Litho-Lineament Mapping of Rocks in Ila Orangun Area Southwestern Nigeria Using Remote Sensing and Aeromagnetic Data: Implications for Mineral Exploration (Published)
Ila Orangun is located north of Okemesi and falls within latitudes 7054l and 8000l N and longitudes 40 53l and 50 00l E respectively. Field studies revealed six lithological units in the study area namely quartzites, granites, granite gneiss, porphyritic granites, amphibolite schist and pegmatites. The aim of the research is to elucidate the geology and structure as well as evaluate the metallic mineral potentials of the study area. An integrated multi-technique study approach was adopted for reconnaissance survey and structural interpretation which involved the use of acquired remotely sensed satellite imageries such as Landsat-8 OLI, shuttle radar topographic Mission, Radar Sentinel-2A and a geophysical method involving the use of aeromagnetic data. Results obtained from interpretation of remote sensing data was used to produce different lineament maps which displayed fractures of varying lengths and trend dominantly in the NE-SW directions and subsidiary fractures also orientate in the NW-SE, E-W and N-S directions. Results from the stereographic projection plots showed that the dominant orientation of foliation planes is in the NE-SW direction with the NE and SE sections of the study area recording the highest cluster of the foliation planes. Hence, the eastern section spotted as the mineralization zone.
Keywords: Aeromagnetic data, Brittle, Ila Orangun, Remote Sensing, lineament
Hydrocarbon Prospect Evaluation from Remote Sensed Data in Parts of Lower Benue Trough (Published)
The search for hydrocarbons in parts of the lower Benue basin has remained comatose because of poor discoveries. The basin has attracted focused attention in the recent because of the continued discovery of commercial hydrocarbons in the contiguous basins of Chad and Niger Republics and Sudan. However, data from drilled wells revealed a number of continuous organic rich stratigraphic intervals with potentials for both oil and gas generation. With the rising global energy demand and uncertainties in supply, explorations are taking new dimensions with the adoption of new technologies. Remote sensing offers an attractive, robust and innovative reconnaissance technique that compliments the geophysical methods in hydrocarbon exploration. In the present study, a satellite image-based analysis was conducted for extracting surface lineaments and terrain attributes for hydrocarbon prospect evaluation in parts of the lower Benue basin. Advanced space borne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM) and Landsat 8 OLI/TIRS data were used. Results revealed that lineament distribution, density and orientation vary across the study area. The tectonic highs (escarpment) have high prevalence of lineaments and lineament density than the lowlands/valleys, suggesting a structurally deformed area. The NE-SW is the most dominant lineament orientation and the major tectonic feature that control the structuration of the study area, while NW-SE, N-S and E-W lineament orientations are less dominant. Terrain attributes were partly lineament-controlled and lithological and could be related to the development of petroleum entrapment structures. Hydrocarbon prospect zones were delineated in medium to high lineament density areas, where lineament intersections and connectivity capable of trapping hydrocarbons is high. Therefore, Agwu, Awka, Enugu, Nsukka, Udi and Ukehe located on the escarpment are preferred prospect areas than Adanu, Nkalagu and Igumale in the flanking lowland/valley areas for detailed hydrocarbon exploration. Correlation of lineament density and surface hydrocarbon seepage in parts of the basin, revealed that high lineament density correlates with known location of hydrocarbon seepage in the study, indicating the connectivity of these lineaments with deep seated structures.
Citation: Choko C., Ehirim, C. N. and Ebeniro, J. O. (2022) Hydrocarbon Prospect Evaluation from Remote Sensed Data in Parts of Lower Benue Trough, British Journal of Earth Sciences Research, Vol.10, No.4, pp.7-20
Keywords: Benue Trough, Remote Sensing, escarpment, hydrocarbon, lineament
Hydrocarbon Prospect Evaluation from Remote Sensed Data in Parts of Lower Benue Trough (Published)
The search for hydrocarbons in parts of the lower Benue basin has remained comatose because of poor discoveries. The basin has attracted focused attention in the recent because of the continued discovery of commercial hydrocarbons in the contiguous basins of Chad and Niger Republics and Sudan. However, data from drilled wells revealed a number of continuous organic rich stratigraphic intervals with potentials for both oil and gas generation. With the rising global energy demand and uncertainties in supply, explorations are taking new dimensions with the adoption of new technologies. Remote sensing offers an attractive, robust and innovative reconnaissance technique that compliments the geophysical methods in hydrocarbon exploration. In the present study, a satellite image-based analysis was conducted for extracting surface lineaments and terrain attributes for hydrocarbon prospect evaluation in parts of the lower Benue basin. Advanced space borne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM) and Landsat 8 OLI/TIRS data were used. Results revealed that lineament distribution, density and orientation vary across the study area. The tectonic highs (escarpment) have high prevalence of lineaments and lineament density than the lowlands/valleys, suggesting a structurally deformed area. The NE-SW is the most dominant lineament orientation and the major tectonic feature that control the structuration of the study area, while NW-SE, N-S and E-W lineament orientations are less dominant. Terrain attributes were partly lineament-controlled and lithological and could be related to the development of petroleum entrapment structures. Hydrocarbon prospect zones were delineated in medium to high lineament density areas, where lineament intersections and connectivity capable of trapping hydrocarbons is high. Therefore, Agwu, Awka, Enugu, Nsukka, Udi and Ukehe located on the escarpment are preferred prospect areas than Adanu, Nkalagu and Igumale in the flanking lowland/valley areas for detailed hydrocarbon exploration. Correlation of lineament density and surface hydrocarbon seepage in parts of the basin, revealed that high lineament density correlates with known location of hydrocarbon seepage in the study, indicating the connectivity of these lineaments with deep seated structures.
