Fusion of Aeromagnetic Data and Satellite Images for Delineation

of Lineaments – Melut Basin, SE Sudan

K. A. Elsayed Zeinelabdein
Alneelain University, P.O. Box 12702,

kalsayed2001@yahoo.com, Khartoum, Sudan.

Abstract

Melut Basin is located in Sudan approximately 550 km south of Khartoum. The geology of Melut Basin has been poorly studied because of lack of exposures, climatic difficulties and inaccessibility. In the present time Melut is considered as one of the most potential basins in Sudan with respect to petroleum. Lineament features are of great importance in this basin since trapping styles are fault related. Mapping of lineamnets using satellite images and geophysical data utilizing fusion techneques produced lineament map of high quality that may be used as a guide in exploration works.

1. Introduction

Sudan is located in northeast Africa with an area of about 2.5 million km². Sudan is the largest country in Africa, but its geology and natural resources have been poorly studied. Despite the considerable efforts that has been made in the recent times to map different areas in Sudan, there are large areas still unexplored.

Till 1977 the geology of Melut Basin has been studied very poorly because of lack of exposures, climatic difficulties, inaccessibility and the unstable political conditions. By the year 1984 Chevron Overseas Petroleum Inc. has acquired a vast amount of geological and geophysical data that covers the southern and central Sudanese rift basins (including Melut). In the present time Melut is considered as one of the most potential basins with respect to petroleum. Many oil fields have been discovered in this area and prospecting activities are, so far, going on.

Melut Basin is located onshore Sudan approximately 550 km south of Khartoum. It lies along the Mesozoic/Cenozoic trend of rift –related basins running from Nigeria to the west through Chad, the Central African Republic, Sudan and possibly into Kenya (RRI, 1989). Of elongated form, the basin is about 175 miles long and 60 miles wide, exhibiting rift extensional tectonic feature with strike-slip compressional effects resulting in complex fault-bounded anticlines (RRI, 1989). Major fault trends throughout the basin are SSE-NNW, simillar to the trend of the basin.

The basin was mapped in three horizons (approximately Eocene, Paleocene and basement). Structurally, the majority of the basin area is enclosed between two major faults trending NNW-SSE. Trapping styles identifed in the area are fault-related and can be defined as either tilted fault-blocks or fault-bounded anticlines (RRI, 1989).

The aim of image fusion is to integrate different data in order to obtain more information than can be derived from each of the single sensor data alone (Pohl and Van Gendren, 1998). Image fusion is performed at three different processing levels according to the stage at which the fusion takes place. These are: pixel, feature and decision level. It aims at the integration of disparate and complementary data to enhance the information apparent in the images as well as to increase the reliability of the interpretation (Pohl and Van Gendren, 1998). In this study, the objective of image fusion is to sharpen the image by increasing its spatial resolution by merging panchromatic band with Landsat TM bands and to reveal the subsurface structures by integrating satellite images with aeromagnetic data.

2. Data Types

The analysis of multi-source remotely sensed data can provide rich, reliable and useful information. Two data types were used in this study: remote sensing data: 5 TM and 5 ETM scenes (see table 1) and aeromagnetic data. Aeroagnetic data for Melut Basin comprised mainly of 1km grid of total magnetic field. This data have been prepared by GETECH in the year 2000 as a part of the African Magnetic Mapping Project (AMMP).

Table (1)

Remote sensing data types

Image	Date	Image	Date
TM 171-54	13-01-1987	ETM 171-54	06-05-2002
TM 172-53	17-01-1986	ETM 172-53	03-02-2001
TM 172-54	17-01-1986	ETM 172-54	12-09-2000
TM 173-53	18-11-1984	ETM 173-53	04-11-1999
TM 173-54	08-01-1986	ETM 173-54	24-12-2000

3. Digital Image Processing:

3.1 Pre-processing

Digital image processing techniques were performed with special emphasis on spatial enhancements to extract lineament features within the study area. Image pre-processing was carried out including radiometric and geometric corrections. All bands were transformed into UTM coordinate system. Mosaics for the different multispectral bands and panchromatic band were prepared. These mosaics were then subset to portray Melut Basin. All images were resampled to a common grid size using nearest neighbourhood resampling method. The aeromagnetic data were transformed to raster format and registered to the satellite data.

