Modelling the Effects of Scale on Mapping Trees Outside ... · IIRS Member : Dr. S. P. S. Kushwaha...

Modelling the Effects of Scale on Mapping Trees

Outside Forests

S. S. Chhabra December, 2004

Modelling the Effects of Scale on Mapping

Trees Outside Forests

By S. S. Chhabra

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geoinformatics. Thesis Assessment Board Thesis Superviors Chairman: Prof. Dr. Alfred Stein IIRS Supervisor: Mr.C. Jeganathan External Examiner : Dr. Alok Saxena ITC Supervisor: Dr. I. W. Bijker Forest Survey of India IIRS Supervisor : Mr.C. Jeganathan IIRS Member : Dr. S. P. S. Kushwaha

iirs INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS &

INDIAN INSTITUTE OF REMOTE SENSING, NATIONAL REMOTE SENSING AGENCY(NRSA) DEHRADUN, INDIA

MODELING THE EFFECTS OF SCALE ON MAPPING TREES OUTSIDE FORESTS

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Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.


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ACKNOWLEDGEMENT

At the very outset I would like to express my sincere appreciation for the efforts which led to the commencement of this unique programme of Masters in Geo-informatics. I express my thanks to the authorities of “GEONEDIS” Project- a Joint Logo Program of the International Institute for Geo-Information Science and Earth Observation, The Netherlands and the Indian Institute of Remote Sens-ing (National Remote Sensing Agency), Department of Space, Government of India. I feel privileged to have received a rare opportunity to undergo this course. It not only enriched me in terms of techni-cal knowledge, but also provided me an opportunity to acquire a different cultural experience. In true sense of the term it has been a great learning all along the course.

The present thesis, a part of the same programme has been no different; rather it provided the opportu-nity for a greater learning. It taught me how to think imaginatively on a subject, conceptualize it logi-cally, and execute it efficiently. These are the qualities which I learned from my both supervisors, Ms. Dr. Ir. Wietske Bijker of ITC and Mr. C. Jeganathan of IIRS. Both of them tried to make me more and more independent and taught me how to work towards the goal even when the odds don’t go in favour. They remained extremely co-operative and helpful through out my research period. I could seek clari-fications to my doubts, could get my write ups reviewed whenever I wanted to. My sincere thanks to both of them. I must admit they have laid a good foundation of independent working in me which definitely is a good virtue and will go a long way in my life.

There had been many more minds and hearts working for me in seeing my work through. I owe my gratitude to Dr. Andreas Wytzisk and Dr. Valentyn Tolpelkin of ITC who provided me with the initial input as to how to conduct a research work. Dr. Valentyn needs special thanks for his added guidance and support in the absence of Dr. Bijker during project formulation stage. Thanks are due to the mem-bers of the draft proposal defense committee comprising, Prof. Dr. Ir. Alfred Stein, Mr. Gerrit Huurneman, and Dr. Norman Kerle for showing the correct path ahead. Thanks are also due to the mid term review committee members for similar efforts. The unexpected review by the Dean, IIRS, Dr. V.K. Dadhwal needs a special mention. It was due to him that I received valuable and useful sugges-tions which supplemented my efforts. I express my heart felt thanks to him.

The ever smiling and extra ordinarily supportive Mr. P.L.N.Raju, In charge, Geo-informatics Division, IIRS deserves special thanks. I owe a great deal to him in terms of all the support I received through-out the course.

Ms. Vandita Srivasthava, Ms. Meenakshi Kumar of IIRS and Mr. Rajesh Kumar of National Sample Survey Organisation deserve my special thanks for providing me with much needed important techni-cal inputs from time to time.

Almighty as usual had always been with me and provided all what I needed about which I myself was not aware of.


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ABSTRACT

Trees Outside Forests (TOF) not only play an important role in ecology of our landscape but also serve many important economic functions. They help conserve biodiversity, control erosion and provide fuel wood and fodder. However, little is known about this resource on the large area basis. This is due to the fact that they are usually small and isolated making it difficult to map. Scale which is thought of as pixel or grain size and which is the same as the spatial resolution of the satellite sensor plays an impor-tant role when using satellite remote sensing data to map such a resource.

The present research looks into the effect of scale on mapping such resources in a small suburb of De-hradun, India. The effect of changing spatial resolution on classification accuracy, on shapes of typical TOF patches and minimum detectable patch area have been addressed. The use of various data sets have been made, which includes data from IKONOS, ASTER, IRS 1D, ETM and RESOURCE SAT AWIFS sensors, along with simulated data sets at varying spatial resolutions from 8m to 60m and fused data sets at 1m and 6m.Classification of the data sets has been carried out using unsupervised algorithm.

It is observed that though the highest overall classification accuracy is obtained for IKONOS MSS, the fused images of 1m and 6m also give high value of overall accuracy and even better estimates of TOF area than IKONOS MSS. The fused image of IKONOS (MSS and PAN) prove better for classifica-tion of linear patches of TOF along roadside and field bunds. It is also noted that the shapes of the classified TOF patches decrease in shape complexity as the resolution becomes coarser. The minimum detectable TOF patch area for different spatial resolutions has been estimated. A comparative analysis of simulated and original data sets at similar resolutions involving aspects pertaining to visual, statisti-cal and classification accuracy has also been attempted.

KEY WORDS: TOF, Scale, Grain, Extent, Resolution, Patch, Shape.


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TABLE OF CONTENTS

Acknowledgment............................................................................................................I Abstract ......................................................................................................................... II

Table of Contents ........................................................................................................ III List of Tables................................................................................................................. V List of Figures ............................................................................................................... V

List of Appendices.......................................................................................................VI

1. Introduction ..............................................................................................................................1 1.1 Thoughts on Scale ................................................................................................................1 1.2 Forest & TOF .......................................................................................................................1 1.3 Importance and Assessment of TOF - Indian Scenario ........................................................2 1.4 Developments in the Field of Remote Sensing and GIS ......................................................3 1.5 Motivation ............................................................................................................................3 1.6 Problem Identification..........................................................................................................3 1.7 Research Objective...............................................................................................................4 1.8 Research Questions ..............................................................................................................4 1.9 Research Utility....................................................................................................................4 1.10 Structure of the Thesis........................................................................................................4

2. Literature Review....................................................................................................................5 2.1 Landscape Pattern and Scale ...............................................................................................5 2.2 Studies on Scale ..................................................................................................................5

3. Study Area & Data .................................................................................................... 8 3.1 Study Area...............................................................................................................................8

3.1.1. Locational Information........................................................................................8 3.1.2. Landuse................................................................................................................8

3.1.3. Forest & TOF in the Study Area........................................................................10 3.1.4. Reasons for choosing the Study Area................................................................10 3.2 Data......................................................................................................................................10

3.2.1. Original Sensor Data..........................................................................................11 3.2.2. Simulated Data...................................................................................................11

3.2.3. Fused Data..........................................................................................................12 3.2.4. Resampled Data .................................................................................................13

4. Methods......................................................................................................................14

4.1 Research Approach............................................................................................................14 4.2 Data Preparation................................................................................................................14 4.3 Visual Interpretation of IKONOS PAN Image of the Study Area....................................16

4.3.1 Methodology Adopted.......................................................................................16 4.3.2 Quality of Visual Interpretation & Its Limitation..............................................17 4.4 Image Classification..........................................................................................................17

