Surveying & Geoinformatics
Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/70
Surveying & Geoinformatics
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Item Land Cover Classification: Comparison between Fuzzy and Boolean Classifier(FIG Working Week 2016 Recovery from Disaster Christchurch, New Zealand, May 2–6, 2016, 2016-05-06) Abdullahi Ahmed KUTA, Oluibukun Gbenga AJAYI, Ekundayo Abayomi ADESINA, Zitta NANPON, Hassan .A. SAMAILA-IJAProduction of Land Use/Land cover maps is very important for environmental monitoring and development. Image classification using either hard/or soft classifiers is crucial in the production of these maps. While fuzzy classification is suitable for modelling vagueness due to mixed pixels in the land cover, Boolean, on the other hand, is suitable for modelling land cover with well-defined boundaries. The analyst’s choice of image classifier is a very important decision in image classification, as this determines the classification output. Using Landsat5 TM of 1984, Landsat 4 TM of 1992 and Landsat7 ETM+ of 2000 satellite images, this research looks at the comparison between soft (Fuzzy) and hard (Boolean) classifiers. The Landsat ETM+2000 of a 15m spatial resolution was resampled to a 30m pixel size so that the three images would be of the same pixel size to effectively carry out pixel-to-pixel analysis. Due to the nature of the landscape and bearing in mind that land cover responds differently to various Landsat spectral bands, three band combinations (image bands 2, 3, and 4) were considered for the classification. The images were classified into four (4) different land spectral classes by employing the fuzzy membership function and maximum likelihood classification tools in Idrisi Taiga 16 software. The results obtained show that the spatial distribution of the modelled land cover classes for both Fuzzy and Boolean is the same, which buttresses the performance level of both models. The major difference between the two models lies in the output; while fuzzy shows a subtle representation according to the degree of membership function of each land cover class, the Boolean, on the other hand, represents the land cover types with a well-defined boundary. Also, the summation of the fuzzy land cover areas is not equal to the size of the study; 108% in 1984, 107% in both 1992 and 2000, are unlike the Boolean with 100%.Item Dynamic Road Segmentation of Part of Bosso Local Government Area, Niger State(American Journal of Geographic Information System 2015, 2015) Oluibukun G. Ajayi, Joseph O. Odumosu, Hassan A. Samaila-Ija, Nanpon Zitta, Ekundayo A. Adesina, Olaniyi J. OlanrewajuDynamic road segmentation (DRS) data model finds maximum application in GIS Transportation (GIS-T) studies and analysis, serving as a data model that splits linear features into a new set of segments wherever its attributes change. An attempt has been made by this research to carry out the Dynamic road segmentation of part of the Bosso Local Government Area of Minna using an IKONOS image of 1-m Pan-sharpened spatial resolution and other field survey acquired data. Geometric data was acquired using Handheld GPS receivers while the attribute data was acquired via the social survey approach(administration of questionnaires, direct observations, and on-site interviews). A Geo-database was designed and created within the ArcGIS 10.0 software environment. Analysis and queries were also performed to solve some pertinent issues concerning the route segments and to highlight the closest infrastructural facility in case of emergencies. The result highlighted the present road pavement condition of the considered road segments, adjacent land use, traffic congestion rate, notable crime spots, and accident hotspots. It also suggested that the building up of traffic congestion along the Kpakungun roundabout axis is due to the road width (8m), high traffic volume, and the dilapidating state of the road’s pavement.Item Modelling Surface Runoff and Mapping Flood Vulnerability of Lagos State from Digital Elevation Model(FIG Congress 2014 Engaging the Challenges – Enhancing the Relevance Kuala Lumpur, Malaysia 16-21 June 2014, 2014-06) Olayemi Joseph ODUMOSU, Oluibukun Gbenga AJAYI and Ekundayo ADESINAFlooding in recent times has become a critically problematic phenomenon of spatio-temporal order and considerably high frequency of occurrence all over the world, and most especially in coastal nations/states. Lagos State, one of the nine (9) Coastal States of Nigeria, has witnessed and is still witnessing multivariate cases of flooding which attains its peak in the rainy seasons (April-October) of every year resulting in loss of life and economic valuables/properties. To curb this menace, an integrated solution (combination of empirical hydrological models with remote sensing and GIS capacity) is thus presented herein using a downloaded Digital Elevation Model of the study area to delineate watersheds, flow direction, contributing areas, and flow path/Channel. Also, surface runoff was simulated for an eight-hour homogeneous rainfall, and the resulting gauge readings from eleven (11) fictitious gauge stations distributed across the state were obtained. The study was able to produce a map categorising Lagos state into three (3) zones based on their vulnerability to flood. The Quantum GIS software was used for the analysis and simulation.Item AN EVALUATION OF GEOMETRIC DATA ACQUISITION USING LANDSAT IMAGERY(CASLE – Abuja 2016 Conference Papers., 2016) Oluibukun .G. Ajayi, Yusuf .D. Opaluwa, Quadri .A. Adejare, Joseph. O. Odumosu, Nanpon Zitta and Ekundayo .A. AdesinaThe implementation of appropriate digital image processing method is crucial for deriving urban land cover maps of acceptable accuracy and cost. This study examines the effect of acquiring images in various spectral regions (bands), the impact of some image processing techniques on the combination of the different bands and the acceptable mode in which the features of the image could be classified using unsupervised classification (clustering) and supervised classification based on four different hard classifiers. Four different filter types were experimented on the colour composite images before classifying the images into different distinct land spectral classes. The Integrated Land and Water Information System (ILWIS) software was used to classify LandSAT 7 image of 2001, part 189r053, zone 32, bands 1 (Blue), 2 (Green), 3 (Red), 4 (Near infrared), 5 and 7 (Middle infrared) wavelength. From the study, it was observed that AVG 3x3 filter type is the most preferred. Colour composite of bands 5, 4, 3 in the RGB planes gave the best representation of the features of the image and that Box classifier, Minimum Distance to Mean Classifier and Maximum Likelihood classifier are excellent classifiers for image supervised classification