requires the identification and mapping of areas that are susceptible to flooding in order to support planning and mitigation efforts. This study assessed flood vulnerability zones in Esan South East Local Government Area, Edo State, Nigeria, using geospatial techniques and multi-criteria decision analysis. The specific objectives were to delineate the spatial distribution of flood-influencing factors, standardize the factors using fuzzy membership functions, determine factor weights through Principal Component Analysis (PCA), and generate a flood vulnerability map using the Weighted Linear Combination (WLC) approach. Five flood-conditioning factors comprising elevation, slope, proximity to drainage networks, proximity to water bodies, and watershed characteristics were derived from geospatial datasets. The factors were standardized using fuzzy membership functions and weighted using PCA before integration through WLC. The resulting flood vulnerability map was classified into Very Low, Low, Moderate, High, and Very High Vulnerability categories. The results showed that proximity to water bodies (22.27%) exerted the greatest influence on flood vulnerability, followed by elevation (20.28%), proximity to drainage networks (20.23%), slope (20.05%), and watershed characteristics (17.17%). The flood vulnerability assessment revealed that 30.69%, 27.22%, 19.07%, 6.33%, and 16.68% of the study area fall within the Very Low, Low, Moderate, High, and Very High Vulnerability classes respectively. Settlement vulnerability analysis identified 49 settlements located within High and Very High Vulnerability zones, with communities such as Asaboro Quarters, Omo Quarters II, Willy Quarters, Udeze Quarters, Ifianyi, Udenka Quarters, Okperu Quarters, Nana Quarters, Emmanuel Quarters, Ekuale Quarters, Odiotor, and Erobobo among those most exposed to flood hazards. The concentration of vulnerable settlements within Emu, Oria, Illushi, and Ewohimii wards highlights the potential threat of flooding to human settlements and socio-economic activities. The study demonstrates the effectiveness of integrating GIS, fuzzy logic, PCA, and WLC techniques for flood vulnerability assessment and provides a scientific basis for flood risk management, land-use planning, and disaster preparedness within Esan South East Local Government Area.
Integrated GIS-AHP and Hydrogeophysical Approaches for Groundwater Potential Mapping in Akure Metropolis, Ondo State, Nigeria
Groundwater remains the principal source of potable water for domestic, agricultural, and industrial activities in many parts of Nigeria. However, increasing population growth, urban expansion, and climate variability have intensified pressure on groundwater resources, necessitating accurate assessment of groundwater potential zones for sustainable water resource management. This study integrated Remote Sensing (RS), Geographic Information Systems (GIS), the Analytical Hierarchy Process (AHP), and Vertical Electrical Sounding (VES) techniques to delineate groundwater potential zones in Akure Metropolis, Ondo State, southwestern Nigeria. Eight groundwater conditioning factors comprising geology, lineament density, land use/land cover, drainage density, soil, rainfall, slope, and elevation were selected and weighted using the AHP multi-criteria decision-making approach. The thematic layers were integrated through weighted overlay analysis to generate a groundwater potential map. Furthermore, forty-one (41) Vertical Electrical Sounding (VES) stations were conducted across the study area to characterize subsurface hydrogeological conditions using parameters such as overburden thickness, aquifer thickness, aquifer resistivity, bedrock relief, hydraulic conductivity, transmissivity, transverse resistance, and longitudinal conductance. The VES-derived groundwater potential map was subsequently integrated with the GIS-AHP model to improve prediction reliability and reduce uncertainty associated with surface-based analyses.The integration resulted in the classification of the study area into five groundwater potential zones: very low, low, moderate, high, and very high. Moderate potential zones covered the largest area—52.08%, high-26.36%, low-18.78%, very high-1.66% and the very low-1.12%. Areas with high to very high groundwater potential were predominantly concentrated in the northeastern and eastern parts of the study area, particularly around Ogbese and Ita-Ogbolu, where favourable geological formations, high lineament density, and enhanced aquifer characteristics occur. Conversely, low to very low groundwater potential zones were mainly located in the southwestern parts of the study area where unfavourable hydrogeological conditions prevail.The integration of GIS-AHP and hydrogeophysical data demonstrated strong spatial agreement between predicted groundwater potential zones and subsurface aquifer characteristics obtained from VES interpretation. The study highlights the effectiveness of integrating geospatial and hydrogeophysical techniques for groundwater exploration, borehole siting, and sustainable groundwater resource management in crystalline basement terrains.