4.4.1 Unsupervised Classification..............................................................................18 4.4.1.1 ISODATA Classification Algorithm.................................................18


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4.5 Accuracy Assessment...........................................................................................19 4.5.1 Contingency Matrix.........................................................................................19 4.5.2 Kappa Coefficient............................................................................................19 4.5.3 Reference Points..............................................................................................19

4.6 Shape Analysis of TOF Patches..........................................................................22 4.6.1 Patches & Landscape.......................................................................................22 4.6.2 Measurement of Landscape.............................................................................22 4.6.3 Characterization of Patches/ TOF Patches under consideration......................22 4.6.4 Metrics for the Measurement of Patch & its Shape Complexity.....................23 4.6.4.1 Perimeter-Area Ratio.....................................................................23 4.6.4.2 SHAPE Index................................................................................24 4.6.4.3 Fractal Dimension Index...............................................................24 4.6.4.4 SqP................................................................................................25 4.6.5 Focus of the Study & Method followed..........................................................25

4.7 Minimum Detectable Patch ...............................................................................26

4.8 Comparison of Simulated and Original data sets............................................28 4.8.1 Visual Comparison.........................................................................................29 4.8.2 Statistical Comparison...................................................................................29 4.8.3 Classification Accuracy Comparison.............................................................29

5. Results & Discussion...........................................................................................30

5.1 Classification Accuracy..............................................................................................30 5.2 TOF Area Estimation.................................................................................................36 5.3 Shape Analysis ..........................................................................................................38

5.3.1 Visual Appeal on Various Images ...............................................................38 5.3.2 Classified Outputs ........................................................................................44 5.3.3 Shape Indices................................................................................................46

5.4 Minimum Patch Area.................................................................................................47 5.5 Comparative Analysis of Data Sets – Original and Simulated .................................49

5.5.1 Visual Analysis..............................................................................................49 5.5.2 Statistical Analysis........................................................................................51 5.5.3 Classification Accuracy and TOF Area Comparison....................................54

6. Conclusions & Recommendation.........................................................................56

7. References ..............................................................................................................59


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LIST OF TABLES: Table 3.1 Details of the Satellite Data Procured for the Study 11

Table 3.2 Spatial Resolution of Simulated Data Sets 12

Table 3.3 Details of Fused Data Sets 12

Table 4.1 Accuracy of Georeferencing 15

Table 5.1 TOF Area Estimation in Different Data Sets 37

Table 5.2 Summary of Occurrence of Various Patches of TOF in Different Resolutions

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Table 5.12 Minimum Detected TOF Area in Different Data Sets 48

Table 5.13 TOF Area in Neighboring Eight Pixels of the Selected TOF Pixel 49

Table 5.14 Comparison of Coefficient of Variations 52

Table 5.15 Significance of Coeffiecient of Variation 53

Table 5.16 Comparison of Coefficient of Correlation 53

Table 5.17 Comparison of Accuracy 54

Table A 1 Comparison of Spectral Profiles between IKONOS MSS & Fused IKONOS (MSS & PAN)

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Table A 2 Comparison of Coefficient of Correlation Between Bands 74

LIST OF FIGURES: Fig. 3.1 Location Map of Study Area 9

Fig 4.1 Research Approach 14

Fig. 4.2 Steps Involved in the Preparation of Data Sets 16

Fig. 4.3 Reference Image and its Visual Interpretation 17

Fig. 4.4 Steps Involved in Unsupervised Classification and Accuracy Assessment 21

Fig. 4.5 IKONOS – PAN Images of Selected TOF Patches for Shape Analysis 25

Fig. 4.6 Methodology for Shape Analysis 26

Fig. 4.7 Steps Followed in Determing the Minimum Detectable Patch 27

Fig. 4.8 Single Pixel of TOF and its Buffer 28

Fig. 4.9 Aspects in Simulated and Original Data Set Comparison 28

Fig. 5.1 Standard FCC and Classified Outputs 30

Fig. 5.2 Variations in Overall, Producer’s & User’s Accuracies and Overall Kappa Statistics

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Fig. 5.3 Variation in Overall Accuracies for Different Methods of Reference Points Selection 33

Fig. 5.4 Variation in Producer’s Accuracies of TOF for Different Methods of Reference Points Selection 34

Fig. 5.5 Accuracy Variation in Simulated , Original and Fused Data Sets 35

Fig. 5.6 Variation in Accuracy due to Edge Points 36

Fig. 5.7 TOF Area Estimation in Different Data Sets 37

Fig. 5.8 TOF Patches on FCC and Classified Images 39

Fig. 5. 9 Variation of Shape indices for Compact Patch 44

Fig.5.10 Variation of Shape Indices for Linear Patch along Road 45

Fig. 5.11 Variation of Shape Indices for Linear Patch along Field Bunds 46

Fig. 5.12 Single TOF pixels of 48m Simulated Resolution and its Neighbourhood 47

Fig. 5.13 Visual Comparison of Simulated and Corresponding Original Sensor Images 50

Fig 5.14 Comparison of Accuracy and TOF Estimation 55

Fig. A 1 FCC of IKONOS MSS and Fused IKONOS Along with Spectral Profiles 73

LIST OF APPENDICES:

APPENDIX: A-I Concepts & Terminologies 63

APPENDIX: A-II Image Interpretation Key for TOF Delineation using IKONOS PAN Data

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APPENDIX: A-III Error Matrix ( Reference Points from Visually Interpreted IKONOS PAN Image)

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APPENDIX: A-IV Error Matrix ( Reference Points from Visually Interpreted IKO-NOS PAN Image Falling on Small Patches or Edges of TOF)

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APPENDIX: A-V Error Matrix ( Reference Points from Visually Interpreted IKONOS PAN Image)

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APPENDIX: A-VI Error Matrix ( Reference Points from LISS III Classified Image) 69

APPENDIX: A-VII Error Matrix ( Reference Points from AWIFS Classified Image) 70

APPENDIX:A-VIII Shape Indices 71

APPENDIX: A-IX Spectral Profile Comparison Between IKONOS MSS & IKONOS Fused (MSS & PAN)

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Chapter 1: Introduction “Scale can be defined as the window through which the investigator chooses to view the world. It is a particular point of view, among many others that are possible, about geographic reality” ….. Levin (1992) 1.1 Thoughts on Scale Of all words that have some degree of specialised scientific meaning, scale is one of the most ambigu-ous and overworked (Godchild and Quattrochi, 1997). In a general sense it indicates the spatial dimen-sions at which entities, patterns, and processes can be perceived and analysed. It determines the pres-ence or absence of various landscape features. The same feature may appear differently at different scales. In the context of remote sensing, scale corresponds to spatial resolution, which refers to the ability of a sensor to record and display the fine spatial detail as separated by its surroundings (Wood-cock and Strahler, 1987). Scale is one of the important factors that determine the nature of spatial variation in images. It is fundamental to the capture, storage, manipulation and analysis of data. This in turn affects the accuracy and precision with respect to statistics and their interpretation. Though a certain scale is always presumed by the pixel resolution, different objects have their own inherent scale. The appropriate scale for observations is a function of the type of environment and the kind of information desired. The classification techniques used to extract information from imagery also inter-act with these variables to influence the selection of an appropriate scale. That is, many factors like possible combinations of scales, analysis methods, environments, and the extent of information that can be desired about it are all interlinked.

