Thesis Ebenezer KP

8/6/2019 Thesis Ebenezer KP

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Detection of Land Use and Land CoverChange in the Accra Metropolitan Area

(Ghana) from 1990 to 2000

Ebenezer Kwakye Bentum

Masters of Science Thesis in Geoinformatics

TRITA-GIT EX 07-013

School of Architecture and the Built Environment

Royal Institute of Technology (KTH)

100 44 Stockholm, Sweden

October 2009


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TRITA-GIT EX 07-013

ISSN 1653-5227

ISRN KTH/GIT/EX--07/013-SE


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ACKNOWLEDGEMENT

Getting something done is an accomplishment but getting it done well is an achievement. I

would like to take this opportunity to express my sincere and profound gratitude to the entire

staff of the Department of Geodesy and Geoinformatics at Royal Institute of Technology for

the training they have given me throughout the entire studies. Special thanks to Dr. Hans

Hauska (Assoc. Prof.) and Dr. Yifang Ban (Prof.) for their able assistance, patience and

constructive criticism without which the work wouldn't have been successful. I am indebted to

the entire staff of Geomatic Engineering, KNUST, Kumasi, Ghana for giving me the basic

training and knowledge which motivated me for further studies. I appreciate the help of

Emmanuel Tetteh and Eric Kofi Forson, both at Centre for Remote Sensing and GIS,University of Ghana, Legon. I am also highly indebted to my mother for the financial, moral

and spiritual support throughout my studies. I would like to express my appreciation to Alfred

Awotwi, KTH for generating the regression equations (using PCI Geomatica) which were

used in normalising the satellite images used in the analysis. Last but not least, I wish to

express my sincere and profound gratitude to all my friends for their encouragement and

support; especially Amos Atakorah Mensah, Alfred Awotwi, Daniel Dennis Konadu of

University of Oxford and Joseph Sakyiama Afari of University of Westminster, UK. Finally,

my deepest appreciation goes to the Almighty God for his continuous benevolence,

sustenance of life, provision of wisdom, guidelines and knowledge throughout my studies.

Stockholm, Sweden, October 2009

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ABSTRACT

There has been rapid change in the land use and land-cover types in Accra, Ghana in the past

decade. The major change is the conversion of agriculture and forest lands into urban areas

mostly in an un-planned manner making urban sprawl characterising the urban change

dynamics. Land use change has been the reason for many social, economic and environmental

problems in Accra, the capital city of Ghana over the past decade. This has engaged many

researchers to find out possible measures to address and monitor this phenomenon. The

ultimate objective of the research is to detect the land use/land-cover change of Accra from

1990 to 2000. Satellite images of Accra at two different periods, 25/12/1990 and 04/02/2000

were analysed. Two change detection techniques namely post classification comparison

(indirect method) and image-to-image comparison change detection (direct method) were

employed. The different change detection techniques were also evaluated. In the indirect

method, both supervised and unsupervised classifications were performed. The supervised

classification proves to be better than the unsupervised classification with accuracies of

85.62% and 89.1% for 1990 and 2000 classified images are respectively. Post-classification

comparison change detection was conducted to reveal the areas that have changed over the

decade. In this method, the from-to-change informational classes were available. The results

revealed drastic growth of urban areas and reduction of agriculture and forest lands over the

decade. Overall accuracy for both change/no-change trajectories was found to be 74.63% with

kappa index of 0.698. Although the classes were being detected correctly as change/no-

change area, some of the change trajectories do not necessarily match the corresponding real

cases. The change detection procedure, however, was able to identify the areas of significant

change. In the direct change detection method (image-to-image comparison), three different

techniques were used and evaluated. The three methods are: normalised difference vegetation

index, principal component analysis and TassCap transformation. The accuracies for detecting

the changes in the urban area for the indirect methods used are 79.51%, 82.32% and 70.13%

respectively with kappa indexes are 0.715, 0.753 and 0.698. The seasonal variation of the two

satellite images used in the analysis affected the spectral resolution which subsequently

affected the change detection process. Because of the variation of the temporal resolution and

other environmental factors, the same land cover class can have different radiance values

between the images. It can be concluded from the resulting statistics that the image-to-image

change detection was more accurate that the post-classification comparison. The land-

cover/land-use classes classified are closed vegetation, open vegetation, dense herbaceous

cover, grass, urban/bare areas and water bodies. The percentage change in the land coverclasses was found to be 56.4%, 64.07%, 28.7%, 25.61%, 59.34% and 3.8% respectively.

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TABLE OF CONTENT

ACKNOWLEDGEMENT .................................................................................................. iiiABSTRACT ......................................................................................................................... iiLIST OF TABLES................................................................................................................ vLIST OF FIGURES ............................................................................................................ vi1 INTRODUCTION............................................................................................................. 1

1.1 Background .................................................................................................................. 11.2 Problem statement ........................................................................................................ 21.3 Objective ...................................................................................................................... 31.4 Outline of sections/organisation of the thesis ................................................................ 3

2 OVERVIEW OF URBAN SPRAWL, LAND COVER/LAND ........................................ 4USE CHANGE AND CHANGE DETECTION .................................................................. 4

2.1 Definition of urban sprawl ............................................................................................ 42.2 Characteristics of sprawl .............................................................................................. 5

2.2.1. Single use zoning .................................................................................................. 52.2.2. Low - density land use .......................................................................................... 52.2.3 Car dependent communities ................................................................................... 52.2.4 Leap frog development .......................................................................................... 6

2.3 View of Urban sprawl ................................................................................................... 62.4 Causes of urban sprawl ................................................................................................. 62.5 Effects of sprawl .......................................................................................................... 72.6 Difference between land use and land cover ................................................................. 72.7 Usefulness of remote sensing technology in land use change studies ............................ 7

2.7.1 Definition of change detection ............................................................................... 82.7.2 Previous works on change detection worldwide ..................................................... 82.7.3 Overview of Change detection techniques ........................................................... 122.7.4 Previous work on change detection in Ghana ....................................................... 15

3 STUDY AREA AND DATA DESCRIPTION ................................................................. 18

3.1 Description of the study area ...................................................................................... 18

3.1.1 Boundary and Administrative Area ...................................................................... 183.1.2 Land use and land cover types ............................................................................. 19

3.2 Potential users and stakeholders ................................................................................. 203.3 Data Collected ............................................................................................................ 20

4 METHODOLOGY FOR LAND USE/LAND COVER CHANGE................................ 23DETECTION ..................................................................................................................... 23

4.1 Flow chart for the methods employed ......................................................................... 234.2 Pre-processing of the multiple date remotely sensed data: ........................................... 244.3 Image classification .................................................................................................... 244.3.1 Unsupervised classification.................................................................................. 26

4.3.1.1 Clustering to detect the land use classes ........................................................ 274.3.1.2 Aggregation of clusters ................................................................................. 27

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4.3.2 Supervised Classification ..................................................................................... 284.3.2.1 Training Sites Development .......................................................................... 284.3.2.2 Signature Development ................................................................................. 29

4.3.2.3 Maximum Likelihood Classifier........................................................................ 294.3.2.4 Classification accuracy assessment ............................................................... 30

4.3.3 Post classification comparison change detection .................................................. 314.3.3.1 Detecting from-to change using Cross Classification and Tabulation........... 324.3.3.2 Detecting and Visualization of change in urban areas by colour coding ......... 324.3.3.3 Change Map ................................................................................................. 324.3.3.4 Change detection accuracy ............................................................................ 33