Agricultural Land Suitability Mapping in Akwa Ibom State, Nigeria using Cloud-Based Remote Sensing and GIS
Rapid population growth and concurrent urban expansion in Nigeria have placed unprecedented pressure on arable land, necessitating precise, data-driven geospatial evaluations to secure regional food supplies. This study integrates cloud-based remote sensing and Geographic Information System (GIS) technologies to map agricultural suitability zones in Akwa Ibom State, Nigeria. Google Earth Engine (GEE) and ArcGIS 10.8 were employed for land use/land cover classification using Sentinel-2 imagery, factor weight determination via the Analytical Hierarchy Process (AHP), and weighted overlay analysis. Five biophysical factors were assessed: soil properties, climate, topography, land use/land cover, and Normalized Difference Vegetation Index (NDVI) using data spanning 2014-2024 from ISRIC SoilGrids, ALOS PALSAR DEM, CHIRPS, and ERA5. AHP assigned weights of soil properties (51.8%), climate (26.5%), topography (10.4%), land use/land cover (7.9%), and NDVI (3.4%). Results indicate 14.89% (1,002.60 km²) is very highly suitable and 28.19% (1,898.14 km²) is highly suitable for agriculture, with Nsit Ubium and Uruan Local Government Areas showing the highest suitability. Conversely, 8.96% (603.31 km²) and 17.03% (1,146.66 km²) were classified as very low and low suitability. Ground-truthing via soil sampling validated the map, confirming superior soil characteristics in high-suitability zones. The study demonstrates the efficacy of cloud-based geospatial technologies for data-driven agricultural planning and provides a replicable framework for sustainable land-use management.
Hydrogeophysical Validation of GIS-Based Groundwater Potential Mapping in Lagos Island, Nigeria
In rapidly urbanizing settings where public water supply systems are inadequate, stream or surface water has emerged as an important freshwater resource. The study combined the use of Geographic Information Systems (GIS), Remote Sensing (RS) and Vertical Electrical Sounding (VES) techniques to map and validate groundwater potential in Lagos Island, Nigeria. Six groundwater conditioning factors were prepared and integrated: geology, slope, rainfall, soil, drainage density, and lineament density, which were all done in a GIS environment using the Analytical Hierarchy Process (AHP) and weighted overlay analysis. To assess the subsurface aquifer conditions and to confirm the groundwater potential zones derived from GIS, Hydrogeophysical (Schlumberger electrode configuration) studies were done. The groundwater potential map produced revealed the study area to be under high, moderate and low groundwater potential with 78, 21 and 1 per cent of the area respectively. Overburden thickness, as interpreted from VES, ranged from 14 m to 46 m while the basement resistivity values were low, suggestive of fractured and water-bearing formations. A good agreement was found between the groundwater conditions obtained from the GIS-AHP results and VES results, which shows that the combination of the geospatial and the hydrogeophysical methods in groundwater exploration is reliable. The study emphasizes the benefits of hydrogeophysical validation to enhance the groundwater potential evaluation in a coastal urban setting. Recommendations for future groundwater exploration programmes include hydrogeophysical validation, groundwater monitoring systems and more field-based datasets to improve sustainable groundwater resource management in Lagos Island and other coastal urban settings.
Forecasting Long-Term Urban Growth Patterns and Drivers in West African Secondary Cities: A Case Study of Uyo, Nigeria (2025–2050)
Secondary cities in West Africa are the new frontiers of global urbanization, yet they often lack the long-term spatial foresight needed for sustainable growth. In Uyo, Nigeria, rapid demographic shifts are putting unprecedented pressure on land resources, necessitating a shift toward predictive urban modeling. This study utilizes an integrated MLP-Markov chain model to simulate urban growth for the years 2035 and 2050. A Logistic Regression model was first employed to identify spatial drivers, revealing that proximity to major roads and elevation were the primary determinants of expansion. The resulting projections were then evaluated using the SDG 11.3.1 (Land Use Efficiency) framework, comparing projected built-up expansion against a UN-validated population growth rate of 4.32%. The simulation predicts that Built-up areas will expand to 106.58 km² (58.06%) by 2035 and 118.47 km² (64.54%) by 2050. The LCRPGR ratio is projected to drop to 0.20 by 2050, indicating that while land use is "efficient" in per-capita terms, the city is facing extreme hyper-densification. The findings serve as a spatial early-warning system, advocating for a transition from horizontal sprawl management to inclusive vertical densification and the preservation of remaining ecological corridors to ensure Uyo’s long-term resilience.
Analysis of the Accuracies of CSRS-PPP and AUSPOS Online Post-Processing Services
The static mode of point positioning requires post-processing, which can be done automatically using some of the free online post-processing services. It is, therefore, important to evaluate the accuracy of coordinate solutions provided by these services, determine the optimal observation duration to achieve specific accuracy levels, identify when longer observation durations yield less accuracy improvement, and compare the overall accuracy performance of online post-processing services. In this study, the accuracies of two free online post-processing services (CSRS-PPP and AUSPOS) were tested using data acquired at three Continuously Operating Reference Stations (CORS) located at Asaba, Benin and Warri, all located in Southern Nigeria. The acquired 8 hours Global Navigation Satellite System (GNSS) observation data recorded in RINEX (Receiver Independent Exchange) format version 3.05 were decimated into shorter sessions of (15 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, and 6 hours). The findings show that both services produced centimetre-level horizontal accuracy. However, CSRS-PPP achieved 10 cm accuracy within 15 minutes compared to 1 hour with AUSPOS, but the rate of accuracy improvement with both services decreased significantly after 2-3 hours. Also, from 14 performance evaluation criteria at each of the three stations totaling 42 total comparisons used in the study, CSRS-PPP won 29 out of 42 (69%). This study provides important practical information for geospatial professionals on which service offers better accuracy and which works faster.
