The application of remote sensing technique to verify changes in landscape due to exploration activities.

Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol. 3, No.8, 2013
18
The Application of Remote Sensing Technique to Verify Changes
in Landscape due to Exploration Activities.
R. Ehigiator-Irughe1
, M. Eboigbe2
and M. O. Ehigiator3
1 GeoSystems and Environmental Engineering, Benin City, Nigeria
raphehigiator@yahoo.com
2 University of GlamorganPontypridd, United Kingdom
mitchayes@yahoo.com
3 Department of Physics and Energy,Benson – Idahosa University Benin City, Nigeria.
geosystems_2004@yahoo.com
Abstract
Detecting and analysing landscape changes is of great values firstly for environmental monitoring and secondly
for huge administrative purposes. This study, gives an insight on how remote sensing can be used to ascertain the
extent of mineral exploration in the said locality. Many remote sensing methods are presently adopted for
analysing multi-temporal changes but the approach used here is simple and could be interpreted by anyone. The
first approach is visual interpretation of two remotely sensed images after all correction and enhancement
supported by manual digitized polygons and the second is the variations in the spectral attributes of selected
features after image sharpening. The last approach used is the unsupervised classification of the selected features
whose result is in consonance with the initial analysis conducted. The result shows that remote sensing is not
merely scientific manipulations but also it is the realities of our daily human activities.
Keywords: Structural Deformation, Kinematic, Kalman Filter
1.0 Introduction:
Over the last two decades, Remote Sensing approach in accessing and addressing environmental issues have
improved so much. The visual interpretation of several spectral characters is a possible way of interpreting
geological features every band in the electromagnetic spectrum depicts a certain character [1]. Multi temporal
radiometric rectification with a physically based reflectance analysis is practically adequate to interpret features
as covered in the satellite imageries after all radiometric control spectral and spatial enhancement. Temporal
changes in any physical domain exert the propensities to depict its features according to the influences as exert
by reflections and absorptions [2].
For this study, Auchi in Edo State, Nigeria area was used. This areas under review are basically surrounded by
heavy deposit of limestone and other mineral resources. There is both legal and illegal exploitation of these
resources’ especially the open cast blasting of limestone at the detriment of the communities in these areas.
There are over 12 communities all on heavy mountainous terrain with so many companies including not licensed
doing blasting. The resultant effect is a menace which calls for an urgent review.
Fig 1.0: Map of Nigeria Showing Edo state

Vol. 3, No.8, 2013
19
2.0 An Overview of the Study area:
Since it was not possible to go to the study site, the goggle earth was used as an instrument in achieving the
unsupervised classification. The land cover features of interest are: Areas of opencast blast, Settlements, Existing
Roads and Rocks.
These features were identified on the Google earth and their geographical co-ordinates picked. Prominent point
like the river Niger was also identified to be used for visual interpretation. Unsupervised classification is based
mainly on the clustering ability of the Erdas Imagine software based on already identified features.
Fig 2.0: Download of Satellite Imagery:
Obtaining the satellite imagery for this area was actually a problem. I had to place an order from the USGS
website which took three days to come. Two imageries used for the study are 1987 and 2001 respectively.
Coincidentally, 1987 is about the period industrial activities actually commenced in the study area. The images
were first copied to desktop and a new folder created in my university space.
Fig 3.0 Satellite Image of the study area for year 1987, 2001
3.0 Image processing
Stack Laying was used to add the bands into one image. Images were left as dynamic subset to have a clearer
view. In the image reduction, Haze reduction was used to bring out the particular area of interest as shown below:
Fig 3.2: Reduced Image of the study for 2001 and 1987
3.1 Image Correction and Enhancement
This is to ensure that both satellite images are interpretable, making the areas of interest more visible for
visualization. This is performed such that the areas of open cast blast are clearly understood. The characteristics
of imaging remote sensing instruments operating in the visible and infrared spectrum region can be summarized

Vol. 3, No.8, 2013
20
in terms of their spatial, spectral and radiometric resolutions.
Fig. 4.0 Spectral Profile and Histogram for 2001 Satellite Image
The spectral value is the wavelength interval which helps to identify features through colour separation in the
electromagnetic spectrum. It denotes the frequency of change of an object. Rebecca et al [3].
Fig 4.1 metadata for subset 2001 Image
Spectral profile, histogram and the metadata of layer 1 of the 2001 image showing the spectral properties of the 4
selected features in the study area on layer one.
3.2 Spatial Value:
This is the degree of change in the image DN value as relative to distance. It enables us to interpret the linear
separation between pixels as they are in the real life perspective which will help me to differentiate between the
areas of the open cast blast from other areas. Band 1 always had the highest values in all the features.
Figure 4.2: Spatial Profile for the open Cast Blasting Area
The original band combination of 4:3:2 (RGB) false colours were changed to 5:3:2. Increasing the Red by one
band is to bring out the area of blast and rocks. The only spatial enhancement tool applied is the quick low-pass
filter to reduce the high frequency noise. This is true because we analyse features with wide margin in contrast
Ryo, Zhiben and Bing [4]. The was no any Normalised Difference Vegetation Index as all the vegetation is
already covered by open cast blasting and rocks.