4.4 Image Differencing..................................................................................................... 334.4.1 Radiometric normalisation ................................................................................... 344.4.2 Image Enhancement ............................................................................................ 354.4.3 Image-to-image comparison (Differencing) ......................................................... 35

4.4.3.1 Normalised Difference Vegetation Index ....................................................... 354.4.3.2 Principal Component Analysis ...................................................................... 364.4.3.3 Tasselled Cap transformations (TASSCAP) ................................................... 37

5 RESULTS AND DISCUSSIONS..................................................................................... 385.1 Unsupervised classification ........................................................................................ 38

5.1.1 Clustering ............................................................................................................ 385.1.2 Aggregation of clusters ........................................................................................ 38

5.2 Supervised classification ............................................................................................ 415.3 Classification accuracy assessment ............................................................................. 425.4 Post-classification comparison using supervised classification .................................... 48

5.4.1 Detecting and Visualization of from-to changes by colour coding ...................... 515.4.2 Change/Non-Change map .................................................................................... 535.4.3 Change detection accuracy assessment ................................................................ 53

5.5 Image Differencing.........................................................................................................56

5.5.1 Radiometric Normalisation .................................................................................. 575.5.2 Normalised Difference Vegetation Index (NDVI) ................................................ 59

5.5.2.1 Performance and limitations of NDVI ........................................................... 605.5.3 Principal Component Analysis (PCA) .................................................................. 625.5.4 TASSCAP transformations................................................................................... 64

5.6 Change areas accuracy assessment ............................................................................. 665.7 General discussions .................................................................................................... 67

5.7.1 Closed vegetation cover ....................................................................................... 695.7.2 Open vegetation cover ......................................................................................... 69

5.7.3 Dense Herbaceous cover ...................................................................................... 695.7.4 Grass/herb ........................................................................................................... 695.7.5 Built up/bare area ................................................................................................ 705.7.6 Water ................................................................................................................... 70

6 CONCLUSION AND RECOMMENDATIONS ............................................................ 716.1 Conclusion ................................................................................................................. 716.2 Recommendation........................................................................................................ 72

REFERENCES .................................................................................................................. 73

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LIST OF TABLES

Table1: Number of pixel selected in each land-cover category for training ..........................29

Table 2: Correlation between the bands and principal components........................................37Table 3: Error matrix for 1990 land use map of of Accra (Unsupervised)..43

Table 4: Error matrix for 2000 land use map of Accra (Unsupervised)......44

Table 5: Error matrix for 1990 land use map of Accra (Supervised). ....45

Table 6: Error matrix for 2000 land use map of Accra (Supervised)......46

Table 7: Producers and users accuracy in percentage for unsupervised

Classification..........................................................................................................................47

Table 8: Producers and users accuracy in percentage for supervised classification................47

Table 9: Cross tabulation results for classified images, 1990 and 2000.................................49

Table 10: Percentage Change of the land-cover types from 1990 to 2000..51

Table11: Error matrix analysis of from-to-change................................................................55

Table 12: Producers and Users accuracies of the from-to change.........................................56

Table13: Change detection accuracies image-to-image comparison methods........................67


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LIST OF FIGURES

Fig. 1: Map of Accra metropolis showing the surrounding districts ..................................19

Fig. 2: Satellite image of Accra, 1990.................................................................................21

Fig. 3: Satellite image of Accra, 2000.................................................................22

Fig. 4: Flow chart for the methodology..............................................................................23

Fig. 5: clustering of 1990 image .......................................................................................38

Fig. 6: clustering of 2000 image..........................................................................................38

Fig.7: Land use map of Accra, 1990 from unsupervised classification...............................39

Fig.8. Land use map of Accra, 2000 from unsupervised classification...............................40

Fig.9: Land use map of Accra, 1990 from supervised classification...................................41

Fig.10: Land use map of Accra, 2000 from supervised classification.................................42

Fig.11. Cross classification image for transition from 1990 to 2000..................................48

Fig.12. Transformation of open vegetation-cover into urban areas ....................................50

Fig.13. Change in urban areas from 1990 to 2000..............................................................52

Fig 14: Change/Non-Change map.......................................................................................53

Fig15: Scatter plot for 1990 band2(y) and 2000 band2(x)..................................................57



Fig18: Normalised difference vegetation difference for 1990 image..................................59

Fig19: Normalised difference vegetation Index for 2000 image.........................................60

Fig.20: Change in vegetation/Non-change area...................................................................62

Fig. 21: Principal component transformation for 1990 image.............................................63

Fig. 22: Principal component transformation for 2000 image.............................................63

Fig. 23: Difference in the second Principal components.....................................................64

Fig. 24: Differencing in the brightness value between the two images...............................64

Fig.25: Difference in greenness value between the two images..........................................65

Fig.26: Differencing moistens (Change/non-change map from differencing moistens

layers....................................................................................................................................66

Fig.27. General Comparison of Area occupy by Land cover categories in Accra...............68


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1 INTRODUCTION

1.1 Background

In recent times, many decision makers, government bodies and agencies, urban planners,

environmentalist etc., have expressed much concern about changes in land use/ land cover

change dynamics. Expansion of urban areas is currently a ubiquitous phenomenon in

developing countries of Africa. The region has the highest rate of urbanization in the

world, though unaccompanied by economic growth (World Bank, 1995). Dramatic

acceleration in urban growth and the associated ecological footprint (Rees, 1992) have

serious implications for land-cover change on the one hand, and sustainability of urban and

peri-urban livelihoods on the other. This phenomenon has engaged many researchers to

investigate into the possible causes, its effect and pragmatic measures to alleviate its

occurrence. There are many driving forces contributing to this phenomenon which could

be social, economic or geographic in nature. In most cases, agriculture lands are being

converted to residential, industrial and commercial lands. The major land cover change as

described by many researchers is the expansion of the urban areas at the expense of

vegetated areas. The expansion of the urban areas is described as a quality residential

sprawl. Urban sprawl is sometimes used by some people to describe almost any growth but

this is misleading cited by Ademola et.al (2003).This phenomenon is usually associated

with the idea of unsuitable development. It depends on numerous simultaneous processes

which make it difficult to deal with. Its modelling appears then an interesting means to

understand and to run simulations according to different scenarios of urban development

for making planning decisions. These changes and their repercussions require careful

consideration by local and regional land managers and policy makers in order to make

informed decisions that effectively balance the positive aspects of development and itsnegative impacts in order to preserve environmental resources and increase socioeconomic

welfare. In order to understand the expansion of the urban fringe, its causes, effects and

measures to alleviate this pandemic, an interdisciplinary approach is needed. There is the

need to understand the possible causes, its effect on the environment, the economic and

social impacts as well. In order to simulate the land cover change dynamics, the following

should be considered:

(i) Classification of the land-cover categories

(ii) Quantification of the dynamics of the land-cover classes


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(iii) Detection of the change

These are associated to a spatio-temporal database using a grid to store the needed

information within the GIS environment and the available technology.

Land cover change is a dynamical temporal process which can be simulated and modelled.

Some models established with remote sensing technologies have been achieved by combining

time series and spatial analysis. With the advancement in technology, remote sensing and

Geographic Information System (GIS) prove to be important tools in addressing land cover

change dynamics phenomenon. The use of digital change detection is becoming increasingly

popular as means of studying changes in the landscape.