Citation: Choko C., Ehirim, C. N. and Ebeniro, J. O. (2022) Hydrocarbon Prospect Evaluation from Remote Sensed Data in Parts of Lower Benue Trough, British Journal of Earth Sciences Research, Vol.10, No.4, pp.7-20
Keywords: Benue Trough, Remote Sensing, escarpment, hydrocarbon, lineament
Depletion of Forested Area: Geidam Perspective (Published)
Land cover maps provide best understanding of current landscape change over time. One can evaluate past land cover maps for several different years for management decisions as well as gain insight into the possible effects on decisions making. One of the key monitoring areas is how the environment keeps degrading resulting from increased anthropogenic activities such as the removal of the forest covers. This study monitors the pattern changes of the Geidam Yobe state Nigeria, using Landsat images of two different periods from Enhanced Thematic Mapper (ETM+) image of data of 1988 and 2018. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (MLC) algorithm to produce land cover maps of the Geidam. The accuracy of the classification was assessed with confusion matrices giving results morethan the minimum 85% required. The results revealed that the built-up and tree area increase by (+30.97%), water body reduced by (-5.06%) and forest reduce by (-23.48%) within the study period. This shows a rapid decrease in the forest, which is partly attributed to deforestation activities and partly to climate change impact.
Citation: Alhaji, Mustapha Isa; Ayuba, Abubakar Fusami, and Danboyi, Joseph Amusu (2022) Depletion of Forested Area: Geidam Perspective, British Journal of Earth Sciences Research , Vol.10, No.4, pp.1-,6
Keywords: Change Detection, Classification, Landsat data, Remote Sensing
Depletion of Forested Area: Geidam Perspective (Published)
Land cover maps provide best understanding of current landscape change over time. One can evaluate past land cover maps for several different years for management decisions as well as gain insight into the possible effects on decisions making. One of the key monitoring areas is how the environment keeps degrading resulting from increased anthropogenic activities such as the removal of the forest covers. This study monitors the pattern changes of the Geidam Yobe state Nigeria, using Landsat images of two different periods from Enhanced Thematic Mapper (ETM+) image of data of 1988 and 2018. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (MLC) algorithm to produce land cover maps of the Geidam. The accuracy of the classification was assessed with confusion matrices giving results morethan the minimum 85% required. The results revealed that the built-up and tree area increase by (+30.97%), water body reduced by (-5.06%) and forest reduce by (-23.48%) within the study period. This shows a rapid decrease in the forest, which is partly attributed to deforestation activities and partly to climate change impact.
Keywords: Change Detection, Classification, Landsat data, Remote Sensing
Effect of Urbanization on Land Use Land Cover in Gombe Metropolis (Published)
This study examined the integration of Remote Sensing and Geographic Information System (RS/GIS) for analyzing land use and land cover dynamics in Gombe Metropolitan, the Gombe State capital for the period 1976 to 2016. Land sat (TM) images of 1976, 1996and 2016 were used. The study employed supervised digital image classification method using Erdas Imagine 9.2 and Arc GIS 10.5 software and classified the land use into undisturbed vegetation, sparse vegetation, Settlements, Farmlands, Rock outcrops, Bare surfaces. The images were analyzed via georeferencing, image enhancement, image resampling and classification. The results obtained showan increasing settlements (from 0.36% – 4.01%) and farmlands (from 24.8% – 51.2%), over a decreasing of other LULC classes (bare surfaces, undisturbed and sparse vegetation, and rocky outcrops) for the time period of 1976 to 2016. These results could help city planners and policy makers to attain and sustain future urban development. It is therefore recommended that encouragement should be given to people to build towards the outskirts, like New mile 3 and Tumfure,etc through the provision of incentives and forces of attraction that is available at the city center in these areas to avoid the problem of overcrowdings.
Keywords: : GIS, Change Detection, Remote Sensing, Urbanization, gombe, land use
Remote Sensing Application in Forest Monitoring: An Object Based Approach (Published)
Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to forest classification. This paper introduced an object based method to SPOT5 image to map the land cover in Yen Nhan commune in 2015. This approach applied multi-resolution segmentation algorithm of eCognition Developer and an object based classification framework. In addition, forest maps from 2000 to 2015 were used to analyze the change in forest cover in each 5 years period. The object based method clearly discriminated the different land cover classes in Yen Nhan. The overall kappa value was 0.73 was achieved. The estimation of forest area was 89.05 % of forest cover in 2015. By overlaying achieve forest maps of 2000, 2005, 2010 and the classified map of 2015 shows vegetation changed during 2000-2015 remarkably.
Keywords: : GIS, Change Detection, Forest Classification, Remote Sensing, SPOT 5 Image