Hybrid CNN–MLP Deep Learning Framework for Urban Land Cover Mapping in Heterogeneous Tropical Landscapes
Rapid urbanization in sub-Saharan Africa presents significant challenges for sustainable land management, particularly in tropical landscapes where spectral similarities between built-up areas and bare soil often lead to classification inaccuracies. While Convolutional Neural Networks (CNNs) have shown promise in remote sensing, they often struggle to resolve 'spectral confusion', a phenomenon where distinct land covers exhibit identical spectral signatures, because they prioritize spatial feature extraction over the non-linear spectral relationships required for class discrimination. To overcome these limitations, this study proposed a Hybrid Deep Learning framework that integrates a Multi-Layer Perceptron (MLP) with a CNN, combined with expert-driven spatial feature engineering. Using multi-temporal Landsat imagery (2000–2025) for Uyo Metropolis, Nigeria, the study developed a dual-stream architecture that fuses raw spectral bands with Center-Versus-Neighbors (CVN) texture descriptors and the Normalized Difference Vegetation Index (NDVI). The empirical findings demonstrated that the hybrid framework significantly outperforms traditional baseline classifiers (Random Forest), achieving a validated Overall Accuracy of 96.17% and a Kappa Coefficient of 0.94, calculated across the independent test set. The inclusion of spatial texture successfully resolved spectral confusion, reducing misclassification of "Bare Land" as "Built-up" by 22%. Spatiotemporal analysis revealed a 158.10% increase in the urban footprint over the 25-year period, with the most aggressive expansion (3.95 km²/year) occurring between 2020 and 2025. The study concluded that hybrid architectures are essential for mapping heterogeneous tropical environments. The proposed framework offers a scalable, high-precision, and low-cost tool for monitoring urban sprawl in secondary African cities, providing a robust empirical foundation for achieving Sustainable Development Goal (SDG) 11 targets.
Geographic Factors Enhancing Banditry and its Impact on Educational Services in Rafi Local Government Area, Niger State, Nigeria (2020–2026)
This study examined the geographic factors that enhance banditry and their impact on educational services in Rafi Local Government Area of Niger State, Nigeria, between 2020 and 2026. The study adopted a descriptive survey research design complemented by Geographic Information System (GIS) and Normalized Difference Vegetation Index (NDVI) spatial analysis techniques. The population of the study was 320,000, comprising teachers, students, parents, community leaders, and security personnel. A sample size of 384 was determined using the Krejcie and Morgan Table. The instrument used for data collection was a questionnaire that was validated by experts and pilot-tested with 50 respondents from Tegina. The reliability of the instrument was determined using Cronbach’s Alpha, which yielded a reliability coefficient of 0.78. Frequency counts were used for data analysis to answer the research questions. The study also utilized a Normalized Difference Vegetation Index (NDVI) map of Rafi Local Government Area to depict vegetation density and identify potential areas favourable to bandit activities. Geographic Information System (GIS) technology was used to map the spatial distribution of terrain within the identified banditry hotspot areas in Rafi LGA, Niger State. The findings revealed that dense forests, poor road networks, and porous community boundaries significantly enhance banditry in Rafi LGA, whereas remote settlements and illegal mining/rural poverty were not identified as major contributing factors. The study also found that banditry negatively affects educational services through increased fear among teachers and students, teacher transfers, reduced student attendance, disruption of teaching and learning activities, and the withdrawal of children from school by parents. The study concluded that geographic factors significantly enhance banditry and undermine educational development. The study recommends that Government and security agencies should increase the deployment of security personnel in identified banditry hotspot areas such as Tegina, Pandogari, and Yakila in order to improve surveillance and rapid response to attacks.
A Geospatial Approach to Mapping Municipal Solid Waste Disposal Patterns: A Case Study of Uyo Metropolis, Nigeria
Rapid urbanization in Nigerian cities like Uyo Metropolis has exacerbated municipal solid waste (MSW) management challenges, leading to widespread illegal dumping, environmental degradation, and public health risks. This study attempts to simulate waste disposal patterns in Uyo Metropolis of Akwa Ibom State, Nigeria, using geospatial. Sentinel-2 satellite image of 2026 was acquired and processed in Google Earth Engine (GEE) to compute spectral indices. Spatial analyses included distribution mapping, multi-ring buffering (300 m, 500 m, 800 m), Hotspot analysis (1,000 m threshold), and kernel density estimation (1,500 m radius), with overlays on reclassified indices. Results revealed a non-random, road-centric distribution, with dumpsites clustering along key arteries like Ikot Ekpene, Oron, Nwaniba, and Abak Roads, forming linear and nodal patterns in the urban core. The trend reveals solid waste disposal occurring in highly populated areas with heavy concentration of human activities. High-confidence hotspots (95–99%) were concentrated centrally, aligning with high/very high Urban Index classes and high/very high NDBaI bareness zones. These indicate that accessibility, impervious surfaces, and land exposure as key drivers of indiscriminate dumping system. The application of geospatial framework offers a cost-effective tool for prioritizing interventions to mitigate illegal dumping and promote resilient urban planning.