Vol. 3, No.8, 2013
21
3.3 Visual Image Interpretation
From the digitized polygons and the side by side link of both images above, the following inferences is deduced.
There is no evidence of industrial activities as at 1987 and Auchi town is expanding toward the North – east.
Areas of open cast blast appear as white on both satellite images and confirmed using coordinates from the
Google earth. There is clear evidence of settlements right inside the areas of open cast blasting. What looks like
yellow inside Auchi town in the 2001 and whitish in the 1987 is actually a mining site. This implies that as at
1987, mining was already going on in Auchi town. Arrows shows area characterised with mining acticities.
Figure 4.3: Visual Image
3.4 Image Sharpening
To investigate the fact presented above, image sharpening was carried out which helps to enhance the temporal,
spatial, radiometric and spectral qualities of features. We performed the IHS –RGB transformation as it helps to
reveal more details not easily understood in the previous image formats. Compares is made between the
transformed image and the original image.
Figure 4.4: Image sharpening
It is very obvious that the area of interest (open cast blast) is evidently more pronounced on these false colours.
These areas now appear as whitish yellow against the red background. The 2:3:4: layers were layed.
3.5 The Unsupervised Classification
Digital numbers of features gives similarity according to their spectral classes. Results from the unsupervised
classification approach is estimated from the categorization of land cover features; thereby rendering it for
possible evaluations Sylvie [5]
Both the gray and the approximate true colours for both years were supervised. For 2001, 4:3:2 on 4 classes on
gray and 5:3:2 on 6 classes for approximate true colours. 1987 has 4:3:2 combinations on 4 lasses and 6 classes
respectively.

Vol. 3, No.8, 2013
22
Figure 4.5: 1987 Image on greyscale colour scheme
Figure 4.6: 1987 grey scale colour on initial colour combination.
Figure 4.7: 2001 Image Grey Scale/ Approximate True Colour

Vol. 3, No.8, 2013
23
Figure 4.8: Compares on both years on their approximate true colours.
3.6 Discussion
In 1987, there is an almost clustering of features out of the spectral order as compared to the 2001. In closeness,
the colour signature indicates it is not as a result of contrast or variations in the colour combination. This analysis
would have been clearer assuming the Landsat 7+ was used for both years. Even with the band difference, there
is still clarity in the features display but a sharp variance on their surface properties. This further proves that a
very big change has occurred in the area of study. Also in 2001, there is an inward clustering which depicts the
fact that there is common activity going on. We all know that in areas of open cast blast, there are always
particles in the air. The colour of this particle depends on the kind of material been removed. At this instance,
limestone is very whitish which has an overwhelming effect in the neighbouring community. The unsupervised
classification has also confirmed the mean spectral values as initially displayed before all corrections and
enhancement. Despite this success by the unsupervised classification, we still strongly believe that it did not give
a strong deviation between the mining areas from other surfaces. In future I hope to carry out this study again
with the following inputs:
Conclusion
From all the results as obtained above, it is now very clear that open cast blast activities in the study area is on a
high rate. We have been able to show too how close these activities are to human settlements. Judging from the
unsupervised classifications, there are likely hood that these substances remitted into the air can have a
proportional interface with any close surface.
Reference:
Christopher Small (2002) ‘Multitemporal Analysis of Urban Reflectance’, Remote Sensing of Environment, 81
(2002), pp.427-442, Elsevier (online). Available at: www.elsevier.com/locate/rse (Assessed: 03 April 2012).
Paul M. Mather (2004) Computer Processing of Remotely Sensed Images. England: John Wiley and Sons Ltd.
Rebecca L. Powell, Dar A. Roberts, Philip E. Dennison, and Laura L. Hess (2007) ‘Sub-Pixel Mapping of Urban
Land Cover Using Multiple Endmember Spectral Mixture Analysis: Manaus, Brazil’, Remote Sensing of
Environment, 106(2007), pp. 253-267, Elsevier (online). Available at: www.elsevier.com/locate/rse (Assessed:
29 March 2012).
Ryo Michishita, Zhiben Jiang and Bing Xu (2012) ‘Monitoring Two Decades of Urbanization in the Poyang
Lake Area, China through Spectral Unmixing’, Remote Sensing of Environment, 117 (2012), pp. 3-8, Elsevier
(online). Available at: www.elsevier.com/locate/rse (Assessed 03 April).
Stanislaw C. Mularz ‘Satellite and Airborne Remote Sensing Data for Monitoring of Open – Cast Mine’.
Commission IV Symposium on GIS- Between vision and Applications, Stuttgart,Germany.’ D.Fritsch,
M.Englich and M. Sester, eds, ‘IAPRS’, 34(4), PP. 395-402 (online). Available at:
isprs.org/proceedings/xxix/congress (Assessed: 01 May 2012)
Sylvie le Hegarat-Mascle, Isabelle Bloch, and D. Vidal-Madjar ‘, Application of Dempster- Shafer (1997)
Evidence Theory to Unsupervised Classification in Multisource Remote Sensing’ IEEE Transactions on
Geoscience and Remote Sensing, 35(4), pp. 1018- 1031, (online). Available at: perso.telecom-paristech.fr/-
bloch/papers/tgrs975sylvie.pdf (Assessed 05 May 2012)

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