Effect of Terrain Configuration on the Performance of SRTMv3 and ALOS PALSAR DEMs Over the Federal Capital Territory (FCT), Nigeria.

Digital Elevation Model (DEM) and its resulting parameters are essential terrain related information. DEM and the extracted information (slope, aspect, roughness etc.) have been identified as one of the most important and fundamental variables to various streams of engineering and planning designs which are the hall marks of development all over the world. Thus, to delineate the major surface and subsurface structures for evaluating the Planning framework for the Federal Capital City of Nigeria, analyzing the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM data is very crucial. Hence this paper aimed at examining the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM. The methodology involved data acquisition of ALOS PALSAR, SRTMV3 and Ortho DEMs, after which the ALOS PALSAR and SRTMV3 DEMs were resampled to 10m of the Ortho DEM, image classification and then an assessment of the impact of terrain configuration on DEM performance with horizontal profiles was carried out. The results revealed that SRTMV3 v3 performed better with close resemblance with the Ortho DEM on flat and undulating terrain while it underestimated the rolling terrain and overestimated the hilly and mountainous terrain. ALOS PALSAR DEM when compared against the Ortho DEM grossly overestimated all the terrain configuration in the study area. In all, the overall performance of SRTMV3 v3 had a close resemblance in performance to that of the Ortho DEM, while ALOS PALSAR had a significant difference in performance. It was therefore recommended that SRTMV3 v3 should be used as an alternative DEM source where high-resolution elevation data are not readily available. Keywords: ALOS PALSAR, Digital Elevation Model, SRTM, Terrain modelling. DOI: 10.7176/JEES/10-6-14 Publication date:June 30th 202

Mapping of Urban Features of Nnewi Metropolis Using High Resolution Satellite Image and Support Vector Machine Classifier

When maps and master plan of an area are available, they will definitely guide in the urban development, especially as a working document for enforcing planning laws by both government and the private urban developers. However, such basic geospatial information is reasonably lacking in this study area and sequel to this, the researcher aims at Mapping of Urban Features (Natural and Man-Made), which includes the Vegetation, wetlands/water bodies, buildings/pavement, open/bare surfaces and farm lands captured in GeoEye-1, High Resolution Satellite Image (HRSI) of 2016 using Support Vector Machine Classifier (SVMC) with a view of developing a reliable urban land use and land cover map of the area, which will serve as a base map for land-use planning and monitoring for a variety of end-users. The objectives include: to identify and extract features/regions of interest (ROIs) in a subset HRSI of the study area, to perform supervised classification using SVM in ENVI Software. The methodology used include Image acquisition, Image enhancement, Image Sub-setting, Image masking, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC, Post-processing Accuracy Assessment, and Preparation of maps. Environment for Visualizing Image (ENVI 5.1) incorporated with Interactive Data Language (IDL 8.3) software was used for image processing, masking, spatial data analysis and image classification. Meanwhile, Esri ArcGIS 10.2 was employed for database development and production of thematic maps. Microsoft Excel and word was used for statistical analysis and result presentation. The result of image classification using SVMC, Radial Basis Function (BRF) default kernel in ENVI 5.1 indicates that Nnewi-North L.G.A is having 13.52% of Built-up Areas, 24.23% of Vegetation, 22.05% of Water bodies, Farm lands is equal to 39.40% and open/bare surface is 0.81% and result of image classification was validated using Error Matrix and Kappa Coefficient which results revealed that (SVMC overall Accuracy =98.07% and Kappa Coefficient = 0.97. The result revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare for every Remote Sensing Analysis. The research recommends that it is imperative to check for ‘ROIs index separability’ before using it for classification, also there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of Urban Areas.. Keywords: Support Vector Machine Classifier, Extraction of Regions of Interests (ROIs), ROIs separability index analysis, High Resolution Satellite Image (HRSI), Urban Landuse and Landcover. DOI: 10.7176/JEES/9-6-11 Publication date:June 30th 201

Comparing the Capabilities of SVMC and MLC Using Contingency Matrix and a Novel Template