Spatio-Temporal Analysis of Tuberculosis in Anambra State, Nigeria Using GIS: Emerging Urban Clusters and the Role of Built-Up Environment (2013–2023)
Tuberculosis (TB) remains a significant public health challenge in Nigeria, with transmission dynamics increasingly influenced by rapid urbanization and high human mobility. This study investigated the spatio-temporal trends of TB transmission across the 21 Local Government Areas (LGAs) of Anambra State from 2013 to 2023, focusing on the emergence of TB clusters and their association with urban development. Retrospective data were analyzed using Geographic Information Systems (GIS), incorporating TB case notifications, population projections, and Land Use/Land Cover (LULC) changes. Spatial cluster analysis was performed using Global Moran’s I, Local Indicators of Spatial Association (LISA), and Getis-Ord statistics, while multivariate regression assessed the impact of urbanization indicators. The study found that the Case Notification Rate (CNR) rose dramatically from 38.1 per 100,000 in 2013 to 183.7 per 100,000 in 2023, representing a 382.2% increase. The spatial distribution of TB shifted from a random pattern between 2013 and 2020 (p > 0.05) to a significantly clustered pattern from 2021 onward (Moran’s I = 0.208, p = 0.028), with hotspots concentrated in the heavily built-up and commercial LGAs of Onitsha North, Onitsha South, Awka South, and Nnewi North, as well as the rapidly expanding Onitsha–Idemili axis (including Obosi and Nkpor). Correlation analysis revealed that built-up area is moderately positively correlated with TB rate (r = 0.333), while population density shows a weaker positive relationship (r = 0.143). The multivariate regression model explained approximately 12.1% of the variation in TB incidence (R2 = 0.121), identifying built-up area (beta = +0.320) as a stronger predictor than population density (beta = +0.100). These findings suggest that TB transmission is primarily driven by the structural and functional intensity of the urban environment; specifically high-mobility commercial hubs and transit axis, rather than raw population size alone. This highlights the need for public health interventions to target high-activity urban centers and commercial hotspots to effectively curb the TB burden in Anambra State.
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
Assessment of the Effect of Climatic Variables on Vegetation in North Central, Nigeria
This study assessed the effects of temperature and precipitation on vegetation dynamics in North-Central Nigeria from 2000 to 2020. The study integrated Landsat-derived Normalised Difference Vegetation Index (NDVI), MODIS Land Surface Temperature (LST), and CHIRPS precipitation datasets. Google Earth Engine was used for image preprocessing and annual compositing, while ArcGIS 10.8 was employed for spatial harmonisation, projection standardisation, and raster-to-point conversion. Ordinary Least Squares (OLS) regression, Pearson correlation, partial correlation, Multiple Linear Regression (MLR), and residual analysis were applied to quantify climate–vegetation relationships and distinguish climate-driven change from anthropogenic influence. Mean NDVI exhibited a marginal positive trend (β = 0.0018 yr⁻¹), suggesting regional vegetation stability. However, spatial disaggregation revealed that 42.7% of the area experienced greening while 18.8% showed degradation, particularly around urbanising centres, mining corridors, and intensively cultivated landscapes. Temperature increased persistently at +0.0976 °C yr⁻¹, while rainfall showed a weak declining tendency (−0.1474 mm yr⁻¹) with strong interannual variability. Correlation analysis revealed a stronger negative NDVI–temperature relationship (r = −0.4925) than NDVI–precipitation (r = 0.2699). Residual analysis identified negative clusters around Abuja, Jos, and Lokoja, indicating anthropogenic pressure beyond climatic expectations. Vegetation dynamics in North-Central Nigeria are governed by the combined effects of rising temperature, rainfall variability, and human disturbance. Thermal intensification constitutes the dominant climatic constraint. The study recommends climate-responsive land-use planning, afforestation, soil–water conservation, and routine geospatial monitoring to enhance ecosystem resilience in the Guinea Savannah transition belt.