In this context, it may be interesting and worthwhile to look into how the various spatial resolutions depict the ground reality? How subtle a feature becomes as one glances from a finer to coarser resolu-tions? And what is the effect of this subdued depiction on feature extraction, image statistics and its accuracy? What happens to the spatial patterns if so exist in the scene as the resolutions get coarser? In particular, no one has been able to predict the spatial patterns to be expected to for a particular location in a particular type of imagery. Instead the use of spatial data has been limited to the empirical associa-tion between surface phenomenon and spatial patterns in images (Curtis et al. 1988).

By understanding the relation of spatial structure of images as a function of resolution, various meth-ods of aggregation and dis-aggregation can be performed to obtain multiple scales with the same data set. This is of great use as it is impossible to acquire the data at the true scale for modelling a given process. Also this insight of spatial pattern with respect to spatial resolution of different sensors on-board can aid in thoughtful selection of data sets for a given purpose. This in turn can considerably reduce the frittering away of money and valuable time in inapt data purchase and analysis.

The present research proposes to look into these aspects with semi-urban landscape as the spatial envi-ronment with Trees Outside Forests (TOF) as the principal object of observation.

1.2 Forest & TOF

Food & Agricultural Organization (FAO) of United Nations defines forest as land with a tree canopy cover of more than 10% and area of more than 0.5ha. Forest is determined not only by the presence of


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trees but also by the absence of other predominant land uses. Thus, according to FAO, timber and rub-ber wood plantations are classified as forests but fruit orchards and trees planted under agro forestry system are categorized as other lands with Trees Outside Forests (TOF).

In India, an area of land recorded as forest in revenue records or proclaimed to be forest under a forest law or Act is described as forest. Thus, “forest area” is an area recorded as forest in the government records. Often the term is also written as “recorded forest area”. In Indian context, the trees having diameter of 10 cm or more at breast height, which grow outside the recorded forest areas embedded in a landscape matrix composed by different land-uses are referred to as Trees Outside Forests (TOF). These trees mainly grow on the agricultural land, on meadows/grazing lands, along rivers, canals or roadsides, in towns, gardens, and parks (State of Forest Report, 2001).

It is now well known that trees outside the forests not only serve many ecological functions, such as biodiversity conservation, erosion control, and carbon sequestration, but also many economic func-tions, such as provision of fuel wood, fodder, fence posts etc. However, little is known about this re-source on the large area basis. Although there have been several initiatives in assessing the TOF at various levels, the purpose and scale of these different studies have been different, and several meth-odologies have been tried. Davis (2001) opined “TOF is a field which is little systematically worked out, definition and classifications are not in place, the terminology is not harmonized, and hard data are not available, above all not for larger areas”. This is due to the fact that they are usually small and isolated making it difficult to map.

1.3 Importance and Assessment of Tree Outside Forests - Indian Scenario

The National Forest policy of India (1988) has set a goal that 33 percent of country’s geographical area should be under forest and tree cover. All lands other than the recorded forest area having a tree canopy density1 of 10 percent or more can be considered as TOF.

In India, tree species like Peepal (Ficus religiosa) and Banyan (Ficus bengalensis) have been tradi-tionally worshipped as an abode of Gods and are commonly found on temple premises and roadsides. Also the traditional practice of maintaining homestead gardens of fruit bearing trees and planting trees on farmlands, along roadsides and rivers have contributed significantly to the present status of TOF. It includes woody vegetation like mango, neem, peepal, litchie orchards etc. In the recent years impetus has also been given to road side, wasteland and farm forestry plantations. This points out to the fact that substantial tree wealth (TOF) exists in the country, which includes small wood lots (less than 1 ha in extent), block plantations, linear plantation along roads, canals etc., and scattered trees on home-steads, farmlands and urban areas. Considering its intrinsic and tangible contribution to the national bio-resource stock, it is essential to account this valuable resource on a periodic basis. The status of TOF would also indicate the success of afforestation projects such as social forestry and wasteland development. As information on TOF is mostly absent from official statistics, policy initiatives for its management are also hurdled with time consuming methods of age-old inventorying.

Forest Survey of India (FSI), an organization under the Ministry of Environment & Forests, Govern-ment of India is engaged in assessing the TOF wealth of the country since 1991. However in trying to improve upon the methodology of TOF assessment which has so far been based on field inventory 1 Canopy Density: Percent area of land covered by the canopy of trees. Also referred as “Crown Density.”


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methods, use of IRS LISS III and PAN data is being made in the recent times. The fused image ob-tained from LISS III and PAN is also being made use of in editing and refinement of classified images of LISS III and PAN (Rawat et al., 2003).

1.4 Developments in the Field of Remote Sensing and GIS

In the recent past, the field of remote sensing technology has made a quantum jump both in terms of technology development and in its management. Current remote sensing systems offer unique methods for detecting patterns at the surface of the earth, and for acquiring data about the underlying processes at a variety of spatial scales, ranging from centimetres to kilometres. Analysis and computing envi-ronments now allow data transformation from a diverse and multivariate form to a usable state. Paral-lel improvements in the understanding of how and why remotely sensed data and methods are impor-tant in forestry and forest science have particularly aided information synthesis developments (Wulder and Franklin, 2003). In a complementary fashion, GIS provide opportunities to create multi scale rep-resentations by incorporating and linking digital maps at different scales, and through the development of statistical and mathematical functions to deal with scale as a generic issue.

1.5 Motivation

It is not only the vast expanse of thick forests which has given me pleasure as a forester but even a lone tree of mango or ficus standing beside a house or in a city lawn drawn my attention. It has al-ways been a pleasure to watch a long stretch of linear avenue plantation since my childhood. If the large imposing tracks of forests can be called a majority then the latter must be referred to as minority. Such resources so far have not been mapped effectively as is the case in our lives too where majority always gets recognised and minority neglected.

As a forester my interest is to know about the quality and quantity of the trees growing outside the recorded forest areas. Hence there is a need to make an attempt to map them effectively to find out how much of such resource exists. This coupled with availability of images of a finer spatial resolu-tion, multiple scales of data and the issue of scale i.e. the variation across different levels of pixel reso-lutions while using remotely sensed data add on to my motivation. It will be interesting and useful to explain and if possible to predict how different variables and patterns of TOF change across different spatial resolutions (scales).

1.6 Problem Identification

The distribution of trees outside forests in the Indian semi-urban area can be mainly grouped into three main category viz., as trees standing in isolation, as trees in a straight stretch giving rise to linear patches and as group of trees forming clusters of different shapes. Based on the spacing of trees within the patch, the last category can be further divided into patches having crown density more than 80% and patches having crown density less than 80%, and also trees existing in well definable patterns. These three categories of TOF are taken as objects of observation and explored through multi-scale data sets. It is observed that the perception of objects varies widely with the change in spatial resolu-tion. These observations crystallized into the problem statement regarding the variations in classifica-tion accuracy results, changes in the shapes and area of the features and disappearance of certain pat-terns due to aggregation of features in coarser resolutions with respect to different spatial data sets. In nutshell, the scale dependency of TOF and how to extrapolate these results across different scales and resolutions culminated as the subject of research.


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1.7 Research Objective

• To study the influence of different spatial resolutions on classification, shape and size of various patterns of trees outside forests.

