satellite image analysis using remote sensing data in...

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4):612-617 (ISSN: 2141-7016)

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Satellite Image Analysis using Remote Sensing Data in Parts of

Western Niger Delta, Nigeria

O. Uchegbulam and E.A. Ayolabi.

Department Of Geosciences, University Of Lagos, Nigeria.

Corresponding Author: O. Uchegbulam _________________________________________________________________________________________ Abstract The study investigated the effect of human activities on the environment. Normalized difference vegetation index (NDVI) was used as an indication of environmental degradation in the Western Niger Delta region for the past one-half decades. The bands used for the calculation of the NDVI of 1987 and 2002 were band 4 (infrared wavelength) and band 3 (red wavelength). The Land Use Land Cover (LULC) maps and NDVI analysis revealed that dense forest in 1987 covered 1089702.2 hectares and the built-up area was 42904.1ha. The dense forest value reduced to 987688.5 ha in 2002 and the built-up area slightly increased to 45423.8 ha. In 1987, wetland covered an area of 275917.7 hectares, but reduced to 130209.4 hectares in 2002. The LULC and NDVI of the area have shown that the area has been degraded, since the NDVI values (which depict the amount of green vegetation) decreased from 0.3 in 1987 to 0.2 in 2002. These can be attributed to the effect of hydrocarbon exploration and other anthropological changes in the study area. This work will help to alert all concerned on the devastation done to the environment and measures to take to curb further devastation. __________________________________________________________________________________________ Keywords: built-up area, electromagnetic radiation, exploration activities, sensor wavelength, wetland. INTRODUCTION The Niger Delta is a large curve shaped delta which is located in Southern Nigeria like some other deltaic environments in the world. It occupies an area lying between longitude 40 - 90E and latitude 40 - 60N. It is bounded in the west by the Calabar flank, in the north by the Anambra platform and in the south by the Atlantic Ocean under which it extends. Both marine and mixed continental depositional environment characterize the Niger Delta of Nigeria (Uko, et al; 1992). The Niger Delta covers an area of about 75,000km2 (28,957mi2) in southern Nigeria, where the Niger Delta discharges it’s water into the Atlantic Ocean through a series of distributaries. The Niger Delta is one of the most hydrocarbon-rich regions in the world. Exploration and exploitation of hydrocarbons has been going on in the region since 1957, when oil was discovered there. The oil and gas production and a rapidly growing population have resulted in environmental degradation of the Delta. The objective of this study is to investigate the degradation extent of hydrocarbon exploration in the Western part of Niger delta (Sapele and environs), by analysing the remote sensing data through the use of Land Use Land Cover and Normalized Difference Vegetation Index calculation. Monitoring changes using remote sensing data has the advantage of synoptic view, repetitive coverage, cost effectiveness and availability.

The underlying premise for using remote sensing data is that a change in the status of an object under investigation must result in a change in radiance value (Mas, 1999). STATEMENT OF THE PROBLEM Hydrocarbon pollution and contaminants constitute serious problem wherever exploration and exploitation activities are carried out. According to a report by the Directorate of Petroleum Resources (DPR, 1997), over 6000 spills had been recorded in the 40 years of oil exploitation in Nigeria, with an average of 150 per year. In the period 1976 – 1996, 647 incidents occurred resulting in the spillage of 2,369,407.04 barrels of crude oil. With only 549,060.38 barrels recovered. 1,820,410.50 barrels were lost to the ecosystem. Other human activities such as felling of trees, indiscriminate disposal of chemicals and refuse, flooding caused by the blockage of water ways, etc. has equally led to the devastation of the ecosystem in the study area. This research work attempt to study the effect on the environment through the calculation of the Normalized Difference Vegetation Index (NDVI) and land use land cover over the period of 15 years, 1986 – 2002 , by analysing the satellite image of the study area. GEOLOGY OF THE STUDY AREA The Niger Delta is a geologic province in West Africa also known as the Niger Delta basin. The Niger Delta province is situated in the Gulf of Guinea and extends throughout the Niger Delta as defined by Klett et al (1997).