Comparative Assessment of Pixel-Based and Object-Oriented Classification Techniques using Sentinel-2 Imagery of the Federal University of Technology, Akure (FUTA) Campus
Precise classification of land cover is essential for effective environmental and urban planning, particularly in diverse landscapes with intricate spatial patterns. This study offers a comparative evaluation of pixel-based and object-oriented image classification techniques using Sentinel-2 satellite imagery of the Federal University of Technology, Akure (FUTA), Nigeria. The pixel-based classification applied the Maximum Likelihood Classification (MLC) method, which depended exclusively on spectral data, while the object-oriented approach integrated multi-resolution segmentation and contextual features such as shape and texture. Ground truth data were gathered from thirty (30) georeferenced locations using a mobile GPS for validation. Results show that while using the pixel-based method, the vegetation covers 2.515 km² (37%), compared to 2.266 km² (33%) from object-oriented classification; Farmland accounts for 1.917 km² (28%) versus 1.803 km² (27%); Bare Ground is recorded at 1.206 km² (18%) as opposed to 1.232 km² (18%); and Built-up is measured at 1.161 km² (17%) compared to 1.496 km² (22%) from the pixel-based classification. Accuracy assessments using confusion matrices revealed that the object-oriented method outperformed the pixel-based method, achieving an overall accuracy of 90% with a Kappa coefficient of 0.8663, compared to 80% accuracy for the pixel-based method. The object-oriented classification proved more effective in distinguishing built-up and bare ground areas, while both methods performed similarly in classifying vegetation. This study concludes that object-oriented classification is preferable for complex and urban environments where accuracy is critical. Expanding ground-truth data beyond thirty points and employing higher-resolution imagery would further enhance classification reliability and precision.
Landslide Susceptibility Mapping Using GIS-Based Multicriteria Decision Analysis: A Case Study of Lokoja, Nigeria
Located between rivers and hills, Lokoja’s development is advancing into hilly areas due to limited flat and safe land without recourse to the stability of the hills. The study aims to assess the structural stability of hilly areas through the production of landslide susceptibility maps using both a Geographic Information System and an analytical hierarchy process (AHP) model. Twelve data layers are exploited as the conditioning factors to detect the most susceptible areas. These factors include slope, aspect, plan curvature, land use and land cover, lithology, topographic wetness index, stream power index, distance to road, distance to stream, slope length, and fault. Subsequently, a Landslide susceptibility map was produced using a precise and conclusive weighted-overlay analysis of the AHP pair-wise evaluation methodologies to evaluate the effects of the conditioning factors. Results indicate that 5,703 hectares (32.34%) of the study area are ‘not susceptible’, 7,881 hectares (44.69%) are ‘moderately susceptible’, and 4,051 hectares (22.97%) are ‘highly susceptible’ to landslides. The study concludes that the susceptibility model is reliable for future decision-making, urban planning, and development in Lokoja, Kogi State, to reduce the risk of landslides in the region.
Identification and Mapping of Urban Road Networks Using Support Vector Machine Classification with Unmanned Aerial Vehicle in Benin City, Nigeria
Urban expansion has created significant challenges in transportation planning, infrastructure management, and spatial analysis, particularly in developing cities. This study explores the application of Support Vector Machine (SVM) for the identification and mapping of urban road networks from high-resolution remotely sensed UAV imagery in parts of Benin City, Nigeria. The SVM algorithm, trained using a Radial Basis Function (RBF) kernel, was developed to classify road features from UAV images. Results demonstrated that SVM provided high classification accuracy and effectively distinguished road features from surrounding land uses, confirming its potential for scalable urban mapping solutions. The results were validated using reference data thereby yielding an overall classification accuracy of 89.7% with particularly robust performance in delineating major thoroughfares, clearly visible in the eastern portion of the study area where the primary arterial road exhibits clean, continuous vectorization and a Kappa coefficient of 0.85. The clear delineation of the primary road network (width > 8m) achieves 93.4% accuracy (σ = 2.1), while the more fragmented appearance of secondary roads in the central portion of the image corresponds to the lower accuracy rate of 85.7% (σ = 3.2). The mapped road network demonstrated strong correspondence with existing road infrastructure, including both major and minor roads, highlighting the capability of the SVM approach in delineating complex urban features. This study demonstrates the applicability of machine learning techniques in urban road mapping and provides a cost-effective, scalable solution for updating spatial road databases, particularly in developing urban environments with limited geospatial resources. The findings support informed urban planning and infrastructure development initiatives in Benin City and similar urban centers across sub-Saharan Africa.
Integrating Fuzzy Multi-Criteria Decision-Making and GIS for Sustainable Landfill Site Selection in Rapidly Urbanizing Ilorin City, Kwara State, Nigeria
An effective landfill site is essential for sustainable solid waste management, particularly in urban areas experiencing rapid population growth. This study develops a decision support framework for landfill site selection by applying Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and weighted overlay techniques to evaluate potential landfill locations based on various factors. The study area covered Ilorin and its environs in Kwara State, Nigeria. The Fuzzy AHP was applied to determine the weights of relevant criteria, accounting for uncertainty in expert judgment. The weighted overlay technique was then used to integrate the various input criteria maps according to their assigned weights, thereby producing a landfill suitability map for the entire study area. The results of the findings show that 5% and 4% of the study area are unsuitable and less suitable, respectively, while 28% are moderately suitable, and 51% are suitable. On the other hand, 13% of the study area is highly ideal for landfill siting. The output suitability map was validated with two dumpsites. The old and decommissioned dumpsite at Ita Amo, which falls entirely within an unsuitable area, and the currently used site at Ago Aiyekale span both moderately suitable and suitable areas.