1.8 Research Questions

1. How does changing scale of observation (spatial resolution) affect classification results and their interpretation?

2. How the classification accuracies vary while mapping trees outside forests?

3. How do the shapes of TOF patches vary with changing spatial resolutions?

4. What is the minimum detectable patch area in different spatial resolutions?

5. How do the simulated data sets compare with the data from the original sensors at the similar spatial resolutions?

1.9 Research Utility

The present research may serve as an input in the endeavours of an organisation like Forest Survey of India, which is engaged in mapping forest and tree resource of the country. It may also help in the long run in formulating appropriate policies, assessing the success of afforestation projects such as social forestry and wasteland development, drawing effective management plans and for monitoring changes in the status of TOFs.

1.10 Thesis Structure

This thesis is divided into six chapters. Chapter 1 deals with introduction regarding basic concepts of scale and the reasons behind the research along with its objective and research questions. Chapter 2 deals with review of relevant literature pertaining to the research. Chapter 3 introduces the study area and the various data sets used in the study. Chapter 4 describes in brief about the methods employed in achieving the objective and addressing the research questions. Chapter 5 presents the various outputs and their analysis. Chapter 6, the final chapter of the thesis summarises the work, draws conclusion and provides few suggestions and recommendations.


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Chapter 2: Literature Review

“There is now ample theoretical and empirical evidence for recognizing the resolution dependency as a basic aspect of remote sensing. Its implications must be fully pursued” ……Lovejoy and Schertzer (1995)

A remotely sensed image is a non– selective and true representation of the complex spatial reality in which we live in. Change in the spatial resolution changes the patterns of reality, which has obvious implications for understanding the dynamics of any environmental system. The study of the relation-ships between the patterns at different levels may help in obtaining a better understanding of the scale and resolution problem (Changyong Cao and Nina Siu – Ngan Lam, 1997). The optimal spatial resolu-tion of remotely sensed imagery varies with the objectives of analysis and the inherent characteristics of the scene in question (Quattrochi and Pelletier, 1991).

2.1 Landscape Pattern & Scale

Landscape spatial patterns are dependent not only on interacting physiographic and physiological proc-esses, but also on the temporal and spatial scales at which the resulting patterns are assessed. It is widely recognized that many environmental processes and patterns are scale dependent, where in they appear homogeneous at one spatial scale and heterogeneous at another (Ehleringer & Field, 1993; Foody & Curran 1994). Conceptually, scale represents the window of perception through which a land-scape may be viewed or perceived (Levin, 1992) and has two distinct components: grain and extent. Grain represents the finest distinction that can be made in an observation set (i.e. spatial resolution), while extent corresponds to the span of all detected entities (Allen and Hoeskstra, 1991) (i.e. the total area covered within an image swath). Hence grain refers to the smallest units in an observation array, and extent refers to the range over which observations are made. From a remote sensing perspective, grain is equivalent to the spatial, spectral and temporal resolution of the pixels composing an image, while extent represents the total area, combined bandwidths and temporal duration covered within the scene (Hay et al., 2001). Detailed description on various types of resolutions and scale can be found in Appendix: A-I.

2.2 Studies on Scale

The first step in the analysis of the scale problem in the natural sciences was the development of ap-propriate quantitative methods for detecting scales or discrete levels at which regular and irregular pat-terns occur in the landscape. Among the first techniques used are the blocking techniques that involve iterative aggregation of contiguous quadrats, and analysis of within and between block size variance performed at each aggregation level (Wiegert, 1962; Goodall, 1974; Ludwig and Goodall, 1978; Ludwig, 1979). Spectral analysis is also used to identify cyclic patterns in a sequence of equally spaced data according to the length of the intervals within which variation occurs (Ripley, 1978; Renshaw and Ford, 1984; Mulla, 1988). Autocorrelation functions, such as correlograms and semivariograms, are also commonly applied to measure the degree of spatial dependence between se-quences of data as a function of the distance separating them (Legendre and Fortin, 1989; Carlile et al., 1989; Oliver and Webster, 1986; McBratney and Webster, 1986; Mulla, 1988). Edge detection meth-


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ods have also been used in various applications, such as analysis of edges along altitude gradients (Beals, 1969), species distribution and abundance (Schuerholz, 1974), composition gradients (Wilson and Mohler, 1983), and ecological gradients (Ludwig and Cornelius, 1987).

Another aspect in fully understanding the scale issue is to explain and predict how patterns and proc-esses change across scales. A good example of the use of hierarchy theory for downscaling is provided by the aquatic production relationship demonstrated by Vollenweider (1975) and Schindler (1977). Bloschl and Sivapalan (1995), and Raupach and Finnigan (1995)s describe conceptual guidelines for scale translations and a series of approaches developed to perform the linkages across scales in the particular contexts of catchment hydrology and boundary-layer meteorology.

Ecological and physical processes operate at different spatial scales. To study these dynamic phenom-ena, appropriate scaling laws in order to relate information across a wide range of scales is necessary. As pointed out by some authors (Wiens, 1989; Levin, 1993), a variety of statistical and mathematical tools can be used for scaling, such as correlation and extrapolation. However, it appears that such tech-niques can be appropriate only when applied for short-term or small-scale predictions or, in other words, within the relevant domain of scale for the phenomenon under investigation. Extension across scale thresholds may be hazardous because of the instability in the dynamics of the transition zone between two domains of scale. The major steps required in order to make predictions across scales were best summarized by Turner et al. (1989a). They imply: 1) the identification of the processes of interest and parameters that affect this process at different scales, 2) the development of rules to trans-late information across scales, and 3) the ability to test these predictions at the relevant spatial and temporal scales.

Benson and MacKenzie (1995) examined the effects of decreasing spatial resolution from 20 m to 1.1 km, using three sets of satellite data, on landscape parameters characterizing spatial structure. Their results indicated that most measures were sensitive to changes in spatial resolution: some parameters decreased, while others increased, or were invariant with scale.

The fractal model could play an important role in detecting the scale and the resolution effects in re-mote sensing and GIS. Although few real world curves and surfaces are pure fractals, empirical studies indicate that the measured fractal dimensions of these curves and surfaces do change with the scale and resolution, which suggests that the fractal dimension index and its changes across scales and reso-lutions may be used to indicate the scale and resolution effect. Fractals and fractal analysis have been suggested as an innovative technique for characterizing remote sensing images as well as identifying the effects of scale changes on the properties of data (De Cola 1989, 1993: Lam & Quattrochi, 1992, de Jong & Burrough, 1995). Fractals can be applied to a variety of landscape problems because they conveniently describe many of irregular, fragmented patterns found in nature. The scale at which the highest fractal dimension is measured may be the scale at which most of the processes operate (Lam and Quattrochi, 1992).

The environmental processes are affected by a variety of spatial and temporal scales. Hence use of sta-tistical tools can be made that recognize multi-scalar patterning. Topographic variables like slope and aspect may not measure n same at all scales. Therefore there is a need to develop methods that test and characterize spatial distributions at different scales. It is important that tools and techniques be devel-oped which operate at multiple scales to work with data whose scale are not necessary ideal and to produce results that can be aggregated or disaggregated in ways that suit the decision making process.


