Manual Interpretation of Ibadan Area

8/6/2019 Manual Interpretation of Ibadan Area

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BY

AREOLA, MODUPE OLUWAFUNMI

(109082025)

Being a seminar paper presented to the Department of Geography on GRY 811

(Manual Image Analysis) in partial fulfillment of the requirement of M.Sc.

Geography (Remote Sensing)

School of Postgraduate Studies

University of Lagos

Course Examiner; Dr. M. J. Fasona

April, 2011.


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1.0 INTRODUCTION

According to Janssen & Huurneman (2001), the most intuitive way to extract information from remote

sensing images is by visual image interpretation, which is based on mans ability to relate colours and

patterns in an image to real world features. For many, sensor data often seem to be abstract and foreign

because of their unfamiliar overhead perspective, unusual resolutions and the use of spectral regions

outside the visible spectrum. As a result, the effective use of sensor data requires analysis and

interpretation to convert data to information for addressing practical problems.

Humans have innate ability to derive meaning from the complex patterns and colors that form certain

images (Campbell, 1996). At another higher level, they learn to derive meaning beyond mere

recognition of objects to interpret the arrangement of figures and subtle differences in posture, and to

assign meaning not present in the arbitrary pattern and colors. Thus, the image tells a story it conveys

a meaning that can be received only by observers who can understand the significance of the patterns,

shapes and colors.

1.1 ELEMENTS OF IMAGE INTERPRETATION

From this, it can be deduced that there are certain factors that describe the characteristics of objects and

features as they appear on remotely sensed images. A systematic study of aerial photographs and

satellite imageries usually involves several characteristics of features shown on an image. The

following characteristics (elements) are called fundamental elements of image interpretation. These

elements aid visual interpretation process of aerial photos and/or satellite imagery.

1.11 Tone

Tone refers to the colour or reflective brightness of objects and features on sensor images. Ground

objects of different colours reflect the incident radiation differently depending upon the incident wave

length, physical and chemical constituents of the objects. The imagery as recorded in remote sensing is

in different shades or tones. For example, ploughed and cultivated lands record differently from fallow

fields. Tone is expressed qualitatively as light, medium and dark. In SLAR imagery, for example, the

shadows cast by non-return of the microwaves appear darker than those parts where greater reflection

takes place. These parts appear of lighter tone. Similarly in thermal imagery objects at higher

temperature are recorded of lighter tone compared to objects at lower temperature, which appear of

medium to darker tone. Similarly top soil appears as of dark tone compared to soil containing quartz

sand. The coniferous trees appear in lighter tone compared to broad leave tree clumps.


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Tone along with texture and shadow (as described below) help in Interpretation and hence is a very

important key. Differences in moisture content of the soil or rock result in differences in tone. Tonal

contrast can be enhanced by use of high contrast film, high contrast paper or by specialized image

processing techniques such as 'Dodging' or 'Digital Enhancement'. Sometimes Infrared film can give

better contrast but it can also reduce resolution and loss of detail in shadows.

1.12 Texture

Texture is an expression of roughness or smoothness as exhibited by the imagery. It is the rate of

change of tonal values. It is dependent upon (a) image tone (b) shape, (c) size, (d) pattern and scale of

the imagery. Any slight variation of these can change the texture. Texture can qualitatively be

expressed as course, medium and fine. The texture is a combination of several image characteristics

such as tone, shadow, size, shape and pattern etc., and is produced by a mixture of features too small to

be seen individually because the texture by definition is the frequency of tonal changes. As an example,leaves of a tree are too small to be seen on an aerial photo collectively along with shadow they give

what is called texture, which in turn helps to differentiate between shrubs and trees. Texture sometimes

can be very important factor in determining the slope stability. In the case of a humid ground, the

blockage of water or bad drainage a characteristic texture results. Even spring and seepage of water

from the base of clay give a kind of' turbulent' texture. So is the case with mud flows. The term texture

is also, sometimes, used to denote drainage density and the degree of dissection of land surface.