Comparative Evaluation of GNSS Positioning Accuracy Using RTK Techniques and CORS-Based Post-Processing Solutions
Global Navigation Satellite System (GNSS) applications in Osun State, Nigeria, are limited by poor positional accuracy, particularly in vertical measurements. With the recent establishment of a Continuously Operating Reference Station (CORS) by private surveyors, it is important to assess the performance of real-time and post-processed GNSS solutions for local survey practice. This study compared two real-time kinematic (RTK) connection methods: radio frequency (RF) and network RTK via internet (NRTK) with static precise positioning using Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) and Osun CORS RINEX data processed in South Geomatics Office software. Field data were collected on three control stations with a Tersus Oscar GNSS receiver, and accuracy was evaluated using root mean square error (RMSE) against published control coordinates. Results show that CSRS-PPP (0.11 m easting, 0.17 m northing, 3.78 m height) and Osun CORS (0.09 m easting, 0.11 m northing, 3.76 m height) achieved horizontal accuracies within the allowable 0.05-0.50 m limit, but vertical errors exceeded the 0.10 m tolerance. NRTK (0.06 m easting, 0.03 m northing) performed better than RF-RTK (0.53 m easting, 1.29 m northing) for horizontal positioning, yet both produced unacceptable vertical errors of 24.14 m and 8.54 m, respectively. The findings confirm that online PPP and internet-based RTK provide reliable horizontal accuracy in Osun State, but vertical accuracy remains inadequate and requires further methodological improvement.
Urban Flood Mapping using Sentinel-1 SAR Data and Machine Learning: A Case of Maiduguri, Nigeria
Flooding is one of the most devastating hydrological hazards, resulting in significant human and economic losses, particularly in rapidly urbanizing areas with poor urban planning. Recently, Maiduguri, Nigeria, faces recurrent floods, and traditional flood mapping methods relying on optical remote sensed data are often hindered by cloud cover. This study leverages Sentinel-1 Synthetic Aperture Radar (SAR) data and machine learning to overcome these limitations. The research employs threshold-based classification and change detection techniques to analyze pre-flood (January–August 2024) and post-flood (September–October 2024) SAR imagery. The methodology includes radiometric calibration, speckle filtering, and terrain correction to enhance flood detection accuracy. Flood extents were validated using ground reference points, achieving an overall accuracy of 88.6% and a Kappa coefficient of 0.82, confirming the reliability of SAR-derived flood maps. Findings reveal a 524.6 km² of inundated area, with severity concentrated along River Ngadda and low-lying areas. Zonal analysis highlights significant disparities, with the Maiduguri-Monguru Road experiencing 27.98% flooding, while elevated areas like Bornu Industrial Park (0.017%) remained minimally affected. The study also reveals that critical infrastructure are at risk. The research recommends improved urban drainage system, stricter land-use regulations and early warning systems to mitigate flood vulnerability.
Geographic Information System-Based Multi-Criteria Decision Analysis of Flood Risk Zones in Madagali, Adamawa State, Nigeria
The main purpose of this research was to apply geospatial methods to map flood risk zones within Madagali Local Government Area in Adamawa State of Nigeria towards developing enhanced measures for flood risk mitigation. Core variables assessed included topographic elevation, land use/land cover dynamics, rainfall variability, and characteristics of the drainage network. The study combined Landsat 8 satellite images, Digital Elevation Models (DEMs), and rainfall data using Multi-Criteria Decision Analysis (MCDA) with a weighted overlay method to assess flood susceptibility in the study area. The results indicate that approximately 35% of the study area is situated within high to very high flood risk categories, predominantly low-lying areas subject to intensive land-use patterns and variability in rainfall distributions. The vulnerability assessments undertaken in this study revealed distinct classifications such as: 18.53% of areas exhibit low vulnerability, 30.50% moderate vulnerability, 52.8% high vulnerability, and 60.7% very high vulnerability. In conclusion, the geospatial methods applied in this study provide evidence-based insights for policymakers thereby, supporting effective flood risk management and enhancing community resilience within Madagali Local Government Area. Key recommendations emerging from this study underscore the need for stakeholders to pursue afforestation interventions, river channel maintenance, development of contextually appropriate hydraulic infrastructure, and drainage infrastructure near Madara Mountain which can serve dual purposes of flood mitigation and dry-season agricultural water storage.
Copyright (c) 2025 E. S. Ebinne, M. K. Adegoke, U. C. Nkwunonwo, J. A. Ogbodo https://creativecommons.org/licenses/by-sa/4.0
Predictive Modelling of Water Surface Elevation Variations Using Multi-Mission Radar Altimetry in the Bayelsa River, Nigeria
Flooding in Nigeria’s Niger Delta poses severe risks to lives and infrastructure, yet hydrometric data remain sparse. This study develops a predictive framework for water surface elevation (WSE) in the Bayelsa River by combining multi-mission radar altimetry with Artificial Neural Networks (ANN). Altimetric data from Jason-2, SARAL/AltiKa, and Sentinel-3 (for year 2008 to 2015) were preprocessed, corrected and merged into consistent WSE time series. Regression models revealed seasonal dependencies but achieved moderate accuracy (R² ≈ 0.41–0.89). By contrast, the ANN (39-20-1 feed-forward backpropagation) significantly improved prediction skill, attaining R² = 0.91, RMSE = 0.29 m, and MAE = 0.24 m against gauge observations. The ANN successfully captured double-peak seasonal hydrographs driven by bimodal rainfall, outperforming traditional statistical approaches. Findings confirm the potential of integrating radar altimetry and machine learning for flood forecasting in data-poor river basins. The framework offers a scalable, cost-effective solution for early warning systems and can be extended to other African basins and future SWOT mission datasets.