1.2 Problem statement

Accra, population 1,970,000 (2005) is the capital of Ghana and the country's largest city as

well. It is the administrative and economic centre. The primary economic activities are

financial and other services, agriculture, fishing and manufacturing such as lumber and

plywood, textiles, clothing and chemicals (Wikipedia, 2007). Previous researches on the

nexus of global and local forces suggest that African cities such as Accra and Dar-esSalaam

are experiencing new forms of settlement. For Accra, the new form has been described as a

quality residential sprawl with centric tendencies. Demographic and housing data confirm the

emergence of this form (Ghana Statistical Service, 2000). This development is significant to

city planners and managers, not just in Accra but in other African cities as well.

Accra is rapidly expanding within the last few decades and this expansion is characterised by

sprawl which poses many environmental, economic and other related social issues. The urban

sprawl is creating many problems in almost all spheres of human endeavour. In order to

understand the causes, effects and possible measures to alleviate its occurrence,

interdisciplinary approaches should be combined. Ghanas Population census 2000 indicates

43.8% urban dwellers, as against 9% in 1931. The growth rate is 2.6% per annum, and the

urban population of Accra is expected to double in 17 years (Ghana Statistical Service, 2000).

The metropolitan area alone represents 25% of all urban dwellers in Ghana with an increasing

rate of 4.2% per annum, (Ghana Statistical Service, 2000). In order to simulate the past,

present and the future states of the sprawl, there is the need to integrate many technologies

like Geographic Information System (GIS) and Remote Sensing Technology which will

provide an optimal trade-off between reliability, accuracy, time and cost of modelling.


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1.3 Objective

The objective of this research is the detection of the land use/cover change in Accra. Some of

the sub objectives are:

Classification of the land cover categories

Quantification of the dynamics of the land cover classes

Detection of the change/no-change areas

Evaluation of the different change detection methods and techniques

1.4 Outline of sections/organisation of the thesis

Chapter one of this report deals with the general introduction, which includes the background

to the project, problem statement and objective of the study.

Chapter two is the overview of urban sprawl, land use/cover change and previous work done

on change detection.

Chapter three deal with study area and data description.

Chapter four extensively deals with the methodology employed in image classification and

digital change detection.

Chapter five focuses on the results and analysis.

Chapter six provides conclusion and recommendations.


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2 OVERVIEW OF URBAN SPRAWL, LAND COVER/LAND

USE CHANGE AND CHANGE DETECTION

'Urban sprawl' has recently become a subject of popular debate and policy initiatives from

governmental bodies and nonprofit organizations. With the advancement in technology, the

phenomenon could be simulated and modelled so that policy formulation and implementation

regarding urban planning could be made. This chapter will highlight on the above things discussed.

2.1 Definition of urban sprawl

Many organizations have attempted to provide a definition, but it turns out that the definition

depends on that organizations perspective, usually polarized between a pro-growth and an anti-

sprawl viewpoint. Below are definitions from various organizations as stated by centre for land use

education (2003)

The Heritage Foundation: Sprawl simply refers to the low-density, residential development

beyond a citys limits.

Reason Public Policy Institute: Many people think sprawl is synonymous with

suburbanizationAnother way of characterizing this process is thinking of sprawl as the

transitional period between rural and urban land use.

National Trust for Historic Preservation, Rural Heritage Program: Sprawl is dispersed, low-

density development that is generally located at the fringe of an existing settlement and over

large areas of previously rural landscape. It is characterized by segregated land uses and U.S.

Environmental Protection Agency: [Sprawl is a] pattern of growth [that] has largely

occurred in an unplanned, ad hoc fashion.

The Sierra Club: Sprawlscattered development that increases traffic, saps local resources

and destroys open space.

Natural Resources Defence Council: Sprawling development eats up farms, meadows and

forest, turning them into strip malls and subdivisions that serve cars better than people.

Combining all these definitions, urban sprawl could be defined as rapid and expansive growth of

a greater metropolitan area and traditionally suburbs over a large area. The term has been used by

some critics to describe almost any urban growth but in reality that is not the case. The usage thus


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sometimes tends to be misleading. Urban sprawl could occur as a result of urban growth but the

converse may not be necessarily true.

2.2 Characteristics of sprawl

There are different types of urban sprawl. The type of sprawl determines the characteristics. The

various definitions for urban sprawl suggest that there are different characteristics of sprawl. The

most common ones as cited in Wikipedia (2007) are;

single use zoning

low density land use

car dependent communities

Leap frog development

2.2.1. Single use zoning

This is the type of sprawl whereby industrial, commercial and residential are separated from one

another, Wikipedia (2007). Large tracts of land are devoted to the same type of development. The

various zones are separated from each other by either roads, green spaces or any other type of

physical barrier. As a result, the places where people live, work, shop and recreate are necessarily far

apart from one another. In Accra, the single use zoning tends to be inferior.

2.2.2. Low - density land use

In this case, sprawl consumes much more land than traditional urban development because new

developments are of low density. Single family houses are mixed up with apartments. Buildings

usually have fewer stories and are spaced further apart separated by lawns, landscaping roads,

parking lots or pavement etc., Wikipedia (2007). In these areas, urbanised land is increasing at faster

rate than the population. Sometimes the term leap-frog development may be used. Suchdevelopments are typically separated by large tracts of undeveloped land, resulting in a very low

average density. This is because of the current policies involved in developing parcels of land whereby

users may be asked to set aside some portion of the developed land for some public use. But its not

common in Accra.

2.2.3 Car dependent communities

Areas of urban sprawl are sometimes characterised as being dependent on auto-mobiles, motorbikes


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and bicycles for transportation. Most activities such as shopping, commuting to work, concerts etc.

require the use of a car because of isolation of residential and commercial zones. In Accra the areas

that are quite away from the city centres like Pokuase exhibit this type of sprawl character.

2.2.4 Leap frog development

This type of sprawl means jumping from a built up area over an open space to another built up area.

That is a new development that does not follow a planned, orderly development pattern and that

jumps to areas outside of where services are readily available. This type of development is not

common in Accra.

2.3 View of Urban sprawlSeparation of land used for different purposes is a typical characteristic of urban sprawl.

Sprawl has physical separation of space used for different purposes such as housing subdivisions,

shopping centres, office parks, civic institutions and road-networks etc. Urban sprawl is perceived as

economically inefficient, environmentally irresponsible and aesthetically ugly. Bruegmann (2004)

calls it a logical consequence of economic growth and the democratization of society, with benefits

that urban planners have failed to recognise. People have different perception about the sprawl. Some

think sprawl has enabled man to satisfy his social, economic and environmental needs while othersthink it is generating unnecessary tension on land use. This has raised a lot of debates about urban

sprawl phenomena.

2.4 Causes of urban sprawl

Sprawl is occurring as a result of response to social, economic forces and to the physical geography

of an area. Some of these factors as described by Wassmer and Edward (2005) are:

population growth

strong economy in cities (while weak in countryside)

increasing household income in the inner city

fragmented municipal governments

increased infrastructure cost

decrease in social capital

patterns of infrastructure investments


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topographic barriers and other physical constraints upon development

2.5 Effects of sprawl

Sprawl has both favourable and unfavourable impacts. Considering the social impacts, sprawl

contributes in providing housing opportunities and affordability to minorities. But it has some social

disadvantages as described by Wassmer and Edward (2005) which include lost of community spirit,

values, higher taxes etc. Its impact on ecosystems and environmental resources is considerable.

Sprawl tends to degrade environmental resources such as air quality, landscape aesthetics and

destroys wildlife habitats as well.