Since extraction of useful information from remote sensing data is important, scientists manage to propose efficient algorithms for automatic extraction of constructive information from the satellite imageries. To date, image classification has benefitted from advancements in improved computational power and algorithm development. Therefore, Satellite image classification using GeoEye-1, High Resolution Satellite Image (HRSI) of 2016, Support Vector Machine Classifier (SVMC) and Maximum Likelihood Classifier (MLC) were performed with a view to comparing the capabilities of SVMC and MLC using Post-processing Accuracy Assessment (PAA) and a Novel Template in producing urban land use and land cover map of the area. The objectives include performing supervised classification using SVM and MLC in ENVI Software, analysing the performance of SVM and MLC in mapping geometric features using error matrix and a new template. The methodology used comprise Image acquisition, Image enhancement, Image Sub-setting, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC and MLC, Post-Processing Accuracy Assessment, Statistical Analyses, and Preparation of maps. ENVI 5.1 software was used for image processing, masking, spatial data analysis and classification. Microsoft Excel, GraphPad Prism ver.7.0 and IBM SPSS ver.21 were used for statistical analysis. The result of image classification indicates that Nnewi-North L.G.A is having 13.52% of Built-up Areas, 24.23% of Vegetation, 22.05% of Water bodies, Farm lands is equal to 39.40% and open/bare surface is 0.81% using SVMC while MLC result shows that Built-up Areas is14.99%, Vegetation is 13.01%, Water bodies is 34.08%, Farm lands is 36.00% and open/bare surface is 1.32%. Error Matrix and Kappa Coefficient results revealed that SVMC is better than MLC as follows (SVMC overall Accuracy is 98.07% and Kappa Coefficient is 0.97 while MLC overall Accuracy is 82.50% and Kappa Coefficient is 0.76. Additional statistical testing with aggregate mean from SVM and MLC was used to determine the significance of the mean difference using the researcher’s developed template called “Post Confusion Matrix” (PoCoMa). The result showed that the t-statistics is 0.670 with probability value of -0.476 which is greater than 0.05, thus, the null hypothesis was accepted with a deduction that using any of the algorithms (SVM and MLC) yields no significant difference in performance and efficiency of result of the map produced. The overall study revealed that both classifiers are efficient and accurate statistically, without any significant difference but using error matrix analysis, the research revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare surfaces. The research recommends there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of ‘Urban Areas’ among others. Keywords: Support Vector Machine Classifier (SVMC), Maximum Likelihood Classifier (MLC), Post Confusion Matrix (PoCoMa), High Resolution Satellite Image (HRSI), ENVI 5.1 software, and GraphPad Prism ver.7.0. DOI: 10.7176/JIEA/9-4-06 Publication date:June 30th 201

How decentralisation influences the retention of primary health care workers in rural Nigeria

Background: In Nigeria, the shortage of health workers is worst at the primary health care (PHC) level, especially in rural communities. And the responsibility for PHC – usually the only form of formal health service available in rural communities – is shared among the three tiers of government (federal, state, and local governments). In addition, the responsibility for community engagement in PHC is delegated to community health committees. Objective: This study examines how the decentralisation of health system governance influences retention of health workers in rural communities in Nigeria from the perspective of health managers, health workers, and people living in rural communities. Design: The study adopted a qualitative approach, and data were collected using semi-structured in-depth interviews and focus group discussions. The multi-stakeholder data were analysed for themes related to health system decentralisation. Results: The results showed that decentralisation influences the retention of rural health workers in two ways: 1) The salary of PHC workers is often delayed and irregular as a result of delays in transfer of funds from the national to sub-national governments and because one tier of government can blame failure on another tier of government. Further, the primary responsibility for PHC is often left to the weakest tier of government (local governments). And the result is that rural PHC workers are attracted to working at levels of care where salaries are higher and more regular – in secondary care (run by state governments) and tertiary care (run by the federal government), which are also usually in urban areas. 2) Through community health committees, rural communities influence the retention of health workers by working to increase the uptake of PHC services. Community efforts to retain health workers also include providing social, financial, and accommodation support to health workers. To encourage health workers to stay, communities also take the initiative to co-finance and co-manage PHC services in order to ensure that PHC facilities are functional. Conclusions: In Nigeria and other low- and middle-income countries with decentralised health systems, intervention to increase the retention of health workers in rural communities should seek to reform and strengthen governance mechanisms, using both top-down and bottom-up strategies to improve the remuneration and support for health workers in rural communities