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Openshaw and Taylor (1979, 1981) came out with a new problem, which they described as modifi-able areal unit problem (MAUP). The MAUP originates from the fact that a large number of ways ex-ist in which a study area can be divided into non-overlapping areal units for the purpose of spatial analysis (Marceau and Hay., 1999). The MAUP encompasses two components called the scale prob-lem and the aggregation problem. The former represents the variation in results that can be obtained when data acquired from areal units are progressively aggregated into fewer, larger units for analysis; the latter refers to the variation in results produced by the use of alternative combinations of aerial units at similar scales.

Studies addressing the MAUP demonstrated the possibility of controlling and eventually predicting its impact to a certain extent. In their systematic study of the MAUP in principal axis factor analysis of spatial data, Hunt and Boots (1996) revealed that specific MAUP effects are strongly influenced by the presence of spatial autocorrelation in the data. Similarly, Amrhein and Reynolds (1996), using spatial statistics to assess aggregation effects concluded that the modified Getis correlation statistic might provide a reliable diagnostic to estimate the possible aggregation effects imbedded in a given set of data. Another solution proposed to overcome the MAUP is to recognize its existence and to report the sensitivity of analytical results to variations in both the scale and level of aggregation. To do so re-quire that the data used can be aggregated and that results can be obtained for each higher level. One great advantage of remote sensing is the capacity to provide data at various spatial resolutions that can easily be aggregated at intermediate scales. It can enable the identification of scale thresholds where significant changes in patterns or in the relationship between variables occur. Another study indicates that the use of spatial statistics, such as autocorrelation indices, may be very helpful to control and predict the MAUP effects to some extent (Hunt and Boots, 1996; Amrhein and Reynolds, 1996).

At first sight the MAUP of the social sciences appears to be very similar to the effect of pixel size on the relationship between remotely sensed observations and biophysical variables. However, there are important differences, as in the social sciences the sizes and shapes of the areal unit (e.g. voting area) varies and the area of the spatial unit is often not related directly to what is being observed. For ex-ample, census data are based on areas that vary in size and shape and have a different number of peo-ple within them (Martin, D., 1998).

The studies on the effects of spatial resolution on the ability to classify land-cover/land-use types using digital classification techniques proved that a change in spatial resolution could significantly affect classification accuracies, and that in many cases, the use of successively higher spatial resolu-tion data resulted in lower overall classification accuracy apparently due to an increase in within-class spectral variability, which confused per-pixel classifiers (Marceau & Hay, 1999). Woodcock and Strahler (1987) studied how pixel variance changed according to the spatial variability of the scene and the spatial resolution of the sensor.


8

Chapter 3: Study Area & Data 3.1 Study Area

3.1.1 Locational Information

The study area consists of part of Dehradun City, the capital of Uttaranchal State in North India. It is bounded by Latitude: 30º 16' 20" N to 30º 17' 19"N & Longitude: 78º 03' 42” E to 78º 04' 56" E as shown in the Fig.3.1

Dehradun, also known the "abode of Sage Drona is situated in the mountain ranges of the Himalayas and is one of the old cities of India. The Dehra Dun valley (77°40' to 78°15'E and 30°00'’ to 30°35'N) lies between the west Himalayan mountain ranges in the north and the Shiwalik range running parallel to it in the south at a mean altitude of 485 m. and covers an area of 1920 sq km. In the west it is bor-dered by the river Yamuna and in the east by the river Ganges. The climate of Dehradun City can be divided in to three major seasons. The period from about middle of November to February is the cold season. The hot season that follows continues up to the end of June. The monsoon season is from July to about the third week of September. In summer maximum temperature is 36±6°C and the minimum temperature is 16±7°C where as in winter it varies from 23±4°C and 5±2°C respectively. The average annual rainfall of Dehradun is 2183.5 mm. Dehra Dun is distinguished from most other districts in the state by the existence of very large forests chiefly stocked with Sal (Shorea robusta). The forests ac-count for 1477 sq.kms of area, giving a percentage of 43.70 of the total area of the district (source:http://www.dehradun.nic.in/history.htm). Owing to the variation in altitudes and other aspects, the flora of the district varies from tropical to alpine species. The mountain slopes (north and south) of the valley are covered with pure and mixed forests dominated by Sal (Shorea robusta) which cover 51-58% of the valley and rise up to 1000 m altitude ( source: http://www.delhibird.org/site_guides/ Siteguide_dehradun.htm). However, the flat areas in the central part of the valley are under different land use practices: irrigated & cultivated agricultural land; agro forestry plantations; old tea gardens; orchards; urbanized areas; cantonments and riverine scrubland.

3.1.2 Land use

The land use in the study area mainly consists of agriculture followed by mango orchards and agro forestry plantations of eucalyptus and poplar species. The agriculture crops include paddy, wheat and sugarcane. Wheat is planted in Oct-Nov and is harvested in March and is followed by paddy. Sugar-cane (Saccharum officinarum) being a perennial crop keeps the land occupied through out the year. It can grow from eight feet to twenty feet tall and is generally about two inches thick. It is harvested in Oct-Nov.


9

Source: http://www.mapsofindia.com/maps/uttaranchal/uttaranchal.htm

Figure 3.1: Location map of Study Area


10

3.1.3 Forest & TOF in the Study Area

The study area covers approximately 3.7 square kilometres. There are no recorded forests in the study area and hence all the vegetation falls under the definition of TOF. TOF includes agro forestry planta-tions, mango orchards, poplar plantations along the field boundaries and in clusters (block planta-tions), and road side plantations of eucalyptus and other mixed species. Though the most of the trees under TOF in the study area are found to be matured and have crown diameters varying from 4m to 16m, however there are also some younger plantations of mainly poplar species.

3.1.4 Reasons for choosing the Study Area

The study area has sufficient quantity and variety of the TOFs. It contains all the in-tended variations of TOF which were required for the research work viz single tree, linear pat-terns (road side plantation, along the field bunds), in clusters etc.

Satellite data of different sensors (IKONOS, ASTER, IRS ID, ETM & RESOURCE SAT AWIFS) were already available

3.2 Remote Sensing Data

Remotely sensed digital data is a record or representation of various features on the ground at the time of data capture. The details to which these features can be discerned and extracted depend on the reso-lution characteristics mainly spatial, spectral and radiometric. As the present study envisages the study of the effect of spatial resolution on TOF classification, availability, selection and procurement of data sets of varying spatial resolutions was the first and crucial step initiated in the entire exercise.

It is well known by now that the tree delineation algorithms are all dependent on high resolution im-agery and the spatial resolution should be finer than the size of the tree crown. But it is presumed that there is a range of tolerance surrounding the optimum spatial resolution. Bearing in mind the range of tree crown diameter existing in the study area and to explore the above mentioned aspect, real sensor data sets of varying spatial resolutions ranging from 1 metre (IKONOS PAN) to 60 metres (AWIFS sensor of RESOURCE SAT) were selected for the study. As the spatial resolution from real sensors obtained is of the order of 1m, 4m, 5.8m 15m, 23.5 m, 30m and 60m, resolutions of in between ranges also seems to be essential. This not would help in only ascertaining the tests of significance in statisti-cal analysis but also help in understanding the pattern of variation of various variables concerning TOF along the continuous range of spatial resolutions. Keeping this aspect in mind various synthetic data sets were also created by data fusion and aggregation techniques. These synthetic data sets also enable in the comparison of data from real sensors at similar spatial resolutions. Also it was impor-tant to select uniform spectral bands among different data sets as much as possible as the main thrust of the study is to study the effects of changing spatial resolution on TOF classification, size and shapes. Taking this into account, near Infra Red, Red and Green bands are utilised as spectral data lay-ers from the different sensors. The spectral ranges of these bands are close to each other. Another im-portant aspect considered during data procurement was the year and date of pass of the satellite as there can be seasonal and phenological changes and there by variations in tree crown reflectance. Hence due care was taken while deciding on the dates of the satellite data to be procured. However it was practically impossible to acquire the data of the same date from all the satellites hence the best possible combination was worked out. All data sets selected were of the year 2001 during the period


11

from November to March except in the case AWIFS which was available only of November 2003 as the satellite Resource sat was launched only in the second half of 2003.