2.6 Difference between land use and land cover

There has been confusion on the use of the terms land use and land cover. The distinction between

'land use' and 'land management practice' is also poorly understood. Land tenure and commodities

are other aspects of land occupation that can relate to land use and contribute to land use mapping.

These categories can be distinguished from each other by the following definitions as stated by the

Australian Government, Bureau of Rural Sciences (October, 2006).

2.7 Usefulness of remote sensing technology in land use change studies

Patterns of land use change and analysis of temporal changes can easily be identified with remote

sensing technology in a way that provides an optimal trade-off between cost, accuracy and reliability.

The multi-temporal analysis of changes in the land cover provides sufficient information about the

dynamics of this typical land use as cited by Maldonado et al. (2002). Remote sensing technology

over the decades has been an indispensable tool in environmental modelling. Timely and accurate

change detection of Earth's surface features is extremely important for understanding relationships

and interactions between human and natural phenomena in order to promote better decision making

as cited by Lu et al. (2004). This is accomplished by analysing temporal satellite images/ remotely-

sensed image data to reveal the trend of the land-cover dynamics. Remote sensing technology has

been successfully used to study the changes and modelling of expansion of urban areas. Some of the

studies in land-cover changes using remote sensing in many fields are highlighted in the subsequent

sub chapters, (2.7.2). Some of the examples are forestry, agriculture and sea-ice monitoring and have

achieved satisfactory results. Image arithmetic such as differencing is one of the easiest image

analysis techniques used to recognise change in the pattern of the landscape. Other techniques like


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post classification change detection has been popular in detecting changes of urban fringe as it

provides from- to information. Remote sensing application contributes a lot in studying urban

sprawl phenomenon.

2.7.1 Definition of change detection

Change detection is the process of identifying differences in the state of an object or phenomenon by

observing it at different times cited by Singh (1989). Timely and accurate change detection of Earths

surface features provides the framework for better understanding, relationships and interactions

between human and natural phenomena to better manage and use resources. Change detection

normally entails the application of multi-temporal datasets to quantitatively analyse the temporal

effects of the phenomenon. Because of the advantages of repetitive data acquisition, its synoptic

view, and digital format suitable for computer processing, remotely sensed data such as Thematic

Mapper, Probatoired Observation de la Terre (SPOT), radar and Advanced Very High Resolution

Radiometer (AVHRR), have become the major data sources for different change detection

applications during the past decades cited by Lu et al. ( 2004).

2.7.2 Previous works on change detection worldwide

There are different applications of change detection. Mausel et al. (2004) elaborated on the change

detection applications. There are nine of these applications namely:

1 Urban change:

Ramadan et al. (2005) assessed urban growth in theShaoxing city using satellite remotely sensed

data at three epoch time. The methodology used was based on post classification comparison. Results

showed that the built-up area surrounding Shaoxing City has expanded at an annual average of 7

square kilometres. Analysis of the classified map showed that the physical growth of the urban area

is upsetting the other land cover classes. Gupta et al. (2005) conducted a research to reveal

urban/agriculture changes using multi-scale analysis in Dehradoon city in India. The authors of this

paper have compared the results of five different techniques of band combination, subtraction, band

division, principal component analysis and classification to find the changes in Dehradoon city,

India. The research concluded that in the case of multi-resolution data, direct comparison of two

multi-level image dates is restricted because various spectral and texture phenomena exist at

different scales and resolutions.

A method is required to allocate the unique value to each smoothed area (representing one class)


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images from seven contrasting areas. The images were analysed using scale variance analysis to

determine the spatial frequencies present. Specifically images of the Normalized Difference

Vegetation Index (NDVI) were analysed, which is sensitive to vegetation activity. Image arithmetic

Analyses were performed on images for each of the two dates and change images derived by

subtracting the NDVI values of the first images from those of the second date. The change images

were characterized by higher spatial frequencies than the images of individual dates, but this was

only marked for four of the seven areas. Contrary to initial expectations, knowledge of the spatial

frequency content of the images from the two dates could not be used to infer the spatial frequencies

present in the change images and hence the spatial resolutions needed for detecting change in the

NDVI.

3. Forest mortality, defoliation and damage assessment:

Royle and Lathrop (1997) demonstrated the viability of using change detection technique to monitor

hemlock forest health in New Jersey using Landsat TM data.

A prolonged drought in the western US has resulted in alarming levels of mortality in conifer forests

within 1988 and 1991. Satellite remote sensing holds the potential for mapping and monitoring the

effects of such environmental changes over large geographic areas in a timely manner cited by

Macomber and Woodcock (1994). Results from the application of a forest canopy reflectance model

using multi-temporal Landsat TM imagery and field measurements, indicated that conifer mortality

can be effectively mapped and inventoried. The test area for this project was Lake Tahoe Basin

Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers

of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including

tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The

difference in cover estimates between the dates forms the basis for stratifying stands into mortality

classes, which are used as both themes in a map and the basis of the field sampling design. The

results of this project are immediately useful for fire hazard management, by providing both

estimates of the degree of overall mortality and maps showing its location. They also indicated that

current remote sensing technology may be useful for monitoring the changes in vegetation that are

expected to result from climate change.

Forest canopy and volume change can be simulated by remote sensing technology.

Collins and Woodcock 1994, mapped forest vegetation of Tahoe and Stanislaus National Forests

using landsat TM imagery and a canopy reflectance model. Results of timber inventories in the

forests indicate the vegetation maps form a useful basis for stratification.


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4. Deforestation, regeneration and selective logging:

Change detection can be used in deforestation, regeneration and selective logging. Wilson andSader

(2002)conducted a research on Detecting of forest harvest type using multiple dates of Landsat TM

imagery in northern Maine. The RGB-NDMI change detection classification applied to Landsat TM

imagery collected every 2-3 years appears to be a promising technique for monitoring forest

harvesting and other disturbances that do not remove the entire over storey canopy.

Guatemala's Maya Biosphere Reserve (MBR) has recently experienced high rates of deforestation

corresponding to human migration and expansion of the agricultural frontier. Given the importance

of land-cover and land-use change data in conservation planning, accurate and efficient techniques to

detect forest change from multi-temporal satellite imagery were desired for implementation by local

conservation organizations cited by Hayes and Sader (2001). Normalized difference vegetation index

(NDVI) image differencing, principal component analysis, and RGB-NDVI change detection

techniques were employed in the analysis of three satellite images of the biosphere reserve. The

changes were visualised. This is a typical application of remote sensing technology in deforestation

5. Wetland change:

Wetlands could be monitored using remote sensing technology. Previous study by Munyati (2000) is

an example. Remote sensing change detection approach was used to asses change on a section of the

Kafue Flats floodplain wetland system in southern Zambia, which is under the pressures of reduced

regional rainfall and damming and water abstraction by man. Four temporal satellite images were

classified and analyzed. The results indicated spatial reduction in area of dense green vegetation in

upstream sections of the wetland.

6. Forest fire:

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used for the

detection of fires in various ecosystems throughout the world. In this study, the most commonly used

methods have been applied to a time-series of 63 AVHRR day time images for the whole of West

Africa for the 1991-1992 dry seasons by Kennedy et al., 1994. The West African region includes

ecosystems ranging from dry Sahara grasslands to moist tropical forests. Furthermore, these

ecosystems show considerable seasonal variability. Existing methods were found to be inadequate

for fire detection for the whole region because of the spatial and temporal heterogeneity of the

region's environments. A number of changes were made to the established methods and the new fire

detection procedure was applied to the time-series. Geographical Information System and remote

sensing technology illustrates how such data can improve our knowledge of fire activity at national


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and sub-continental scales.