1.13 Association

The relation of a particular feature to its surroundings is an important key to interpretation. Sometimes

a single feature by itself may not be distinctive enough to permit its identification. For example, Sink

holes appears as dark spots on an imagery where the surface or immediate subsurface soil consists of

lime stones, Thus the appearance of sink holes is always associated with surface lime stone formation.

An example is that of kettle holes which appear as depressions on photos due to terminal moraine and

glacial terrain. Another example is that of dark-toned features associated with a flood plain of a river,

which can be interpreted as infilled oxbow lakes.

1.14 Shape

Some ground features have typical shapes due to the structure or topography. For example air fields

and football stadium easily can be interpreted because of their finite ground shapes and geometry


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whereas volcanic covers, sand, river terraces, cliffs, gullies can be identified because of their

characteristics shape controlled by geology and topography.

1.15 Size

The size of an image also helps for its identification whether it is relative or absolute. Sometimes the

measurements of height (as by using parallax bar) also give clues to the nature of the object. For

example, measurement of height of different clumps of trees gives an idea of the different species,

similarly the measurement of dip and strike of rock formation help in identifying sedimentary

formation. Similarly the measurements of width of roads help in discriminating roads of different

categories I e. national, state, local etc. Size of course, is dependent upon the scale of imagery.

1.16 Shadows

Shadows cast by objects are sometimes important clues to their identification and Interpretation. For

example, shadow of a suspension bridge can easily be discriminated from that of cantilever bridge.

Similarly circular shadows are indicative of coniferous trees. Tall buildings and chimneys, and towers

etc. can easily be identified for their characteristic shadows. Shadows on the other hand can sometimes

render interpretation difficult i.e. dark slope shadows covering important detail.

1.17 Site factor or Topographic Location

Relative elevation or specific location of objects can be helpful to identify certain features. For

example, sudden appearance or disappearance of vegetation is a good clue to the underlying soil type or

drainage conditions.

1.18 Pattern

Pattern is the orderly spatial arrangement of geological topographic or vegetation features. This spatial

arrangement may be two-dimensional (plan view) or 3-dimensional (space). Geological pattern may be

linear or curved. Linear pattern are formed of a very large number of continuous or discontinuous short

ticks which when viewed by eye appear to be continuous lines. Examples of linear geological patternare faults, fractures, joints, dykes, bedding planes, anticlines etc. Examples of topographic pattern are

the typical drainage patterns (controlled and uncontrolled type). The uncontrolled types are those,

which are purely governed by topography, i.e., the slopes whereas the controlled types are those, which

are governed by the underlying geological formations.


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The well-known drainage patterns are: (i) Dendritic, (ii) Trellis, (iii) Annular, (iv) Radial, (v)

Rectangular, (vi) Parallel Type, (vii) Braided, (viii) Anastomotic, (ix) Asymmetrical, (x) Collinear.

1.20 THE STUDY AREA

Fig 1: Map of study area Source: Central Intelligence Agency (CIA) Date: 01 May 2003

1.21 Background Information and Population

Ibadan in Oyo State is an inland area in south-western Nigeria. Oyo state is bounded in the south by

Ogun State and in the north by Kwara State, in the west partly by Ogun State and partly by the

e study area


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Republic of Benin while in the east it is bounded by Osun State. It has a population of 6,617,720 (2005

estimate). Ibadan is the most populous city in black Africa with about two and a half million people.

Fig 2: Map of Nigeria showing population distribution Source: Encyclopedia Britannica Inc, 2001

Oyo State was created in February, 1976 and covers a total of 27,249 square kilometres of land mass.

1.22 Geography

Located at 800N 400E/ 8N 4E, the landscape consists of old hard rocks and dome shaped hills,

which rise gently from about 500 meters in the southern part and reaching a height of about 1,219

metre above sea level in the northern part. Some principal rivers such as Ogun, Oba, Oyan, Otin, Ofiki,

Sasa, Oni, Erinle and Osun Rivers take their sources from this highland. However, most of these are notshown due to the resolution of the satellite image.