3.2.1 Original Sensor Data

In the current scenario of data dissemination, IKONOS was the easily available high resolution data set. IKONOS MSS and panchromatic data of 4m and 1m spatial resolution respectively, thus formed as the key data set. Other data sets used in the study include ASTER, IRS- ID LISS III, and LAND-SAT- ETM and RESOURCE SAT – AWIFS of 15 meters, 23.5 meters, 30 meters and 60 meters spa-tial resolution respectively. In all these data sets, the spectral width of near IR, Red and Green bands that determines separability among vegetation classes is almost of the same range. Hence these three bands are only considered for data preparation. The resolution characteristics pertaining to each of these satellite data are as given in the Table 3.1.

Table 3.1: Details of the satellite data procured for the study 3.2.2. Simulated Data Synthetic data sets were created by taking three bands (IR, Red, and Green) of the IKONOS MSS (4m spatial resolution) by using degradation technique (ERDAS Imagine 8.6). This enabled the degrada-tion of IKONOS MSS image (4m resolution) by an integer factor in the X and Y directions. This aver-ages all of the original "small" pixels that make up the new "big" pixels. If the X and Y factors are large, this method takes more of the original pixels into account in the computation than a bilinear in-terpolation or cubic convolution resample method do, since these resampling methods use only a small

NAME OF SENSOR

DATE OF PASS

SPATIAL RESOLUTION

SPECTRAL RESOLUTION

RADIOMETRIC RESOLUTION

IKONOS PAN MSS

19-04-0119-04-01

1 meter 4 meters

0.45 - 0.90µm 0.52 - 0.61µm (green) 0.64 - 0.72µm (red)

0.77 - 0.88µm (near IR)

11 bits

ASTER 21-12-01 15 meters 0.52-0.60 (green) 0.63-0.69 (red)

0.76-0.86 (near IR)

8 bits

IRS PAN LISS III

21-01-0102-03-01

5.8 meters 23..5 metres

0.50 – 0.75 µm 0.52 - 0.59 µm (green)

0.62 - 0.68µm (red) 0.77 - 0.86µm (near IR)

8 bits

LANDSAT ETM 01-03-01 30 meters 0.52 - 0.60µm (green) 0.63 - 0.69 µm (red)

0.75 - 0.90µm (near IR)

8 bits

RESOURCE SAT AWIFS

04-11-03 60 meters 0.52-0.59 µm (green) 0.62-0.68 µm (red)

0.77-0.86 µm (near IR)

8 bits


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window for computation. The spatial resolutions of synthetic data sets created after degradation are as shown in Table 3.2.

Data Sets Spatial resolution of simulated data

1 8m

2 16m

3 24m

4 32m

5 40m

6 48m

7 60m

Table 3.2. Spatial resolution of Simulated Data Sets

3.2.3 Fused Data Fusion of multi-sensor image data offers additional advantages than the constituent images due to the complementary nature (e.g. IKONOS MSS has high spectral resolution but lower spatial resolution, 4m compared to IKONOS PAN image which has higher spatial resolution of 1m but lower spectral resolution) of the data sets. High spatial resolution is useful for an accurate description of shapes, fea-tures and structures, whereas high spectral resolution is important for land cover classification. There-fore fusion of these two types of data helps to get multi-spectral images with high spatial resolution. There are various techniques for fusion of multi-sensor image data. Ideally, the method used to merge data sets should not distort the spectral characteristics of the high spectral resolution data, particularly with respect to digital classification accuracy. However as Principal Component technique has been proved to be altering the spectral characteristics of the images the least (Vani and Sanjeevi, 2002) among the three conventional techniques of fusion (PCA, IHS and Brovey), it was used to create fused data sets in this cases.

Table 3.3 shows the details of fused images which were created using Principal Component Fusion option available in ERDAS Imagine 8.6 software

Table 3.3. Details of Fused Data Sets

Sl. No.

Input images (spatial resolution)

Spatial resolution of fused image

Fusion technique

used

1 IKONOS MSS (4m) IKONOS PAN (1m)

1m Principal

Component

2 IRS 1D LISS III MSS (24m) IRS 1D PAN (6m)

6m Principal

Component


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3.2.4 Resampled Data

ASTER and ETM data which came with the spatial resolution of 15m and 30 m respectively were re-sampled to 16m and 32 m in order to facilitate their comparison with the simulated data sets at similar spatial resolutions, since the IKONOS MSS (4m) could not be simulated at 15m and 30m due to soft-ware limitations.


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DIGITAL IMAGE

CLASSIFICATION & ACCURACY ASSESSMENT

DETERMINATION

OF MINIMUM DETECTABLE TOF

PATCH AREA

SHAPE

ANALYSIS OF DIFFERENT TOF PATCHES

COMPARISON OF

SIMULATED & REAL DATA SETS

EFFECT OF SCALE ON TREES OUTSIDE FORESTS

SCOPE FOR FURTHER RESEARCH

Chapter 4: Methods

4.1 Research Approach

Considering the research questions and the objective aimed at, the research approach was phased out into four sections namely image classification and accuracy assessment, determination of minimum detectable TOF patch area in different resolutions, shape analysis of TOF patches in various resolutions and comparison of simulated data sets with original data sets of similar spatial resolutions. Overall view of the research approach is given in Fig. 4.1.

Fig. 4.1 Reasearch Aporoach

4.2 Data Preparation

Georeferenced IKONOS PAN image of 1m spatial resolution of the year April 2001 was taken as the base line data. Though the baseline data was already geo-referenced when procured, the positional accuracy of this was again confirmed with respect to Survey of India Topographic sheet of 1:50,000 scale. Other images of varying spatial resolutions viz., ASTER (15m), IRS 1D LISS III(23.5m) and PAN(5.8m), LANDSAT ETM(30m), and RESOURCE SAT AWIFS(60m) were then geo-referenced with IKONOS PAN image by image to image registration technique. IRS 1D LISS III(23.5m) and IRS 1D PAN(5.8m) were resampled to 24m and 6m respectively while the other data sets were kept at the same pixel size. RMS errors varied by sensor’s spatial resolution but were less than half the size of a pixel in all cases as shown in Table 4.1.


