DETECTION OF OIL POLLUTION ALONG THE PIPELINE ROUTES IN TROPICAL ECOSYSTEM FROM MULTI-SPECTRAL DATA Bashir Adamu, Kevin Tansey and Booker Ogutu Department of Geography, Centre for Landscape and Climate Research, University of Leicester, LE1 7RH, UK ABSTRACT The study was conducted in an oil producing environment dominated by mangrove and swamp vegetation in Niger Delta, Nigeria. Ancillary data including oil pipeline map and GPS of spill points were used in selecting sample sites to identify and detect polluted locations. A number of polluted and non-polluted sites were selected and vegetation spectral reflectance and indices for these sample sites were extracted from TM data of January and December 1986. A statistical T-test was used to test for significant differences between vegetation spectral reflectance and indices from polluted and non-polluted sites. The initial results from the analysis of spectral reflectance between polluted and non-polluted did not show any significant difference in all the six spectral bands with p-value >0.005. The results from analysis of various vegetation indices some did not show any significance differences between the polluted and non-polluted sites (e.g. the SRI, SAVI and EVI2). Other VIs (NDVI, MSAVI2 and ARVI2) showed significant differences between the polluted and non-polluted sites. From these preliminary results we can conclude that pollution from oil spills may result to the changes in leaf biochemistry of the Mangroves in the Niger Delta which are detectable from remote sensing data. Future work will focus on undertaking further temporal analysis of additional spill sites to determine what quantity of spilt oil arises in spectral changes of vegetation. Keywords: Detection, oil pollution, pipeline routes, tropical ecosystems, vegetation indices, vegetation, spectral reflectance 1.0 INTRODUCTION In ecology, mangrove ecosystems are defined as an assemblage of tropical trees and shrubs that inhibits the intertidal zone and are found all over the world. It is estimated to cover approximately 181,000 square kilometres [1] in both tropics and subtropics coastlines which are under serious decline worldwide [2] and oil pollution is one of the contributor [3] especially in most of the oil developing nations. The mangroves are generally characterised by high levels of recovery from both natural and anthropogenic disturbances, though depends on the type and degree of matter and energy exchange with nearby ecosystems and growing conditions at the sites[4]. In order to be successful in monitoring the disturbed and recovery of such mangrove areas, detecting the pollutants and mapping the affected areas is important. Oil drilling in the Niger Delta of Nigeria commenced in the 1960s [5] with a growing number of oil fields and other facilities (including pipelines) resulting to destruction of the mangrove ecosystem [6]. Oil production in the region has remained one of the major anthropogenic threats to the tropical ecosystems due to oil spillage [7]. The sources of environmental stress in plants are physical, chemical and biotic [8] and pollution which causes changes in physiological status of plants and altering their spectral behaviours [9]. Also different spectral bands (wavelengths) might indicate different responses and sensitivity to some of the stresses which may be related to some plants being resistant [8]. And it is found that the mean reflectance in visible wavelengths increases coherently and uniform in polluted sites indicating possible stress from oil pollution with NIR wavelength region indicating low vegetation reflectance in the polluted sites. Although certain species of plants are resilient and may not be affected by the oil pollution but detecting and preparing inventory maps of affected vegetated areas is essential. Thus the study explores the use of remote sensing techniques to detect oil pollution on Mangroves in the Niger Delta using multispectral TM data. Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014 Proc. of SPIE Vol. 9121, 91210D · © 2014 SPIE · CCC code: 0277-786X/14/$18 doi: 10.1117/12.2049590 Proc. of SPIE Vol. 9121 91210D-1