Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results

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The study assessed the amount of carbon being sequestrated in the Ondo State Afforestation Project in Oluwa Forest, southwest of Nigeria, with the view to determine the above ground biomass (AGB) and belowground biomass (BGB), estimate the total carbon content and evaluate the CO 2 sequestered. Geospatial techniques and the non-destructive field observation method were used. The results showed that a total of 359 megatons of CO 2 was estimated to have been sequestered using the non-destructive field measurements. In addition, the rate of change in land use land cover between 1984 and 2015 was determined. A geospatial database derived from field measurements was developed to monitor carbon content of the forest.

Abstract. General environmental management, which involves monitoring and modeling, requires the information of the Land surface temperature (LST) status of area concerned. Land surface temperature has gained relevance recognition over the years and there is need to develop approaches that can determine LST using satellite images. This study was conducted in Akure which has experienced rapid urbanization in recent time. The study utilized Landsat data of 1984, 1990, 2000, 2003, 2014 and 2016. The temperature data were derived from Landsat images using remote sensing algorithms for assessing LST from thermal infrared (TIR) data (bands 6 and 10). These data were processed and analyzed using tools in Idrisi and ArcGIS software systems. Surface temperatures derived from Landsat data were validated with ground meteorological data. The results revealed parabolic increase in temperature over the years and the changing pattern was investigated by adopting existing bio-spectral phenomenon Models. The validation operation revealed average bias value of ±0.31 between remote sensing-and ground-based data. This implies that remote sensing technique is reliable and therefore could be employed for large scale temperature mapping. The results could be used in mitigating urban heat island effects such as heat-related stress and ill-timed human deaths.

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos state. In this work, a land cover map for Lagos state is created using Sentinel-1 Synthetic Aperture Radar (SAR) imagery. To this aim, a sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 over Lagos state, Nigeria was acquired and used. Results include an RGB composite of the image, classified image, with overall accuracy calculated as 0.757, while the kappa value for this project was evaluated to be about 0.719. The classification therefore passed the accuracy assessment. It is concluded that the Sentinel 1 SAR results has been effectively exploited for producing acceptably accurate land cover map of Lagos state, with relevant advantages for areas with cloud cover.