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Table 4.1 Accuracy of Georeferencing

For satellite remote sensing applications, the merging or fusing of multisensor satellite data is an effec-tive means of making use of the complimentary nature of different data sets. It helps to combine the spectral and spatial qualities of two different data sets, one with high spectral resolution but with lower spatial resolution and the other with higher spatial resolution but with lower spectral resolution. There are many techniques of the image fusion viz., PCA , IHS, Brovey and Wavelet based image fusion.According to Vani and Sanjeevi (2001), among the three conventional techniques i.e, PCA, IHS and Brovey, PCA is found to be the best method followed by Brovey and IHS in terms of presrving the spectral characteristics as well as for visualisation. However, wavelet based fusion was also tried but was not emplyed as the results were not at all encouraging. It showed lot of noise like effects.In the present study, two fused images of that of IKONOS and IRS 1D sensors were produced by merging IKONOS MSS (4m) with IKONOS PAN(1m) and IRS 1D LISS III(24m) with IRS 1D PAN(6m), resulting in 1m and 6m respectively..

Data degradation technique∗ was employed to produce simulated data sets of resolutions ranging from 8m to 60m. This was done so that the other resolutions viz., spectral, radiometric and temporal remained same in the simulated data sets as the primary aim of the study was to model the effects of scale(spatial resolutions) on mapping trees outside forests. This would help in understanding the effects of changing spatial resolution while the other resolutions remained constant. Taking into account of the various distribution patterns of TOF a subset area was selected and extracted from all the data sets. The spectral bands for all the data sets were limited to Green, Red and Infra Red as these are the bands that contribute maximum to vegetation analysis.

For comparison of data from real sensors to that of simulated ones of various spatial resolutions, data sets from ASTER (15m) and ETM (30m) were resampled to 16m and 32m respectively. This became necessary as IKONOS (4m) could be simulated only to its integral multiples to produce a range of data sets. This also helped to maintain regular steps (intervals) in the data sets. Fig. 4.2 shows the data sets used and steps involved in their preparation.

∗ Please refer to chapter 3. Subsection 3.2.2 for more details.

SL.NO

REFERENCE IMAGE

IMAGE BEING

GEOREFERENCED

PIXEL

SIZE (m)

RMSE

(PIXELS)

1 TOPOSHEET 1:50000 IKONOS-PAN 1 0.3427

2 IKONOS-PAN IKONOS-MSS 4 0.4634

3 IKONOS-PAN ASTER 15 0.3832

4. IKONOS-PAN IRS 1D LISS III 24 0.4845

5. IKONOS-PAN IRS 1D PAN 1 0.3244

6. IKONOS-PAN ETM 30 0.4786

7. IRS 1D LISS III RESOURCESAT-AWIFS 60 0.4802


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Fig. 4.2 Steps involved in the preparation of data sets

4.3 Visual Interpretation of IKONOS PAN image of the study area

Visual interpretation technique relies mainly on the perception or the ability to discriminate features based on its characteristic elements and prior knowledge of the ground reality. The basic elements of image interpretation are tone (colour), texture, shape, size, shadow, pattern, site and height of the fea-tures and also its association with respect to other objects in the scene. The success of this technique is highly dependent on the analyst effectively exploiting the spatial, spectral and temporal elements pre-sent in the image.

4.3.1 Methodology adopted

To begin with, at one hand we have multi-spectral data, panchromatic data and simulated data sets of varying spatial resolutions and on the other hand the ground reality. First and foremost thing was then to familiarise the ground reality and conceptualise this with respect to what it has been represented in the image. Generally ground data should be collected at the same time as data acquisition by the re-mote sensor, or at least within the time that the environmental conditions do not change. In the present case, this was not applicable as the data sets were of the year 2001. Hence ground check was per-formed only to ascertain the feature representation rather than its real occurrence.

The resolution of the imagery affects the interpretability since imagery with smaller pixel sizes gener-ally portrays more natural representations of objects. This is especially true with objects that are

GEO-REFERENCING

DATA FUSION IKONOS (MSS&PAN) - 1m IRS 1D (LISS III &PAN) - 6m

STUDY AREA

DATA SIMULATION FROM IKONOS-MSS (4m)

to (8m, 16m, 24m, 32m, 40m, 48m & 60m)

IKONOS PAN (1m) IKONOS MSS (4m) ASTER (15m) IRS 1 D PAN (5.8m) IRS 1D LISS III (23.5m) LANDSAT ETM (30m) RESOURCE SAT AWIFS (60m)


17

smaller than the dimension of the pixels. Hence, as the initial step, the study area was visited with the high spatial resolution images and the typical TOF patches were identified with that on the image (ground truth collection). The area selected for the study was well within the city limits and no forest area was present in the area of interest. This was cross checked with SOI topographic sheet that de-picts depict forest areas with double dotted lines and wooded lands as green wash patches. An inter-pretation key with respect to IKONOS-PAN image was prepared as given in Appendix A-II.

IKONOS PAN image (1m spatial resolution) of the study area was displayed on the viewer of ERDAS Imagine Software at a display scale of 1: 2000 (which was found to be the largest possible scale at which the TOF objects could be identified without getting pixellated) and the TOF patches were de-lineated by on screen digitisation. TOF pixels form polygons or lines or even lie as lone pixels. In this exercise, shadow and crown closure were taken as the most significant elements of image interpreta-tion. Fused image of 1m spatial resolution was also used while delineating TOF from the IKONOS PAN due to their characteristic dark red tones in TOF features.

4.3.2 Quality of Visual Interpretation & Its Limitation

Quality assurance and quality control of visual interpretation was an essential aspect of this exercise as this vector layer forms the reference data for digital classification and accuracy assessment of the re-maining data sets. The accuracy of the visual interpretation was assessed by cross checking the output simultaneously with three persons having good experience in visual interpretation. Thirty different types of TOF patches including single tree were marked on the IKONOS PAN data and the experts were asked to identify and report them independently. These outputs were then checked with the digi-tised layer and the results depicted an overall conformity of more than 90 percent. IKONOS PAN Im-age of the study area and its visual interpretation of TOF are shown in Fig. 4.3.

IKONOS PAN (April 2001) TOF - Visually interpreted Fig. 4.3 Reference image and its visual interpretation

4.4 Image Classification

Digital Image classification is the process of assigning pixels of an image to categories or classes, generally based on spectral reflectance characteristics. In this process, based on its intrinsic traits (spectral band responses and locational aspects) the categorical class membership (TOF) of an obser-vation (pixel) is determined. Depending on the type of information required, spectral classes may be


18

associated with identified features in the image (supervised classification) or may be chosen statisti-cally (unsupervised classification). In the supervised classification, based on the prior information of the spectral characteristics of the classes, the system is trained to generate boundaries in the feature space within which each class should lie. Then each pixel lying within a class boundary is assigned to that class. Unsupervised classification is purely based on spectral signatures and the interpreter can intervene only in deciding the number of clusters, convergence thresholds and in the recoding process. Classification can also be looked as a method of compressing image data by reducing the large range of DN in several spectral bands to a few classes in a single image.

4.4.1 Unsupervised Classification

Unsupervised classification is a process of grouping pixels that have similar spectral values. Each group of similar pixels is called a spectral class. In unsupervised classification, suitable algorithm is selected to group each pixel in the data into different spectral classes on the basis of natural groupings present in the image gray values.