Geometric Integrity Assessment of Petroleum Storage Tank using Total Station Technology in Compliance with API 653 Standards
This study presents a comprehensive inspection report on a storage tank (B1) at the CNR base in Abidjan, Ivory Coast. Utilizing Total Station surveying techniques with a Kolida reflectorless total station. The investigation focused on key parameters such as base settlement, roundness (ovality), verticality, and base level assessments in compliance with API 653 standards. This study focuses on the geometric integrity assessment of petroleum storage tanks using Kolida KTS 442 reflectorless total station technology, with the objectives of : Present a comprehensive measurement workflow aligned with API 653, demonstrate how total station–derived data supports detection of deformation parameters—such as settlement, out-of-roundness, and verticality deviations and evaluate compliance with API 653 tolerance limits . The inspection methodology involved the use of a total station instrument and its assessories. Temporary control points were established within the tank premises on which Total station was set up. The circumferential level of the tank base was determined for nine(9) points at a marked interval of 2.5meters from each point. Two different set of readings were determined for check purpose. The ovality inspection was carried out by coordinating the nine (9) points marked around the circumference with 2.5m intervals and also coordinating randomly smaller intervals between the 2.5m marks for better circumference. The instrument was used to generate the coordinates of at least one point each from the five couches of the tank vertically upwards. The base level of eight points was taken round the tank B1 circumference. The difference between the highest and lowest base level reading for tank B1 was 3cm. With the mean vertical angle of the four points taken in tank B1, only one point has a high value of 000 02’ 46.46” which is quite different from all other values and this made the mean deflection angle to get to 000 01’ 41.46”. The tank B1 was found to be within allowable deviation. The verticality for four point on the tank had an excess of 1st = 0° 0' 59.42", 2nd = 0° 0' 24.28", 3rd = 0° 1' 41.48", 4th = 0° 0' 43.14" which is within the allowable standard. Total station inspection of storage tank cannot be said to be complete when there is no available initial or subsequent data of the tank under inspection which will serve as basis of comparison for any deviation. Hence it was recommend that a search be made for previous records and all inspection reports be kept safe for ease of access in case of subsequent inspection.
Assessment of Deforestation in Bali Local Government Area of Taraba State, Nigeria, using Geospatial Techniques
Deforestation is one of the leading environmental threat which causes a lot of negative impact to the environment resulting from excessive logging of trees, population growth and urbanization, bush fire, agricultural expansion and traditional medicine, lumbering and overgrazing for economic and other uses. The research aimed at assessing the level of deforestation at different epochs in the study area. It was achieved by performing Land use/ Land Cover for multiple time period using maximum likelihood algorithm. It also determined the rate of changes that occurred during the epoch, and it also assessed the level of deforesting. The methodology involves acquiring satellite imageries for multiple time periods, thus Landsat 7 ETM of 2010 and Landsat 8 OLI of 2015, 2020 and 2024 were employed. The satellite imageries were processed in ArcGIS, Land use Land Cover Classes were classified in to six different land cover classes, thus water Bodies, less dense vegetation, dense vegetation, built up areas, bare surface area and others. Image classification was used to detect alteration in land cover types. Area for land cover classes for the various epoch, 2010, 2015, 2020 and 2024 and percentage changes were obtained. Results obtained shows loss of vegetation cover by -63% from 2010 to 2024, while built up area, bare land, others were increasing as a result of population expansion. The loss of vegetation cover was as a result of urbanization, economic and agricultural expansion as a result of increase in population. It provided valuable insights into the dynamics of deforestation in the study area and recommended the integration of GIS and remote sensing technique to be employed to determine the causes of deforestation, its effect and how to mitigate it for sustainable environmental development.
Terrain Analysis Along Major Roads in University of Lagos Campus
Investigation into surface level is an exercise that provides the required data for lasting solutions to environmental and engineering problems. Flooding is a major environmental problem that causes unquantifiable damages and requires engineering solutions. University of Lagos is located close to the Lagoon making many areas within the University to be susceptible to flooding. Investigation into the surface level on the major roads of the University of Lagos will provide information for a better understanding of flooding on campus. This study, therefore, carried out the terrain analysis of major roads within the University of Lagos to identifying flood prone areas along the road. Automatic level was used in a levelling exercise along the roads. Observations were taking at every 25 meter intervals at the two sides of the roads as well as the center lines of the roads. Observed data were processed and accuracy obtained agrees with a second order levelling. Results were presented in longitudinal sections using the central road data observations. Contour lines and flow direction map were also produced using both center line and road side observations. A Digital Terrain Model of the major roads was produced and a 3D wireframe that shows the terrain formation along the major roads. Analysis of the results revealed flood prone spots. It was concluded that another study of the entire University of Lagos as well as the analysis of the drainage system in the immediate vicinity of the campus is required to proffer a lasting solution to flooding on the campus.