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4): 612-617 © Scholarlink Research Institute Journals, 2013 (ISSN: 2141-7016) jeteas.scholarlinkresearch.org

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4):612-617 (ISSN: 2141-7016)

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It is normally built up only where there is no tidal or current action capable of removing the sediment as fast as it is deposited, and hence the delta builds forward from the coastline. In other words,. the deposition of some of its sediment load from the rivers exceeds its rate of removal. This process of building up is complex, and leads to the formation of a number of separate channels (distributaries), isolated lagoons, and a network of small creeks. From the Eocene to the present, the Delta has prograded Southwestward, forming depobelts that represent the most active portion of the Delta at each stage of its development (Doust and Omatsola, 1990). These depobelts form one of the largest regressive deltas in the world with an area of some 300,000km2 (Kulk, 1995) a sediment volume of 500,000km3 (Hospers, 1965) and a sediment thickness of over 10km in the basin depocenter. THE STUDY AREA The study area is the Western part of Niger Delta. The area lie between latitude: 50 32’ N and 70 09’N and longitude: 5009’E and 60 20’E and it covers about 180km2. The study area covers some major urban settlements such as parts of Sapele, Warri, Ughelli, Benin City,, Asaba, Onitsha, among others. Sapele, the principal city in the study area lies between latitude 50 53’N and longitude 50 40’E in geographic coordinate. The area consists of Fresh water swamp, Coastal Plain Sands, Mangrove swamps, and Sombreiro -Warri plains. Soils are generally hydromorphic and poorly drained (Omo-Irabor and Oduyemi, 2006). Natural vegetation occurs as fresh water swamp forest, mangrove swamp forest and ever green lowland rainforest a major source of timber. The River Niger is the major drainage system from which other discrete river systems originate. The region has a humid equatorial climate. The cloud cover is high, with relative humidity and average rainfall above 80% and 3000mm. The viability and effectiveness of communication network are attributed in large measure to the use of orbiting satellite systems that function as relay stations with wide coverage of Earth’s surface. In remote sensing, the interest is the measurement of the radiation reflected from targets. LIMITATION OF THE STUDY Remote sensing techniques, in most cases, does not pinpoint exact pollutants, and the kind of devastations, so a direct method like chemical analysis of samples and geophysical investigation should be incorporated in quantitative environmental studies.

NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected. Measuring and monitoring the near-infrared reflectance is one way to determine how healthy or unhealthy vegetation may be (Lichtenthaler, et al, 1996). Longer wavelength visible and near infrared radiation is absorbed more by water than shorter visible wavelengths. The Normalized Difference Vegetative Index (NDVI) is a calculation, based on several spectral bands, of the photosynthetic output (amount of green stuff) in a pixel in a satellite image. It measures, in effect, the amount of green vegetation in an area. NDVI calculations are based on the principle that actively growing green plants Strongly absorb radiation in the visible region of the spectrum (the PAR, or Photosynthetically Active Radiation) while strongly reflecting radiation in the Near Infrared region. The concept of vegetative spectral signatures (patterns) is based on this principle. PAR = Value of Photosynthetically Active Radiation from a pixel NIR= Value of Near-Infrared Radiation from a pixel The NDVI for a pixel is calculated from the following formula: NDVI =

This formula yields a value that ranges from -1 (usually water) to +1 (strongest vegetative growth). However, only the red image band is usually used instead of the whole range of PAR. This will therefore lead to the modification of the NDVI calculation. NDVI =

Therefore, higher photosynthetic activity will result in lower reflectance in the red channel and higher reflectance in the near infrared channel. This signature is unique to green plants. Table 1 shows typical reflectance values in the red and near infrared channels, and the NDVI for typical cover types. Water typically has an NDVI value less than 0, bare soils between 0 and 0.1 and vegetation over 0.1.

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4):612-617 (ISSN: 2141-7016)

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Table 1 :Typical NDVI values for various cover type [Artemis Products. 2012 ]. COVER TYPE RED NIR NDVI Dense Vegetation 0.1 0.5 0.7 Dry bare soil 0.269 0.283 0.025 Clouds 0.227 0.228 0.002 Snow and Ice 0.375 0.342 - 0.046 Water 0.022 0.013 - 0.257 MATERIALS AND METHODS Data sets and Image Pre-Processing The satellite images used in this study were obtained from the Global Land Cover Facility of (1987 and 2002). Consisting of one scene from the Worldwide Reference System (WRS-2) of path 189 and row 056 (Table 2). The bands used for the analysis include 1-5 and 7. ILWIS 3.1 software was used for image processing and GIS analysis. The period of acquisition of the images was in the dry season of the year in order to reduce the effects of cloud cover (a source of problem in the tropics). Table 2: Date of Data Acquisition, Type of Satellite and Resolution of Data (Global Land Facility Cover (1987 and 2002).