7. Landscape change:

Landscape morphology can be simulated and monitored over a period of time. Peralta and Mather

(2000) conducted a research on analysis of deforestation patterns in the extractive reserves of Acre,

Amazonia from satellite imagery. Three indices of landscape structure were applied to classified

satellite imagery to characterize the impact of social and economic processes on the development of

the forest landscape. Lacunarity Index was used to measure landscape homogeneity, the Korcak

Patchiness Index to measure the distribution of patches according to their size and the Area-

Perimeter Fractal Exponent to measure change in the shape of cleared forest patches. The work

reveal that the changing shape, size and spatial frequency of patches in the forest landscape showed

that major changes in the forest landscape occurred between 1975 and 1989.

Cushman and Wallin (2000) used Landsat satellite images to quantify the changes in the rates and

patterns of the landscape in a forested area of central Sikhote-alin Mountains of the Russian Far East.

Wild fire and timber harvesting were identified to be the major causative factor for the changing in

rates and patterns of the landscape.

8. Environmental change, drought monitoring, flood monitoring, land slide detection

9. Other applications such as crop monitoring, shifting cultivation monitoring road segments and

change in glacier mass balance

2.7.3 Overview of Change detection techniques

The selection of an appropriate change detection algorithm is very important (Jensen et al.,1993a). It

will have direct impact on the type of image classification to be performed if any and also will

dictate whether important from- to information can be extracted from the imagery. Most change

detection works require that the change information be readily available in the forms of maps and

tables. The most commonly change detection algorithm used as described by Jensen, 1993b are as

follows:

(i) Change detection using write function memory insertion: Individual bands of remotely sensed

data are inserted into specific write function memory banks in the digital image processing system to

visually identify change in the imagery as cited by Price et al. (1992) and Jensen et al. (1993b). This

method is an excellence analogue method for qualitatively assessing amount of change in a region


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but it does not give quantitative information.

(ii) Multi-date composite image change detection: This change detection technique is when multiple

dates of rectified remotely sensed data are placed in a single dataset for change analysis to extract

change information. Thus, selected bands of two thematic mapper scene of the same region are

placed in a single dataset. A traditional classification using the associated bands is performed.

Unsupervised classification technique will result in the creation of change and no change clusters.

These classes are then aggregated into informational classes by the analyst after carefully studies.

The advantage of this change detection technique is that only a single classification is required.

However, it is sometimes difficult in labelling the change classes and from-to change class

information may not be available.

(iii) Image algebra change detection (band differencing or band rationing):The same band in two

different images can be used in change detection either by rationing or differencing them if the

images have previously been rectified to a common base map (Green et al., 1994). The subtraction

results in positive and negative values in areas of radiance change. Zero values are areas that did not

change within the periods. But normally these values are transformed into positive values by adding

a constant. The merit of this change detection technique is that it is an efficient method of identifying

pixels that have changed in brightness value between periods but it does not provide from-to change

classes. It also requires careful selection of the change and no-change threshold which sometimes

make it difficult for its usage.

(iv) Post-Classification comparison change detection: This method of change detection provides

quantitative information. It is the most commonly used quantitative method of change detection

(Jensen et al., 1993a). It involves rectification and classification of each temporal remotely sensed

image. These two classified maps are then compared on a pixel by pixel basis using a change

detection matrix. It provides from-to change information and there is no need for further

classification. However, the accuracy of the change detection is dependent on the classification

accuracy of the two classified images.

(v) Multi- date change detection using a binary mask applied to date 2: In this method, the base

image which is referred to as date 1 at a time n. The date 2 image can either be an earlier image (n-1)

or later image (n+1). Traditional classification of date 1 is performed. One of the bands from both

dates of imagery is placed in a new dataset. The two- band dataset is then analysed using image


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algebra like band ratio, image differencing and principal components to produce a new image file.

The analyst normally selects a threshold value to identify areas of change and no change in the new

image. The change image is then recoded into a binary mask to depict areas that have change over

the periods. This method minimised change error of omission and commission and provides from-to

change information as well. The procedure however is more complicated and time consuming.

(vi) Multi-date change detection using ancillary data source as date 1: In this method, an existing

land cover data source is used to replace traditional remotely sensed imagery. This data is used as

date 1 but it is recoded to be compatible with the classification scheme being used. The date 2 data is

also classified and the two classified data are compared on a pixel-by-pixel basis as in post

classification comparison change detection.

Its advantage is the use of well-known trusted data source and possibility of minimising the error of

omission and commission. Detailed information on from-to cover classes can be obtained and single

classification is required. The disadvantage of this method is that it is dependent on the quality of the

ancillary data.

(vii)Manual, on screen digitization of change: In this method, photographic dataset are scanned at

high resolutions into digital image files. These datasets are then registered to a common base map

and compared to identify change. When digitised high resolution aerial photography is displayed on

a screen, it can easily be analysed using standard photo interpretation techniques such as size, shape,

shadow and texture. The analyst then has to visually interpret both data using heads-up on screen

digitising and compare the images to detect changes. The method however is not so accurate.

(viii) Spectral change vector analysis: In this method, two spectral variables are measured and

plotted for the area both before and after change occurs. The vector describes the direction and

magnitude of the change within the two periods. The total change magnitude per pixel is computed

by determining the Euclidean distance between end points in an n- dimensional change space.

Change vector analysis outputs two geometrically registered files, one containing the sector code and

the other containing the scaled vector magnitudes. The change information is superimposed onto an

image of the area with change pixels been colour coded based on their sector code . The change is

detected if the threshold is exceeded.

(ix) Knowledgebased vision systems for detecting change is also becoming popular of late:Novel

algorithm can be used to achieve automatic detection and positioning of changes for monitoring


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systems in complex environments. The aim is to efficiently detect changes of unknown dimensions,

shapes and velocity and to position them in a sequence of images. The practicality of the algorithm is

simplified by the use of different decision rules in a multistage test for different purposes. These

decision rules identify the changes and number of parts, as well as the position and its optimal pick-

up points for each individual part cited by Cheng et al. (2004). A lighting compensation method is

embedded to maintain a constant lighting environment and therefore the error rate can be reduced.

Experimental results on a variety of image sequences show that the proposed algorithm is effective

and efficient, regardless of the irregularity and number of changes.

2.7.4 Previous work on change detection in Ghana

Some works have been done in studying Land use dynamics using different approaches and

procedures in different parts of the world. In Ghana, much research has been done with the key

interest on the forested land being degraded into desert. Ta (1960) and Mooney (1961) attempted to

classify the forest vegetation of Ghana and their classification was based on inventories of tree forest

reserves. Ademola and Vlek (2004) generated a short-term projection of land cover distribution in

northern Ghana by analysing Thematic Mapper images acquiredin 1984, 1992 and 1999. The work

reveals that the built-up area will increase at the expense of crop-land and natural vegetation,

covering about 39% of the landscape by 2006. The dominant land cover change process was the

built-up area as result of an increase in demand for housing by the increasing population.

