Surveying & Geoinformatics

Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/140

Surveying & Geoinformatics

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End date: 2019Subject: search.filters.subject.Remote Sensing

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    Applications of Remote Sensing Data for Environmental Modelling and Analysis
    (International Conference of School of Technology Education, Federal University of Technology, Minna, Nigeria, 2014-10-15) Onuigbo, I.C., Odumosu, J.O., Adesina, E.A., & Iwuchukwu, K.
    As the ever-growing world population continues to impose immense pressure on the environment, the dynamic balance between environmental stability and resource utilisation is continuously being threatened. Considering the global coverage of this menace, remote sensing appears to be the most economic and efficient means of data acquisition for monitoring and analysing environmental degradation patterns. Five (5) different satellite missions and an online vector-based digital Soil Map have been theoretically examined to evaluate their data products, spatial and spectral resolutions, applications, and source of data access.
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    A Remote-Sensing Based Assessment of Seasonal Variation in Vegetation Quality and Productivity in Nimbia Forest
    (Civil and Environmental Research, 2015) Ifeanyi C. Onuigbo, Joseph O. Odumosu, Ekundayo. A. Adesina, Nanpon. Zitta, Oluibukun G. Ajayi, Abdullahi A. Kuta
    Agriculture plays a vital role in the growth and sustainability of developing countries, such as those in sub-Saharan Africa. Therefore, there is a need to monitor and assess the changes taking place in vegetation quality and productivity. The conventional ground survey method has proved to be highly tedious, laborious, and grossly limited, especially when large and densely forested regions are concerned. This paper presents a remote sensing-based approach as a real-time technology to determine the effect and extent of water loss caused by seasons on vegetation quality in Nimbia forest, Kaduna State. ENVI 4.7, ArcGIS 10.0, and Global Mapper Software were used, and a discrepancy of about 45% in the quality of vegetation has been observed between seasons in the region under study.