Date of Data Acquisition

Type of Satellite Resolution of Data

21 – 12 – 1987 LandSat – 5 Thematic Mapper ( TM )

30m

29 – 01 – 2002 LandSat – 7 Enhanced Thematic Mapper Plus ( ETM + )

28.5m

The first step in satellite image analysis is to change the coordinate system from geographic coordinate system (Longitude / Latitude) to metric coordinate system. This was done through the ILWIS 3.1 software application. BANDS Bands refers to the sensor wavelengths used in acquisition of the data. Bands 1, 2 and 3 are visible bands (0.4 μm – 0.7 μm) Bands 4 and 5 are infrared bands (0.7 μm – 1.1μm) Band 6 is the thermal infrared band (3.0 μm – 100 μm) Band 7 is far infrared band (22μm – 1m) Filtering of the images were done band by band, one band at a time using the ILWIS algorithm AVG 3x3 based on matrix operation. An affine transformation was used to rectify 1987 TM image to the 2002 ETM+ using the Universal Transverse Mercator (UTM) map projection (Zone 32), World Geodetic System 1984 datum (WGS 84) co-ordinate system. As the study area has relatively even terrain relief, only the first degree polynomial equation was required for image transformation. Constant cloud cover is a major problem hindering the use of remote sensing data in tropical regions. In order to improve the misclassification of land cover

classes, clouds need to be eliminated from the image. The problems posed by their presence are two fold – firstly, they increase the land cover classes that have high spectral reflectance e.g. sediments and concrete structures. Secondly, they reduce the land cover classes they overlay on the image (Omo-Irabor and Oduyemi, 2006). Classification System A classification based on nine (9) land cover classes comprising of Bareland / cultivation, Builtup area, Dense forest, Mangrove / light forest, Exposed soil, Light soil, Secondary regrowth, Water body, cloud cover and wetland was used in Landsat TM 1987 data. Landsat Thematic Mapper 1987 image is a 7 band image that has a spatial resolution of 30m. Colour composite of B543 (bands 5, 4 and 3) was done during the filtering, this gave the vegetation the required green colour using Maximum Likelihood classification algorithm. Normalized Difference Vegetation Index (NDVI ) was calculated using band 4 (infrared band) and band 3 (visible , i.e red band). NDVI =

In the ILWIS 3.1 format, it is calculated thus; NDVI = [ SAP 87_B4-SAP 87_B3] / [ SAP 87_B4+SAP 87_B3] where SAP = Sapele (Assigned file name) B4 = Band 4 (Infrared band) B3 = Band 3 (Red wavelength) Landsat Enhanced Thematic Mapper Plus ETM+ 2002 image is a 7 band image that has a spatial resolution of 28.5m. Colour composite of B457 (bands 4, 5 and 7) was done during the filtering, this gave the vegetation the required green colour using Maximum Likelihood classification algorithm. Normalized Difference Vegetation Index (NDVI) was calculated using band 4 (infrared band) and band 3 (visible , i.e red band), NDVI = SAP B4 – SAP B3 / SAP B4 + SAP B3. In the ILWIS 3.1 format, it is calculated thus; NDVI = [ SAP 2002_B4-SAP 2002_B3] / [ SAP 2002_B4+SAP 2002_B3] where SAP = Sapele (Assigned file name) B4 = Band 4 (Infrared band) B3 = Band 3 (Red wavelength) RESULTS AND DISCUSSION 1987 Landsat Thematic Mapper TM has a broad spectral band of 7 bands with a spatial resolution of 30m. The image was cloudless, hence the true satellite signature of the area was imaged. It has a bult-up area of 42904.1 ha (fig.1.2), representing about 1.3% of the total land use land cover LULC

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4):612-617 (ISSN: 2141-7016)

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(fig.1.2) The dense forest covered an area of 1089702.0 ha representing 33.13% of the total Land Use Land Cover. The Normalized Difference Vegetation Index- NDVI highest value for 1987 was 0.3 (figure 1.3) indicating a fairly healthy vegetation. Landsat ETM+ 2002 has a broad band of 7 bands with a resolution of 28.5m hence a very good imagery. The cloud cover was very low, about 0.3% of the total land cover. A built-up area of 45423.8 ha was recorded (fig. 2.2) a slight increase when compared to 1987 value of 42904.1 ha. This is understandable because there was a slight increase in urban areas between 1987 and 2002. Dense forest covered an area of 987688.5 ha, a decrease compared to 1089702.0 ha of 1987 value. This is equally reasonable since the secondary re-growth will take many years to turn to light and dense forest. In 1987 wetland covered an area of 275917.7 hectares which is 8.39% of the total land use land cover in that year, but reduced to 130209.4 hectares representing 3.8% of the total land use land cover in 2002 (figures 1.2 and 2.2). The decrease in wetland can be attributed to exploration activities and other anthropological activities in the study area. The Normalized Difference Vegetation Index- NDVI value of 1987 has the highest value of 0.3 while in 2002, the highest value was 0.2 (figures 1.3 and 2.3). These shows that there was a decrease in the vegetation in the area between 1987 and 2002, a period of 15 years. The major vegetation belts in Nigeria are the Sahel Savannah, the Sudan Savannah, the Guinea Savannah, the Tropical Rain Forest and the Mangrove Forest (Aweda and Adeyewa, 2011). Western Niger Delta vegetation fall within the Mangrove forest belt. In some non oil producing areas of South-western Nigeria (Guinea Savannah zone) report from Aweda and Adeyewa, 2010 showed NDVI value of 0.5 and above for the period 1981-2001