Pabi (2007) researched into the process and amount of land-use/cover changes that have prevailed in

specific localities across Kintampo and the neighbouring districts the within a 10-year period. The

outcome of the study indicated that, in space and time, there have been significant land-use/cover

changes. Variability in change was a constant, rather than occasional feature across these human

dominated landscapes. The conversion and transformation processes indicated that the traditional

land-use strategies are self-sustaining. Multi-temporal Landsat Thematic Mapper (TM) images for

1984, 1992, and 1999 were used to map and detect land-cover changes in a 5400-km2 area within the

Volta Lake basin of Ghana by Ademola and Paul (2004). Their work revealed that the most dominant

land-cover change was the conversion of natural vegetation to cropland, which occurred at an annual

rate of 5%. While the data suggest an increase in human pressure, reversible change in woodland and

grassland occurred in 4% and 2% of the landscape, respectively. A higher proportion of reversible

land-cover changes relating to fallow agriculture occurred in about 14% of the landscape, whereas a


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higher overall increase in woody biomass (10%), compared to an overall decrease of 9%, indicates a

certain level of rainfall-induced resilience in the ecosystem. They recommends for further research

into quantitatively evaluating the mechanisms enhancing vegetation recovery in dry land areas.

The extent of the urbanized areas of Accra was assessed from Landsat-TM satellite images for the

year 2002 and compared to similar information for the years 1985and 1991 by Yankson, Kofie and

Jensen (2004). A texture-based classification method was applied. The results show that the

urbanization of the fringe areas of Accra is occurring at a pace that has increased from 10 km square

per year for the period 1985-1991 to 25 km per year for the period 1991-2002. This development is

subsequently discussed with focus on the unplanned and haphazard nature of the growth and the

corresponding absence of inadequate infrastructure and service provision. Kizito (2007) examine the

relationship between urbanization and flooding in Accra. As a result of the government of Ghanas

implementation of the Structural Adjustment Program (SAP), massive expansion in the built-up area

of Accra occurred. Beside the impact of the increased impervious surface due to urban growth, poor

land tenure and land delivery system, poor garbage collection and disposal, as well as poor

implementation of economic development programs, have been identified as contributors to flooding

in Accra

Richard Brand of the University of Rhode Island conducted a study of the spatial organization of

residential areas in Accra in 1972. He argues that the city of Accra has expanded in a north-eastern

direction due to the movement of the elites low-density housing developments. Brand sites this trend

as beginning in the 1890s with Victoriaborg, and then extending north-eastward with the creation of

the Ridge, then Cantonments, and then the Airport Residential Area. All of these neighbourhoods

except for the Airport Residential Area were developed by the British colonial government as elite

enclaves, with the Ridge and Cantonments being built to house the increased number of European

residents who moved to Accra after World War II (and before Ghanaian independence). Growth in

the north-eastern direction has been aided in recent years due to the main artery that runs through the

area, Independence Avenue.

Recent studies in Accra (Yankson 1997; Konadu-Agyemang 1998; Tipple et al. 1998 and Tipple

2000) focused on urban growth, infrastructure and housing. Konadu -Agyemang (1998) concluded

that the rapid growth of Accras urban population has created a situation in which a wide gap exists

between the needs for and the provision of housing and related infrastructure. Yankson (2000)

assessed land cover change in Accra and tested the viability of Landsat Thematic mapper images for

urban change detection. This study does not investigate and address the complexity of urban growth.

Otto et al. (2006) quantifies the urban growth in the Accra metropolitan area and explores the

causative mechanisms. All the previous researches contribute to understanding of the urban growth.


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In this research, different types and techniques for land use and land-cover change detection of Accra

were evaluated. In the direct change detection method, three different image-to-image change

detection techniques were evaluated. The indirect change detection was conducted using both

supervised and unsupervised classified maps.


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Fig.1. Map of Ghana showing location of Accra

3.1.2 Land use and land cover types

The various land use and land cover categories in Ghana are listed below:

Agricultural land which consist of shrub land, grass/herb, dense/grass fallow

Forest consisting of closed forest, open forest and reverie vegetation

Savannah

Urban areas

Bare land

Water bodies

Unclassified lands

This classification scheme is generalized for the country. However, for a specific area of interest,

local classification scheme could be employed based on the predominant land cover types available.

In this research, the land cover categories established are:

Closed vegetation cover

Open vegetation cover


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Dense herbaceous cover

Grass

Urban/ bare areas

Water bodies

3.2 Potential users and stakeholders

Urban sprawl and growth information are relevant to variety of people, agencies etc. for decision

making. Among these users are both government and non-governmental agencies.

Some of these potential users are town and country planners, urban planners, statisticians,

environmental agencies, land owners etc.

3.3 Data Collected

The Landsat data were acquired from the global land-cover website at the University of Maryland,

USA.

URL; http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp

The images are Enhance Thematic Mapper plus image acquired on 4th February 2000 and thematic

mapper image acquired on 25th

December 1990 shown in figure 2 and 3 respectively. The satellite

data have 30m spatial resolutions.

The TM and ETM Plus images have spectral range of 0.45-2.35 micro meter with bands 1,2,3,4 and

5.

Band 1 = Blue (0.45-0.52)

Band 2 = Green (0.52-0.60)

Band 3 = Red (0.63-0.69)

Band 4 = NIR (0.76-0.90)

Band 5 = SWIR (1.55-1.75)

Aerial photography for 1990 and 2000, which was used as the ground truth data for the accuracy

assessment

Topographic map at a scale of 1:10000

These data were acquired from the Survey Department, Accra, Ghana.

Existing classified Land use maps from Centre for Remote Sensing&GIS, Dept. of Geography,

Legon,Ghana.


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Fig.2. Satellite image of Accra acquired on 25th

December, 1990


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Fig.3. Satellite image of Accra acquired on 4th

Feb. 2000


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4 METHODOLOGY FOR LAND USE/LAND COVER CHANGE

DETECTION

4.1 Flow chart for the methods employed

Fig.4. Flow chart for methodology employed in digital change detection


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In order to detect areas that have changed as a result of the expansion of the urban fringe, the

procedures as shown in the flow chart were followed. The first thing to consider is the

processing of the remotely sensed data to extract change information. Basically, the main

processes involve are pre-processing (geometric registration and radiometric correction),

followed by image classification, change detection and finally assessing the statistical

accuracies of the classification in change detection process.

4.2 Pre-processing of the multiple date remotely sensed data:

Pre-processing of satellite images prior to image classification and change detection is

essential. Due to spatial, spectral and temporal radiometric resolution constraints, the

complexity of physical environment cannot be accurately recorded by normal remote sensing

sensors. As a result, it is necessary to pre-process the remotely sensed data before the

analysis.

Pre-processing commonly comprises a series of sequential operations, including atmospheric

correction or normalization of image, masking etc. The normalization of satellite imagery

takes into account the combined, measurable reflectance of the atmosphere, aerosol scattering

and absorption, and the earths surface. Because satellite data are acquired at different dates

and time, there is the need for the images two be normalised as if they were taken at the sametime. Geometric correction will remove the problem of different coordinates system of the

respective images. Band screening was conducted to detect bias. Three bands namely 4, 3, 2

were selected from each image as they were found to have good correlation.In the image to

image registration, the 1990 TM image was made the reference image and the 2000 ETM

image was registered to the ETM image. This was done by locating identifiable points on both

images and saving their coordinates in a text editor which was used as a correspondence file.

Nearest neighbour re-sampling technique was employed because the output values are the

original input values and it is easy to compute. The root mean square error was 0.399, which

represents 12 metres on ground.

4.3 Image classification

In order to make use of the multitude of digital data available from satellite imagery, it must

be processed. This processing involves categorizing the land into its various use functions.

