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
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
The Effect of Boko Haram Activities on Land Use and Land Cover at Yankari Game Reserve, Bauchi State, Nigeria (Published)
The study examined the effect of Boko Haram activities on Land Use Land Cover change at Yankari Game Reserve, Bauchi State, Nigeria. Vegetation in 2003 was 64.36% but later decreased to 48.35% in 2010 and recently increased to 61.78% in 2016. The decrease in vegetation cover from 64.36% in 2003 to 48.35% in 2010 can be attributed to massive infrastructural development during this era. Similarly, the decrease in 2010 (48.35%) could be attributed to human interference such as fetching of firewood, farming, lumbering, etc. which are the agents of vegetal degradation. It is noteworthy that Boko Haram rampage was at its peak during this period and as such tourists’ patronage (especially foreigners) to the Game Reserve reduced drastically for fear of being bombed, kidnapped, etc. But the increase between 2010 and 2016 can be attributed to regeneration as the fight against Boko Haram is being won by the government. It is also be due to considerable reduction in human activities consequent on patronage by tourist (as there is strict regulatory control of unlawful human activities) and time for vegetation regeneration. It is recommended that the people of the host community of Yankari Game Reserve should be educated through enlightenment campaigns on the consequences of indiscriminate felling of trees. In addition to that, alternative sources of cooking energy should be provided to the local inhabitants. The management of Yankari Game Reserve should adopt Remote Sensing and Geographic Information System techniques which have proven to be effective and efficient in the monitoring of vegetation cover. This would help to control encroachment and illegal logging in the area.
Citation: Ukah Chinomso, Ejaro Sunday P., Makwe Edith and Iwara Anthony (2021) The Effect of Boko Haram Activities on Land Use and Land Cover at Yankari Game Reserve, Bauchi State, Nigeria, British Journal of Environmental Sciences, Vol.9, No.6, pp. 1-18
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.
Assessment of Anthropogenic Activities and Their Impact on Ngong Hills Forest in Kajiado County, Kenya: A Remote Sensing Approach (Published)
Human beings are dependent on forests for various livelihood needs. Forests offer a variety of benefits, including ecological, social as well as economic benefits. As such, the development and conservation of forests around the world is vital. Monitoring of the forest ecosystem is mandatory in order to detect any changes in the ecosystem. Forest cover change detection gives an opportunity to track the productivity, health and the forest cover as well over the years so as to enable proper management, promote conservation and enhance functionality. Optical and radar remote sensors make it possible to monitor changes by use of various analytical techniques that include visual interpretations. The study investigated how remote sensing can be applied to detect change in forest ecosystem and to assess the rate of change of Ngong Hills Forest in Kenya. The project sought to determine whether anthropogenic activities are the major cause of the change in Ngong Hills Forest. Data from satellite images was analysed from 1984 to 2019 to identify the changes that have occurred on the ecosystem. Landsat and Rapid-Eye images were used to inform on change detection. In this case, rapid eye data was found to be better than Landsat data in informing on change detection because of its high resolution thus high precision and better results. The changes depicted by the remotely sensed data were mapped for ease of analysis and visualization. The research depicted a massive decrease in the forest cover despite the afforestation efforts by the Kenya Forest Service (KFS) in the 1990s. The forest has been depreciating massively from 1995 depicting greater deforestation rates between the years 2010 and 2019. This depreciation has been acknowledged by the KFS as it is said to be occurring due to the anthropogenic activities mainly settlement and logging. The means of detecting change by use of remote sensing is thus able to identify the exact areas that change has occurred and thus provide insight for the Kenya Forest Service and other ecosystem protection bodies on the most affected areas and the extent of change. Once the study area is mapped, it is possible to calculate the areas that have decreased in vegetation quantity, areas where increase has occurred as well as the areas that have remained unchanged. The findings of the study make it possible for management agencies to enforce conservation because of the presence of reliable data.
Dealing with large amount of data and running analytics on those data is becoming challenging with rapidly increase in various types of data. Big data is the technology which deals with such large amount of data analytics. It covers wide range of application areas from managing data of social networking sites to the large amount of data on ecommerce portals for decision making. In this paper an attempt is made to present a review of State of Art technology in Big Data, its importance, major benefits and challenging in this domain.
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.
Change Detection in Landuse / Landcover Mapping in Asaba, Niger Delta B/W 1996 And 2015. A Remote Sensing and GIS Approach (Published)
Remote sensing is used in this research work for the development and acquisition of Landuse/land cover data, pattern and its attendant effects in Asaba, Delta State Nigeria. Remote sensing images and digital data verified by ground trothing (field work) satellite data are used to assess the rate of change in Landuse / Land cover between 1996 and 2015. It also examines the extent to which images and GIS softwares effectively contribute to mapping landuse/cover in the Niger Delta region. Remote sensing and geographic Information System (GIS) help integrate natural, cultural, social and economic information to create spatial information system on the available terrain resources. Sets of NARSDA images were acquired corresponding with the years, field checked to ascertain the data captured on the terrain.. The digital satellite data are incorporated as input data into IDRISI 32 GIS environmental to separately map out the landuse/land cover units and their magnitude determine. Five distinct units were identified in classification of landuse/landed cover pattern categories as follows: Farmland, Build up land, Waste land, Forest land and Water bodies. Land consumption rate indicate a progressive spatial expansion of the city was high in 1996/2006 and higher between 2006 and 2015. Also, land absorption coefficient being a measure of consumption of new urban land by increased urban population, was high between 1996 and 2006 and between 2006 and 2015. Ground trothing was carried out to ascertain the accuracy of data and there are major changes in the landuse/land cover. It was discovered that there is rapid inbuilt-up areas evidently explained in buildings projects that resulted in decrease in forest land, agricultural land and open space. This is attributed to the anthropogenic activities of farming, bush burning, grazing, etc. However, the area occupied by water remained unchanged over the years. This study demonstrates that remotely sensed data and GIS based approach is found to be timely and cost effective than the conventional method of analysis, classification of land use pattern effective for planning and management