3.2 Image fusion

After having transformed the dataset into the same coordinate system, the images were fused to produce images which have better spatial and spectral characteristics. To this end, three approaches have been utilized:

3.2.1 Multiplicative

A first method to consider is the multiplicative technique. This technique requires several chromatic components and a multiplicative component, which is assigned to the image intensity. In this study, the chromatic components are multispectral TM bands; the panchromatic or aeromagnetic image is input multiplicatively as intensity.

3.2.2 IHS transforms

This is a common technique that uses the RGB to IHS transforms. In this technique, an RGB color composite of bands (or band derivatives, such as ratios) is transformed into IHS color space. The intensity component is replaced by the grey image, and the scene is then reversely transformed. This technique integrally merges the two data types (Erdas field guide, 1999).

3.2.3 Principal Component Analysis (PCA)

The principal component analysis is a statistical technique that transforms a multi variate inter-correlated dataset into a new uncorrelated dataset (Zhang, 2002). The purpose of PCA is to compress all of the information contained in an original n-band dataset into a fewer than n “new bands” or components.

4. Results and Discussions

In order to achieve the objectives of this study, fusion of both panchromatic band of ETM+ sensor with TM bands and Landsat images with aeromagnetic data were utilized using resolution merge and sensor merge functions, respectively.

Panchromatic images of Landsat ETM+ sensor has one broad band with very good spatial resolution —14.25 m. TM sensors have six bands with a spatial resolution of 28.5 m. Combining these two images to yield a six-band data set with 14.25 m resolution provides the best spatial and spectral characteristics of both sensors.

In the first step, fusion of panchromatic and TM bands was performed using mosaics of either the images that portray the entire area of Melut Basin. The three techniques mentioned above were utilized. Plates 1, 2 & 3 show the results of this process.

The second step includes fusion of aeromagnetic data with satellite images that serves to reveal subsurface structures in the study area. Results of this step are shown in plates 4, 5 & 6 utilizing the same techniques mentioned above.

From plates 1, 2 & 3 it is clear that the resolution of Landsat images was increased to 14.25m. The sharpness of these images was also increased which in turn enables the successful delineation of lineaments within the covered area. On the other hand, the spectral characteristics of TM images were degraded greatly when fused with aeromagnetic data (see plates 4, 5 & 6). This may be due to clustering of the majority of aeromagnetic data in a narrow range of values while the rest is spreading in a relatively wide range of values. This opinion is supported by the fact that this distortion is not homogenous all over the area. This may conclude that even though when the histogram of the aeromagnetic data was rescaled to match the histogram of the intensity component or PC1 of the TM images, the internal distribution variations still influence the quality of the resulting image. This disadvantage came at the expense of exposing the subsurface structures which takes an interest in the current study.

In the final step, all the produced images from fusion of different data types applying different merging techniques were imported in GIS environment. Then lineaments were interactively delineated using one image at a time. By the end of this step a lineament map of the study area was produced (see fig. 1). This map may be used as a guide when exploration works are to be conducted. This map was further divided into 5 zones according to perspectivity. Zone 1 is the most perspective followed by zone 2. Zone 3 and 4 are moderately perspective while zone 5 is less perspective.

fig. (1): Lineament map of Melut Basin

5. Conclusions

Lineament features are of great importance in Melut Basin since trapping styles are fault related. Mapping of lineamnets using satelite images and geophysical data utilizing fusion techneques was performed. The advantages and shortcoming of fusion of different satelite images and aeromagnetic data were discussed. The produced lineament map of the investigated area is of high quality and may be used as a guide in exploration works.

References

1. ERDAS field guide, 1999. On-Line Manuals version 8.4, Atlanta, Georgia.

2. Pohl, C. and Van Genderen, J. L., 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, Vol. 19, pp. 823-854.

3. Ropertson Research International (RRI) and Geological Research Authority of Sudan (GRAS), The Geology and Petroleum Potential of southeastern Central and Eastern Sudan (Unpublished report) 1989.

4. Zhang Y. 2002. Problems in the fusion of commercial high-resolution satelitte images as well as landsat 7 images and initial solutions. International Archives of Photogrammetry and Remote Sensing (IAPRS), Volume 34, Part 4 “GeoSpatial Theory, Processing and Applications”.