Introduction

This study generally taking into account the detection of hydrocarbon seeps through stress of vegetation using hyperspectral remote sensing analysis which concerns on the spectrum reflectance of the plants itself, where and how they happened as there is a growing interest in finding possibilities of new oil and gas deposits focusing on onshore discovery. Thus it is very important to solve these questions in order to get a comprehensive understanding of the phenomena happened. This topic becomes an interest for this study as the environmental issue becomes as global awareness nowadays plus the fact that seeps is an important as indicating petroleum reservoir down below or a source of pollution which plays a crucial role in human’s life. Nowadays, most of reservoir possibilities have been done through geology and seismic exploration as it has the ability to detect the type of soil deposits in detail.

Seepage hydrocarbon phenomena

As scientifically explained, seepages phenomena is due to pressure differences in the Earth’s subsurface, hydrocarbons can migrate from reservoirs to shallower levels and eventually to the surface. Later at the surface, the hydrocarbons escape and consequently become natural source of pollution to soils or water, if it is in form of tar and oil (heavy hydrocarbons), or in form of gases (light hydrocarbons), which contribute to the global warming. Apart from the above mentions, natural hydrocarbon seeps, both macro and micro has been major indicator of the possibilities of petroleum deposits location (Etiope, 2009, Shu-Fang 2008, Noomen 2003, and Shu-Fang 2008). Schumacher (1999) explained that macroseepage refers to the visible oil gas seeps, while microseepage is an elevated concentration of hydrocarbon elements in soils or etc above the petroleum reservoirs.

According from Etiope (2009), there are at least 4 important key points of gas and oil seepages, which are (1) as an indicators of petroleum or natural gas reservoirs, (2) indication of the occurrence of a fault, (3) represent a geo-hazard for societal community and industry and (4) as a natural sources of greenhouse gas. There are many ways in detection of hydrocarbon existence; one of the ways is through stress of vegetation.

Vegetation stress

It is agreeable by many scholars that natural gas including hydrocarbon may influence plant in many ways, as for example through the root system, air, and aerial contact. Smith (2002) stated that the root system was the main medium how the gas may be taken into the plant. In addition, the chemical contains in the soil properties also affect the plant stress of the leaking gas. While there is no harm when involve an aerial expose to the plant.

Smith (2002) added that the age of the trees might have a significance effect to the hydrocarbon. He stated that the younger tree was able to adapt with the seeps elements than trees that more mature. It is due to the length of the root that younger trees is more shallower rooting system than older trees that had deep roots. And because of the high concentration of gas in the soil, the roots were unable to grow towards the surface to get oxygen (Smith 2002).

Other caused of natural gas leakage include growth and reproduction, and decreased numbers of individuals, or a change in the green colour of the leaves. If any of the said scenarios happen, it will automatically change in the reflectance curves of the plant (Smith 2002).

Hyperspectral remote sensing

It is recorded that the very first airborne hyperspectral remote sensing data in Malaysia were acquired in 2004 over some area of Selangor by using UPM-APSB’s Airborne imaging Spectrometer for different Applications (AISA) (Jusoff, 2007). The missions were aimed to map the individual timber species identification and forest inventory. The situation marked as a starting point for hyperspectral remote sensing emerging in Malaysia.

In general each material in the earth has its own unique spectral signature which has been used for the purpose of identification. It marked of the arrival of typical multispectral remote sensing that produce a few broad bands that is limited the differential in order to produce detail identification (Singh, 2010). Nowadays, with the emerging of technology, hyperspectral remote sensing has been introduced which allowed a more detail identification consisting of large number of narrow, contiguously spaced spectral bands (Fig. 2).

Hyperspectral remote sensing also known as spectrometers is a sensor that can be used in laboratories, or mounted on aircraft and satellites as for global exploration that include in mapping an earth elements. Remote sensing in oil and gas exploration has listed a less destruction technology; also give lots of positive impact to economy, safety and efficiency (Shu-Fang 2008).

Fig. 2: The concept of hyperspectral imagery (source: Shippert, 2004).

Spectral library

Spectral Libraries are data archives that consist of spectral signatures measured on selected natural and/or man-made materials (Lukas et. al, 2011). The data in these libraries should also include the meta-information on the records. The two main purposes of having these spectral library data is for: the use of the data in remote sensing as in-situ radiance/reflectance measurements for the calibration and/or end-members selection further data processing (Selige et. al, 2006); or the use of the measurements for rapid laboratory assessment by diffuse reflectance spectroscopy (DRS), for example soil properties (Viscarra et. al, 2006). There exist a lot of spectral libraries such as the Jet Propulsion Laboratory (JPL) spectral library, John Hopkins University (JHU) spectral library where its compilation is available at NASA’s ASTER spectral library collection. The ASTER library collection includes spectra of rocks, minerals, lunar soils, vegetation, and many more, covering the visible through thermal infrared wavelength region (0.4-15.4µm) of the electromagnetic spectrum (Lukas, 2011). In terms of hydrocarbon spectral library, there is mostly data on the rocks and minerals, but almost none spectral data for the hydrocarbon itself on different kind of soils. For soils, there exists the Global Soil Spectral Library which is an on-going development by Viscarra Rossel, under soil spectrometry group (Viscarra, 2008).

Due to the reservoir seepage to the surface, a hypothesis arises that there will be effects on the vegetation of the soil. It was assumed that natural gases displaces the soil air and that oxygen shortage is the cause of changes in vegetation growth and reflectance, where this effect can be seen within 2 weeks of hydrocarbon microseepage to the surface (Noomen, 2007)

Application of remote sensing in detecting hydrocarbon

The detection of hydrocarbon seeps through remote sensing application has been used over the pass 20 year since the introduction of the technology. It is proven in minimizing the destruction that offers from the traditional methods of investigating seepage and also pollution. Stated by Meijde (2008), optical remote sensing has been tested by many scholars for onshore exploration and detection of seeps at the Earth’s surface since 1984. As stated in above section, there are various scholars successfully detect hydrocarbon elements through hyperspectral remote sensing application, either from pipe leakage, seeps, stress of vegetation and also from oil spills through remote sensing data (Smith, 2002, Kuhn, 2002, Noomen, 2003, and Shu-Fang, 2008).

For all materials, the amount of solar radiation that it reflects, absorbs, or transmits varies with wavelength (Sarah and Shuhab, 2007). This vital property of matter makes it possible to classify different substances and separate them by their spectral signatures or spectral curves. In principle, we can recognize various kinds of surface materials and distinguish them from each other by these differences in reflectance (Lillesand and Kiefer, 2000). Hence, this technology is very reliable in order for us to detect and classify hydrocarbons seepage through its use of reflectance spectroscopy. Hyperspectral sensors which acquires image at a very high resolution; very narrow spectral bands; allows for easy interpretation and identification of which material does this wavelength belongs to, compared to the broader band of multispectral sensors that cannot resolve these diagnostic spectral differences (Lillesand and Kiefer, 2000). In terms of microseepage detection, we can determine this occurrence through the spectral absorption signature on the surface image data.