1.23 Climate

Study Area


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The Climate is equatorial, notably with dry and wet seasons with relatively high humidity. The dry

season lasts from November to March while the wet season starts from April and ends in October.

Average daily temperature ranges between 25 C (77.0 F) and 35 C (95.0 F), almost throughout the

year.

1.24 Economic Activities

Agriculture is the main occupation of the people of Ibadan. The climate in the state favours the

cultivation of crops like maize, yam, cassava, millet, rice, plantains, cocoa, palm produce, cashew etc.

There is abundance of clay, kaolin and aquamarine. There are also vast cattle ranches at Saki, Fasola

and Ibadan, a dairy farm at Monatan in Ibadan and the state-wide Oyo State Agricultural Development

Programme with headquarters at Saki. A number of international and federal agricultural

establishments are located in the state.

1.3 ORGANIZATION OF WORK

According to Campbell (1996) the reporting format of an image interpretation task should take the

following outline; (1) objectives, (2) equipment and materials, (3) regional setting, (4) procedure, (5)

results, and (6) conclusions. In light of this, this work will be organized thus:

Objectives Equipment and materials

Procedure Results Interpretation Conclusion

2.0 OBJECTIVES

This work is aimed primarily at interpreting the satellite image of Ibadan and its surrounding area based

on the classification of the area as built-up areas, water bodies, agricultural land, bare land and forest.

Landsat ETM+

image dated August 2000 was used with necessary ancillary data. The report is

presented as a landuse/landcover map (Appendix). Each of these will depict the extent of each of the

identified land use of the study area.

In order to achieve this main objective, the following sub-objectives were drawn;


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i. To download the image of the study area from Global Land Cover Facility of University ofMaryland via the internet.

ii. To print the downloaded image on an A4 paper for easy manual image interpretation.iii. To use ancillary data and the downloaded image to determine the types and extent of landuse

and landcover in the study area.

iv. To generate a landuse/ landcover classification map of the area.

3.0 EQUIPMENT AND MATERIALS

For the acquisition, compilation, enhancement, processing and interpretation of the satellite image, the

following tools were used:

1. Pencils, ruler, Eraser and tracing paper2. Satellite image downloaded from http://www.landcover.org with spatial resolution of 30 x 30 m.

Three different bands - band 2 (Blue 0.525 0.605 m), band 4 (near IR 0.775 0.900 m) and band

7 (mid IR 2.090 2.350 m). The individual bands are shown in section 5. The radiometric

resolution of the bands is 8 bits, with temporal resolution of 16 days.

3. A HP Compaq nx6110 32 bit computer was used for the entire digital process of image acquisition,compilation and enhancement.

4. Idrisi 3.2 and Envi 4.7 software were used to compile and enhance the image bands into aninterpretable format.

5. ArcGIS9.3 software was used to digitize and to carry out some analysis on the image.6. Google Earth and Google Map provided ancillary data on the current land cover of the study area7. A4 paper for printing the composite image for the interpretation exercise, and another A4 for

interpreted map production.

4.0 PROCEDURES

The procedures used for this study are described below.

4.1 IMAGE ACQUISITION

As stated earlier, the satellite image of the study area was downloaded from the Global Land Cover

Facility of the University of Maryland via the URL: http://www.landcover.org.


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Using the ESDI option on the home page of the GLCF, the images were acquired through search for

places and selection of the exact Landsat typology (ETM+).

Three bands were downloaded as directed and the downloaded images were subjected to further image

compilation processes based on the usage of Idrisi 32 software of the Clark Laboratory.

4.2 IMAGE COMPILATION

Idrisi 3.2 software was used to combine the three bands, i.e. bands 2, 4 and 7 to produce a composite

image which is more suitable for interpretation.

4.3 IMAGE ENHANCEMENT

The composite image was further rectified using histogram and contrast stretching. In addition, the

composite image was destripped using the destrip tool in the image correction and enhancement

submenu.