Classification techniques are widely used for Land use/Land-cover mapping and can be usedas source of information for many different applications. The multi-spectral classification can


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be performed by application of varieties of methods which includes:

(i) Algorithm based on parametric and non-parametric statistics that uses ratio and

interval- scaled data and non-metric methods that can also incorporate nominal

scale data (Duda et al. 2001),

(ii)The use of supervised and unsupervised classification logic,

(iii)The use of hard or soft set classification logic to create hard or fuzzy thematic output

products,

(iv)The use of object oriented logic and

(v)Hybrid classification approaches which combines two or three methods

In the parametric method approach of classification, such as unsupervised clustering and

maximum likelihood, there is assumption that the remotely sensed data is normally distributed

and knowledge about the forms of the underlying class density functions are known( Duda et

al.,2001). Non parametric methods such as fuzzy classifier, neural networks and nearest-

neighbour classifiers are used for remotely sensed data that are not uniformly distributed and

without the assumption that the underlying forms are known (Jensen ,2004; Liu et al., 2002).

Non- metric methods such as ruled-based decisions tree classifiers can be used on both real

value data such as reflectance values ranging from 0 to 100% and nominal scaled data

(Jensen, 2004).

Supervised classification requires the manual identification of known land cover/land use

types within the imagery and then using statistical functions to determine the spectral

signature of the identified feature. The spectral fingerprints of the identified features are

then used to classify the rest of the image. In unsupervised classification, the user has to make

decisions on which categories can be grouped together into a single land use category. Both

supervised and unsupervised classifications were performed. The unsupervised classification

has the following advantages:

There is no need to have previous knowledge of the image in order to get a classified

image. Hence, the classification procedure is faster as it does not utilize the training

data as the basis for classification.

The identification of classes of interest against reference data is often more easily

carried out when the spatial distribution of spectrally similar pixels has been

established in the image data.

No extensive prior knowledge of the region required.


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The opportunity for human error is minimised in this classification unlike supervised

classification whereby mistakes in digitizing the training areas can affect the process.

Image signatures are derived based on image statistics (Lillesand, 2004).

By comparing to supervised classification, the decision functions for unsupervised

classification is not biased by previous knowledge or estimates of class membership

Unsupervised classification requires very few inputs into the classification processes.

Unique classes are recognised as distinct units.

In contrast to the a priori use of analyst-provided information in supervised classification;

unsupervised classification is a segmentation of the data space in the absence of any

information provided by any analyst. Analyst information is used only to attach information

class (or ground cover type, or map) labels to the segments established by clustering. Clearly

this is an advantage of the approach.

In this classification process, natural groups of pixels based on their spectral properties are

selected by the software. However, this process still requires user interaction once the

classification has been performed. The advantages of the supervised classification are:

It is more accurate than unsupervised because the analyst has the possibility to

train the classes

It provides informative classes unlike clustering

Bias decision function can be created by the analyst

Image signatures are derived based on the training areas which are true

representative of the classes

4.3.1 Unsupervised classification

Unsupervised classification was used to cluster pixels in a data set without any user-defined

training classes. Although the method requires no user input to create the classified image, the

output tends to require a great deal of post classification operations to make the results more

meaningful. The Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering

algorithm was used. It uses the minimum spectral distance formula to form clusters. It begins

with either arbitrary cluster means or means of an existing signature set, and each time the

clustering repeats, the means of these clusters are shifted. The new cluster means are used for

the next iteration. The ISODATA utility repeats the clustering of the image until either a

maximum number of iterations have been performed, or a maximum percentage of unchanged

pixels have been reached between two iterations. The ISODATA clustering algorithm (Tou &


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Gonzalez., 1974; ERDAS, 1997) compares the radiometric value of each pixel with

predefined number of cluster attractors, aggregates pixels in clusters and shifts the

cluster mean values in a way that the majority of the former aggregated pixels belongs to a

cluster.

The ISODATA algorithm has some further refinements by splitting and merging of clusters

(Jensen, 1996). Clusters are merged if either the number of members (pixel) in a cluster is less

than a certain threshold or if the centres of two clusters are closer than a certain threshold.

Clusters are split into two different clusters if the cluster standard deviation exceeds a

predefined value and the number of members (pixels) is twice the threshold for the minimum

number of members. The ISODATA algorithm is similar to the k-means algorithm with the

distinct difference that the ISODATA algorithm allows for different number of clusters whilethe k-means assumes that the number of clusters is known a priori. Performing an

unsupervised classification is simpler than a supervised classification, because the signatures

are automatically generated by the ISODATA algorithm.

4.3.1.1 Clustering to detect the land use classes

In clustering, the pixels were grouped into classes based on similar spectral characteristics.

This was achieved using isoclust, the implementation of the ISODATA algorithm in IDRISI,

to detect clusters of pixels that have similar spectral characteristics. The ISODATA method

does not need any ground truth. It analyses the images and organizes the pixels into clusters

with similar characteristics without consideration of what these pixels represent in reality.

Here it is assumed that pixels with similar characteristics represent the same land-use. It is

very likely that the same land-use type can be represented with several clusters.

4.3.1.2 Aggregation of clusters

The clustering procedure is just grouping of pixels of similar spectral characteristics. In order

to get informational classes, the clusters were grouped. Vector files with ground-truth data

were used to detect which land-use classes the different clusters that have been created by

Isoclustbelong to. The images were reclassified to create a land-use map for 1990 and 2000

respectively.


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4.3.2 Supervised Classification

This is a procedure for identifying spectrally similar areas on an image by identifying

training sites of known targets and then extrapolating those spectral signatures to other areas

of unknown target.

In supervised classification, the identity and location of some of the land-cover types are

known a priori through a combination of fieldwork, interpretation of aerial photography, map

analysis and personal experience (Hodgson et al., 2003).

Training areas, usually small and discrete compared to the full image, are used to train the

classification algorithm to recognize land cover classes based on their spectral signatures, as

found in the image. The training areas for any one land cover class need to fully represent the

variability of that class within the image. There are numerous factors that can affect the

training signatures of the land cover classes. Environmental factors such as differences in soil

type, varying soil moisture, and health of vegetation, can affect the signature and affect the

accuracy of the final thematic map. It is really important to choose a desirable classification

scheme and algorithm. The Maximum likelihood classifier algorithm was chosen for this

study as it proved to give better results as compare to the other supervised techniques tried.

4.3.2.1 Training Sites Development

A training area is a small sample of homogeneous areas selected by the image analyst prior to

classification. These areas were determined from maps and ortho-photos, topographical maps

and other ancillary information (e.g. land use database). Training sites were free of anomalies

and large enough to provide good statistical representation. Also, there were sufficient

numbers of sites selected for each class to account for small local variations within the class.

Edge pixels containing the combined backscatter of multiple targets (mixed pixels) were

avoided. The objective of training data is to obtain a set of statistics that describe the spectral

pattern for each land-use/land-cover category to be classified. These sets of statistics are used

to determine decision rules for the classification of each pixel in an image. The training site

were representative of their respective classes, including the variation within the class itself

and the training data should closely fit the distribution assumptions, on which the decision

rules are based (Campbell, 2002). Sample pixels representing each of the land cover

categories were selected through digitizing. Polygons (training sites) belonging to the sameland cover category were given the same ID. In total, there were adequate sample of pixels for


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each cover type for statistical characterization. The number of pixels selected for each land-

cover or land-use category in the training is shown in table 1 below.