Fig. 1.1: SAP 1987 LULC Maximum Likelihood classification

Fig.1.2: SAP 1987 LULC histogram

Fig. 1.3: SAP 1987 NDVI band 4 and 3 in gray

Fig.2.1: SAP 2002 LULC using Maximum Likelihood algorithm

Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 4(4):612-617 (ISSN: 2141-7016)

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Fig. 2.2: SAP 2002 LULC histogram

Fig. 2.3: SAP 2002 NDVI band 4 and 3 in gray CONCLUSION The remotely sensed image of the study area revealed that the area has been degraded over the period of 15 years (1987-2002). The analysis of the land use land cover of the area showed that dense forest and wetland decreased over the period, but the built-up area recorded a slight increase. Normalized Difference Vegetation Index, NDVI (which depict the amount of green of vegetation, hence the health of the vegetation) decreased from 0.3 in 1987 to 0.2 in 2002 as against 0.5 and above in some non-oil producing areas at same period. These amount to the degradation of the environment. It can be inferred that the degradation may be due to factors like seasonal changes, lumbering activities, expansion of urban centres, exploration and exploitation of oil and gas, etc..

Though oil facilities are not large space users, but their impact on the environment of the study area cannot be ignored. It is recommended that near surface geophysical survey be carried out in the study area to see the correlation between degrading environment and groundwater quality. REFERENCES Artemis products; 2012 ; Retrieved on April 2, 2012. http://www.met.rdg.ac.uk/~swsgrime/artemis/ch3/ndvi/ndvi.html Aweda, E. D. and Adeyewa Z. D 2010. Determination of the Onset and Cessation of Growing Season in South West Nigeria. 2010 International Conference on Nanotechnology and Biosensors IPCBEE vol.2 (2011) © (2011) IACSIT Press, Singapore Aweda, E. D. and Adeyewa Z. D 2011. Inter annual variation of vegetation anomaly over Nigeria using satellite-derived index. Advances in Applied Science Research, 2011, 2 (3): 468-475 Doust, H., and Omatsola E. 1990, Niger-Delta, in Edwards, JD, and Santogrossi, P.A. (eds.), Divergent/passive margin Basins, AAPG memoir 48: Tulsa, American Association of Petroleum Geologist, P. 239-248. DPR, 1997: Department of Petroleum Resources. Annual Reports. Abuja. 191pp. Global Land Facility Cover (1987 and 2002). Data andProducts.http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp (Accessed 18 March. 2012). Hospers, J., 1965, Gravity field and structure of the Niger-Delta, Nigeria, West Africa: Geological Society of American Bulletin, v.76, p.407-422. Klett, T.R., Ahlbrandt, T.S., Schmoker, J.W., and Dolton, J.L., 1997, Ranking of the world’s oil and gas provinces by know petroleum volumes: U.S Geological survey open-file Report-97-463 CD-Rom. Kulk, H., 1995, Nigeria, in, Kulke, H, ed., Regional Petroleum Geology of the world. Part II Africa, America, Australia, and Antarctica: Berlin, Gebruder Borntraeger, P. 143-172. Lichtenthaler, H. K., Gitelson, A., Lang, M. 1996. Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements, Journal of Plant Physiology Volume: 148, Issue: 3-4, Publisher: Gustav Fisher Verlag, Stuttgart, Pages: 483-493

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Mas, J.F., 1999.Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing.20, pp. 139-152 Omo – Irabor, O.O. and Oduyemi, K., 2006.A Hybrid Image Classification Approach For The Systematic Analysis of Land Cover (LC) Changes in the Niger Delta Region: proceedings of the 6th Int’l conference on earth observation and geoinformation sciences in support of Africa’s development. Cairo, Egypt. Uko, E.D, Ekine, A.S, Ebeniro, J.O and Ofoegbu, C.O. (1992): Weathering structure of the east-central Niger Delta. Nigeria. Geophysics 57, 1228-1233.