4.4 DIGITIZATION AND COMPUTER ANALYSIS

After image enhancement, the image was imported to ArcMap environment for digitization. Ibadan and

surrounding area was cut out of the large image using the clip tool. Thereafter, using the Edit tool, the

various landuse classes were digitized into polygons, and annotated appropriately in the attributes table.

5.0 RESULTS

The Landsat ETM+ is a multispectral scanner (Jensen, 2007) that is sensitive to wavelengths within

0.45m and 0.9 m (7 bands, plus a new panchromatic band). Bands 2, 4 and 7 only were downloaded

and used in this exercise. Band 2 (Blue 0.525 0.605 m) spans the region between blue and red

chlorophyll absorption bands and reacts to the green reflectance of healthy vegetation. Band 4 (near IR

0.775 0.900 m) is very responsive to the amount of vegetation biomass and leaf area present. It is

useful for crop identification and emphasizes soil/crop and land/water contrasts. Band 7(mid IR 2.090

2.350 m) has the ability to discriminate geologic rock formations. The combination of these bands

produced the composite image (Fig 3).

Built areas appear in lighter colours, while water bodies appeared dark. Forests and vegetation appear

in various shades of green. The water bodies shown include some of the afore mentioned (section 1.22).

The results of the exercise are presented as a landuse/landcover classification map in the appendix. The

study area generally consists of built areas, water bodies, forests and vegetation. Due to the resolution


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of the image, other features such as streams, rock outcrops, inter-settlement roads and so on are not

shown. The interpreted image is shown below.

Fig 3: Composite image of study area

It is a composite of bands 2, 4 and 7 (Fig 4, 5 and 6).


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Fig. 4: Band 2 of the entire image of the study area


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Fig 5: Band 4 of the entire image of the study area

Fig 6: Band 7 of the entire image of the study area

The total area covered by each landuse is depicted in the table below.

Table 1: Landuses in the study area and their respective coverage areas

S/No Land Use

Class

Coverage Area

(m2)

Percentage of

Coverage Area (%)

1 Built-up area 1988.824 12.48086234

2 Fallow land 3452.872 21.66849704

3 Forest 5686.708 35.68693498

4 Wetland 37.83259 0.237418416

5 Water body 66.58257 0.417838924

6 Vegetation 4625.957 29.03019072

7 Riperian forest 76.21027 0.478257575

Total 15934.98 100


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7.0 CONCLUSION

The study area is a forested region with interspersed settlements, prominent among which is Ibadan, as

shown in the satellite image of the area. Other smaller farm settlements are seen shooting out to the

northeastern part of Ibadan. The forest in the area is predominantly mangrove, represented by the shade

of green in the image. The entire study area covers 15934.98m2, i.e. 15.93km

2; spanning 7 major

landuse classes in all.

From this, it can be inferred that the area is a forested region with a large built up area/settlement

Ibadan.A copy of the derived landuse map of the study area is attached as an appendix.

References


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"Jian Guo Liu, Philippa J. Mason (2009) Essential Image Processing and GIS for Remote Sensing for

John Wiley and Sons, UK.

"Reddy, M. Anji" (2008) Textbook of Remote Sensing and Geographical Information Systems 3rd

Ed.

BS Publications 4-4-309, Giriraj Lane, Sultan Bazar, Hyderabad

http://www.landcover.org

http--www_wordtravels_com-images-map-Nigeria_map_jpg.htm

Jensen, R.J. (2007) Remote Sensing of the Environment; An Earth Resource Perspective. Prentice Hall

Lucas L. F. Janssen & Gerrit C. Huurneman (2001) Principles of Remote Sensing---An introductory

textbook. The International Institute for Aerospace Survey and Earth Sciences (ITC), Enschede, The

Netherlands

Merriam-Webster, (2001) Geography Merriam-Webster Atlas

Central Intelligence Agency (CIA) 2001 www.cia.com

Manual Interpretation of Ibadan Area

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