The study focus on the integration of Remote Sensing and Geographic Information System for identification and delineation of lineaments in relation to natural hydrocarbon seepage, which occur in Ugwueme, South-Eastern Nigeria. To achieve this objective, remotely sensed data (ASTER Digital Elevation Model and Landsat 8 OLI/TIRS) were used to depict the surface expression of faults, folds and fractures which are expressed in the form of lineaments. The global positioning system (GPS) was also used for ground verification. The geology map of the study area, which is elucidated in the geology of Nigeria was used to show the distribution of rocks and other geologic structures. The delineation of lineament features was done automatically with the PCI Geomatica while the Rock ware was used to generate the Rose diagram for demonstration of the direction of the extracted lineaments. The classification of the lineaments density and the lineaments intersection analysis were categorized as very low, low, moderate, high and very high classes respectively. Areas classified as very high to high lineaments density are potential zone, which act as conduits for hydrocarbon seepage. The result shows that a total lineament frequency of 947 km and a total lineament length of 946 km were delineated from the satellite data. The result further shows that areas with high lineaments density are concentrated in the southwest, south, central and northern part of the study area while areas with low lineament density were found within the eastern part of Ugwueme. The Rose diagram highlight the major trend in the (NE-SW), (N-S) and (NW-SE) directions, and the minor trend in the (W-E) direction. These directional trends depict the directions of lineaments which act as conduits zones for hydrocarbon seepage in the region. The overall findings of the study shows that lineament density, lineament intersection and rose diagrams are concepts applicable in hydrocarbon oil and gas seepages.
Remote Sensing is an excellent tool in monitoring, mapping and interpreting areas, associated with hydrocarbon micro-seepage. An important technique in remote sensing known as the Soil Adjusted Vegetation Index (SAVI), adopted in many studies is often used to minimize the effect of brightness reflectance in the Normalized Difference Vegetation Index (NDVI), related with soil in areas of spare vegetation cover, and mostly in areas of arid and semi–arid regions. The study aim at analyzing the effect of hydrocarbon micro – seepage on soil and sediments in Ugwueme, Southern Eastern Nigeria, with SAVI image classification method. To achieve this aim, three cloud free Landsat images, of Landsat 7 TM 1996 and ETM+ 2006 and Landsat 8 OLI 2016 were utilized to produce different SAVI image classification maps for the study. The SAVI image classification analysis for the study showed three classes viz Low class cover, Moderate class cover and high class cover. The category of high SAVI density classification was observed to increase progressive from 31.95% in 1996 to 34.92% in 2006 and then to 36.77% in 2016. Moderately SAVI density classification reduced from 40.53% in 1996 to 38.77% in 2006 and then to 36.96% in 2016 while Low SAVI density classification decrease progressive from 27.51% in 1996 to 26.31% in 2006 and then increased to 28.26% in 2016. The SAVI model is categorized into three classes viz increase, decrease and unchanged. The un – changed category increased from 12.32km2 (15.06%) in 1996 to 17.17 km2 (20.96%) in 2006 and then decelerate to 13.50 km2 (16.51%) in 2016. The decrease category changed from 39.89km2 (48.78%) in 1996 to 40.45 km2 (49.45%) in 2006 and to 51.52 km2 (63.0%) in 2016 while the increase category changed from 29.57km2 (36.16%) in 1996 to 24.18 km2 (29.58%) in 2006 and to 16.75 km2 (20.49%) in 2016. Image differencing, cross tabulation and overlay operations were some of the techniques performed in the study, to ascertain the effect of hydrocarbon micro - seepage. The Markov chain analysis was adopted to model and predict the effect of the hydrocarbon micro - seepage for the study for 2030. The study expound that the SAVI is an effective technique in remote sensing to identify, map and model the effect of hydrocarbon micro - seepage on soil and sediment particularly in areas characterized with low vegetation cover and bare soil cover.
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