Comparative Analysis of Artificial Neural Networks and Regression Models for Water Level Prediction Using Radar Altimetry in the Upper Niger River
The upper Niger River Basin experiences floodings and remains a recurring disaster, yet existing hydrological monitoring systems are limited by sparse in-situ observations and insufficient predictive accuracy. Traditional regression-based models struggle to capture the nonlinear dynamics of river systems, creating an urgent need for advanced approaches such as radar altimetry integrated with Artificial Neural Networks to improve water level prediction and flood early warning. This study explores the deployment of satellite radar altimetry for predictive modelling of water surface elevation in the Upper Niger River Basin, Nigeria. Satellite altimetry datasets from Jason-2, Sentinel-3, and SARAL/AltiKa, spanning a seven-year period, were integrated with in-situ hydrological measurements to evaluate their effectiveness in water level monitoring and flood forecasting. Artificial Neural Networks (ANN) and regression models were comparatively assessed to determine their predictive accuracy. Results showed that the ANN model consistently outperformed regression analysis across all performance metrics. ANN achieved higher correlation with observed data (R²= 0.8 - 0.95) compared to regression (R² = 0.60 - 0.80); while also recording significantly lower errors (RMSE = 0.20 - 0.50 m, MAE = 0.18 - 0.40 m) than regression (RMSE = 0.50 - 1.20 m; MAE = 0.45 - 1.00 m). Moreover, ANN predictions exhibited minimal bias (0.01 - 0.05 m), closely approximating the ideal zero, whereas regression models demonstrated systematic underestimation or overestimation. The study highlights the strong generalization capacity of ANN across diverse hydrological zones of the Niger River Basin, underscoring its suitability for operational flood monitoring and early warning systems.
Unmanned Aerial Vehicle (UAV)-Based Precision AS-Built Surveying for Institutional Buildings
This study presents a 3D as-built survey of newly constructed laboratory buildings at Bells University of Technology using Unmanned Aerial Vehicle (UAV) technology. As-built surveys are essential for documenting deviations between original engineering designs and the actual constructed features. The objectives of this research include acquiring aerial imagery via UAV, identifying both natural and manmade features within the project area, generating a high-resolution orthophoto, and evaluating the as-built data against the original architectural design. The methodology employed GPS and UAVs for spatial data acquisition, alongside digital processing tools such as Agisoft Metashape Professional, AutoCAD Carlson 2021, and Agisoft Viewer for modeling and analysis. Comparative analysis between architectural and observed measurements revealed minimal deviations, ranging from 0 cm to 9 cm. Overlaying the final building footprints on Google Earth further confirmed spatial accuracy. The findings demonstrate that the constructed structures closely align with the intended design, validating the reliability of UAV-based 3D as-built surveys. This research highlights the effectiveness of UAV technology in producing accurate, cost-efficient, and timely documentation for construction verification, facility management, and future planning.
Copyright (c) 2025 O. S. Ogundele, A. D. Sulaimon, T. O. Ajadi, R. A. Ayanwole, G. O. Ode https://creativecommons.org/licenses/by-sa/4.0
Spatiotemporal Assessment of Shoreline Changes in Ilaje Local Government Area, Ondo State, Nigeria
The world we live in is dynamic, constantly changing and evolving, and changes, both natural and anthropological, have profound impacts on our environment and ecosystems. The goal of this study is to present a spatiotemporal assessment of shoreline changes in Ilaje Local Government Area (LGA), Ondo State, Nigeria, utilising remote sensing and Geographic Information System (GIS) techniques. The primary objectives were to quantify the extent of shoreline changes and to predict future shoreline dynamics. By analysing Landsat 7, 8 and 9 satellite imagery from 2003 to 2023, significant transformations were observed. QGIS 3.32.1 was utilised in pre-processing the imageries, ArcGIS 10.8.2 was utilised for creating composite images and spatial analysis. Digital Shoreline Analysis System (DSAS) software was used in calculating Linear Regression Rate (LRR), as well as predicting the shoreline changes. The results indicate a substantial shoreline retreat, with erosion impacting over 85% of the transects analysed, leading to an average shoreline retreat of over 160 meters. Predictive models suggest that vulnerable areas could experience up to 400 meters of additional erosion by 2033. These findings provide critical insights into the spatiotemporal dynamics of shoreline changes, supporting the development of adaptive management strategies. Recommendations include shoreline stabilisation efforts and integrated land-use planning to mitigate the adverse impacts of ongoing and future shoreline changes on the local ecosystems and communities.