Table1: Number of pixel selected in each land-cover category for training

Land Cover

Category

TM1990 image ETM 2000 image

Closed veg. cover 1209 1179

Open veg. cover 2831 3457

Dense herb cover 4479 4231

Grass/herb 475 357

Urban/bare land 6432 7899

Water bodies 4673 4923

4.3.2.2 Signature Development

Signature files which contain statistical information about the reflectance values of the pixels

within the training sites for each class were created. The statistical analysis for the reflectance

values of the trained areas selected was examined to avoid significant loss of recognition

accuracy. The classes signatures examine were maximum, minimum, and mean values as well

as a class covariance matrix. Histogram plots for the class signatures were also examined.

Finally a scatter plot for the entire data set was also generated. These signatures were used to

assess the performance of the trained areas before classification.

The training site polygons were defined as a vector file of polygons. The vector file was

converted to a raster image during the development. This vector file was created using the on-

screen digitizing feature of the display system. With either raster or vector, training site

classes are indicated by integer codes. Scatter plot and signature comparison chart were

generated to visually analyse the quality of the training sites.

4.3.2.3 Maximum Likelihood Classifier

This is a statistical decision rule that examines the probability function of a pixel for each ofthe classes, and assigns the pixel to the class with the highest probability.


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The classifier assumes that the training statistics for each class have a normal or 'Gaussian'

distribution. However many are not, radar statistics in particular. The classifier then uses the

training statistics to compute a probability value of whether it belongs to a particular land

cover category class. This allows for within-class spectral variance. Equal probabilities were

assigned to each of the land-cover classes to weight the probability function. This

classification technique usually provides the highest classification accuracies. Accordingly, it

has a high computational requirement because of the large number of calculations needed to

classify each pixel.

4.3.2.4 Classification accuracy assessment

This defines the extent to which a manual or automatic processing system correctly identifies

selected classes.This process evaluates the accurateness of a derived thematic map.

There are two main methods normally used to validate the accuracy (or assess the error) of a

remote sensing-derived thematic map namely:

Qualitative confidence-building assessment

Statistical measurements.

In confidence-building assessment accuracy assessment, visual examination of the map

associated with the overall area frame is done by knowledgeable individuals to identify any

gross errors. However, this method is not good for quantitative purposes.

Statistical measurements however can quantify the errors using reference data. They are two

sub categories:

Model-based inference, and

Design-based inference.

Model-based inference is concerned with estimating the error of remote sensing classification

process (or model) that generated the map rather than estimating the accuracy. The design-

based inference is based on statistical principles that infer the statistical characteristics of a

finite population based on the sampling frame. Some common statistical measurements

include producers error, consumers error, overall accuracy, and kappa coefficient of

agreement (Jensen, 2004).

In this research, the design based inference was used as a sample of the total population was

used in determining the accuracy of the classification processes.

Using the ortho-photos a reference data, sample pixels representing each of the land-cover

categories were digitised and compare to the created thematic maps. The overall accuracy

expresses the total accuracy of the whole classification process. It is defined as a fraction of


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the total of the diagonal matrix and the total no of cells. However, it is express as a

percentage. Pixels of known class which are incorrectly classified as other classes are known

as error of omission. Pixels which are incorrectly classified as a given class when they should

be some other known class are known as error of commission. When the correctly classified

pixel in each class is divided by column totals, producers accuracy is obtained.

4.3.3 Post classification comparison change detection

To minimise phonological effects of remotely-sensed data, the post-classification comparison

method was employed for image processing, as this method is less sensitive to radiometric

variations between the scenes (Mas 1999). The supervised classified maps were used because

of its higher accuracy as compare to the unsupervised maps.

Digital change detection techniques aim to detect changes in images over a period of time.

Change detection techniques rely upon differences in radiance values between two or more

dates. Unfortunately, few quantitative comparative studies of change detection techniques are

available. There is no universally 'optimal' change detection technique. The choice is

dependent upon the application. The selection of an appropriate change detection algorithm is

very important. Most change-detection projects require that the from-to information be

readily available in the form of maps and tabular summaries. Post-classification changedetection was chosen because:

It produces a transition matrix. This matrix describes each pixel if it has changed and

if it has, from which class to what. This change information is valuable for analyses

and for planning purposes.

There is no need for radiometric normalization of the images use in the analysis

Sensitive to the spectral variations

It provides from-to change information.

One major drawback of this method of change detection is that the accuracy is dependent on

the accuracy of individual classification results and it is also quite time consuming. The Land

cover change detection scheme used in current study was chosen by considering local

condition, classes of interest and availability of reference ground truth data material available


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4.3.3.1 Detecting from-to change using Cross Classification and Tabulation

In order to have insight into which land cover category is converted into the other both cross

classification and tabulation operations were performed. The cross classification gives visual

information about the transformation of each land cover category by clicking on the legend.

With the cross-classification operation, it is possible to visualize the change of one cover

category to the other within the two periods. There are 36 from to different categories for the

6 different classes. This matrix describes each pixel if it has changed and if it has, from which

class to what. This change information is significant for analyses and for planning purposes.

Some of the land cover classes have higher tendency of transforming to the other and vice

versa. Vegetations have higher tendency to transform into urban areas but the probability for

urban areas and water to change to other classes were considered to have less probability.

4.3.3.2 Detecting and Visualization of change in urban areas by colour coding

In order to depict the areas that have changed as a result of expansion of the urban areas, the

various land-cover categories in 1990 that transformed into urban areas in 2000 from the cross

classification were colour coded red (red). The areas that did not change into any other land

cover categories were colour coded to their respective cover classes with the same colours as

the classified map.

4.3.3.3 Change Map

Cross tabulation analysis was performed using the two classified maps.

The resulting 36 from-to classes were re-classified into change and no change map. Land

cover classes that have no change were separated from those which have change by

comparing the two thematic layers. The non-change areas are the diagonal elements in the

cross-tabulation. The procedure yields 7 different classes, namely

Non-Change area

Change in closed vegetation cover

Change in open vegetation cover

Change in dense herbaceous cover


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Change in grassland

Change in Urban/ bare areas

Change in water bodies

The non-change areas are predominantly urban and water bodies respectively while the

vegetations were transformed to urban areas.

4.3.3.4 Change detection accuracy

The accuracy of remotely sensed map is traditionally assessed by comparing a sample of the

map data with actual ground conditions typically by generating an error matrix. This approach

is useful for both visualizing image classification results and more importantly for statistically

measuring the results. One of the most common means of expressing classification accuracy

is the preparation of a classification error matrix also called confusion matrix. The accuracy of

change detection analysis outputs depend on the following conditions:

Low RMS error of geometric registration of two-date images

High quality of ground truth data and field investigation information

The selection of change detection methods or algorithms

Proper knowledge of the study area

Proper multi-date image classification scheme

Analyst effort and experience.

The accuracy of the change detection process depends on the accuracy of the two-date image

classifications when using post-classification change detection. The change detection statistics

shows the change of each land- cover category. The main disadvantage of this method is that

the accuracy is dependent on the individual image classification. The change detection

accuracy is discussed in the results and discussions. However the overall accuracy of the

individual classified images was good so the expected error in the change detection was

minimal.

4.4 Image Differencing

Image differencing is probably the most widely applied change detection algorithm (Singh, 1989).

It involves subtracting one date of imagery from a second date that has been precisely registered

to the first. According to recent research, image differencing appears to perform generally better

than other methods of change detection (Coppin & Bauer, 1996). It is possible to identify theamount of change between two rectified images by image differencing (Green et al., 1994; Maas,


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the results and discussions chapter.

4.4.2 Image Enhancement

Image enhancement methods provide important information that could be otherwise

Thesis Ebenezer KP

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