4.4.1.1 ISODATA Classification Algorithm

Various statistical techniques are available for clustering. Iterative Self -Organizing Data Analysis (ISODATA) is selected as the classification algorithm for the present study. ISODATA starts with all pixels as a member of spectral class no. 1. The ISODATA procedure then creates less variable spectral class from the variable initial spectral class. The number of clusters can be specified by the user. As there is very high chance of spectral merging, hundred initial spectral classes were specified with a convergence threshold of 0.95 within twenty iterations. The spectral classes thus obtained were grouped into two categories viz., TOF and Others (non TOF) based on the tools available with the software mainly cursor enquiry, color palette manipulation and overlay of spectral classes on the im-age. Spectral profile of prominent TOF classes and its confluence point with other classes were also taken into account while recoding. In fact in order to get pure TOF stands, wherever merging of TOF and fields occurred, that spectral class was attributed to the category that represents spatial majority.

All the data sets were classified using unsupervised ISODATA classification algorithm. Even the fused data sets were subjected to digital classification as coefficient of correlation between IKONOS MSS image and IKONOS fused image between bands was found to be significant (Appendix IX). Unsupervised classification is adopted in the present study mainly due to the following reasons.

1. Unsupervised classification tends to be useful when the desired map classes are spectrally complex, that is, they are not well characterised by a single multivariate distribution function. This situation can result from a variety of factors that may influence the remotely sensed signature of a vegetation class including variability in illumination and reflection geometry due to topography; forest structure, health or species composition between stands; or under storey and background reflectance (Franklin et al., 2003). TOF also represents a similar situation of multivariate distribution.

2. Unsupervised classification requires only minimal initial input, but after classification the resulting spectral classes can be merged, disregarded or manipulated which permits the intervention of the user.

3. Because it is iterative, clustering is not geographically biased to the top or bottom pixels of the data file but has the disadvantage that it does not account for pixel spatial homogeneity.


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4.5 Accuracy Assessment

After the classification the accuracy of the resulting output needs to tested to establish the reliabilty of the classification. Accuracy assessment is the term used for comparing the classification to geographi-cal data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are known. The refer-ence pixels are randomly selected (Congalton, 1991).

4.5.1 Contingency Matrix

In the present study, a confusion matrix is developed for evaluating the classification accuracy. This method provides an organized way of comparing the classification with ground truth data or any refer-ence data. It lists the values of the known cover types of the reference data in the columns and for the classified data in the rows. The main diagonal of the matrix lists the correctly classified pixels. The number of reference pixels is an important factor in determining the accuracy of the classification. Also the error matrix must be representative of the entire area mapped from the remotely sensed data (Congalton, 1991).

The contingency matrix or the confusion matrix gives along with overall accuracy, the producer’s and the user’s accuracy for the various classes. Overall accuracy is the basic measure of classification ac-curacy, while the producer and user accuracy are found for each class. The producer’s accuracy is de-rived by dividing the number of correct pixels in one class by the total number of pixels of reference pixels in that class. It tells as to how well a certain area has been classified. It includes in it the omis-sion errors which refer to the proportion of the observed pixels on the ground which have not been classified. If the errors of omission are more, the producer’s accuracy will be less. The user’s accuracy on the other hand relates to the measure of the reliability of the classified map. It is obtained by divid-ing the correct classified pixels in a class by the total number of pixels that were classified. Errors of commission result when incorrect identification of pixels is done associated with a class while errors of omission occur when pixels are not recognised that should have been identified as belonging to a particular class.

Producer’s Accuracy (%) = 100% - error of omission (%)

User’s Accuracy (%) =100% - error of commission (%)

4.5.2 Kappa Coefficient

The Kappa coefficient is a measure of overall agreement of a matrix. In contrast to the overall accu-racy, the kappa coefficient takes into account the non diagonal elements. It tells us how much better is our classification compared to one where random assignment of class values is done to the pixels.

4.5.3 Reference Points

The number of reference points required for checking the accuracy of the classification as suggested by Congalton (1991) should be a minimum of 50 samples for each land cover class. In the present case this fact was taken into account.


20

In this study stratified random method was applied for the selection of random points so as to cover both the classes, TOF and others (non TOF). Five types of error matrices were created based on the reference image selected for random point generation. They were as follows

1. Points taken on visually interpreted IKONOS PAN image (100 random points each on the two classes, TOF and non TOF i.e., others). These points are taken as reference points and different classi-fied images are tested for accuracy based on these points.

2. Points (falling on edges and small patches of TOF) taken on visually interpreted IKONOS PAN Im-age (54 on TOF and 100 on Others; this combination resulted from the step1 as 54 points on TOF in the step1 were found to be falling on the edges and small patches of TOF). Hence in this case we get 54 points on TOF and 100 points on non TOF and using them as reference points, various classified images were tested for accuracy.

The idea behind doing this was to find out the possible reasons if any for the lower accuracies ob-tained from the step1. Since the resolution of IKONOS PAN image is very high, small scattered TOF patches could easily be delineated. Hence the chances of reference points falling on small patches were high. These small patches are not likely to be classified in coarser resolution images. Points fal-ling on edges were also considered as reference points in this case to account for lower accuracies.

3. Sample Points taken on Individual Classified Images (more than 50 on each class). The number of points varied from image to image so as to have minimum of 50 reference points per class. References points were taken from the visually interpreted IKONOS PAN image.

4. Reference points taken on LISS III classified Image (100 each on the two classes, TOF and non TOF) and these points transferred on to the other classified images and accuracy calculated.

5. Reference points taken on AWIFS Classified Image (50 each on the two classes). The number of reference points got limited to 50 due to limited pixels in the TOF class.

The central idea behind step 4 and 5 was to see how the different accuracies change depending upon from where the reference points are selected. For example if we are taking the reference points from the LISS III classified image, it means we are associating these points to a larger patch of TOF on ground. Points selected from AWIFS means further generalisation of the patch area or aggregation of smaller patches of TOF with the support area. It is assumed that when the reference points are taken from a classified image of finer resolution and tested on a coarser resolution, classification accuracy should fall. However, there may be a situation depending upon the object size and its spatial distribu-tion, that this monotonic trend in accuracy gets altered. It is also assumed that the when the reference points are taken from LISS III classified image and other images are tested for accuracy, there should be a fall in accuracy on both sides. Similarly when the reference points are selected on the more gener-alised map (AWIFS-classified image) the accuracy should show a decreasing trend as the resolution becomes finer. In all the cases mentioned above the selection of points was based on random method.

The overall methodology followed in classification and accuracy assessment is summarised in the Fig. 4.4


21

GROUPING SPECTRAL CLASSES

(TOF & OTHERS)

ERROR MATRIX & ACCURACY ASSESSMENT

REFERNECE POINTS (IRS 1D LISS III)

REFERENCE POINTS (AWIFS)

REFERNECE POINTS (OUTLIERS - IKONOS PAN)

RANDOM TEST POINTS (CLASSIFIED IMAGES)

DATA SETS FROM SENSORS SIMULATED DATA SETS

FUSED DATA SETS

UNSUPERVISED CLASSIFICATION

(ISODATA CLASSIFIER)

SELECTION OF CLUSTER NUMBERS

NUMBER OF ITERATIONS / CON-VERGENCE THRESHOLD

COLOR PALETTE MANIPU-

LATION

CURSOR ENQUIRY

SPECTRAL CLASS OVERLAY

REFERNECE POINTS (IKONOS PAN)

Fig. 4.4 Steps involved in Unsupervised Classification and Accuracy Assessment


22

4.6 Shape Analysis of TOF Patches

4.6.1 Patches & Landscape

Patches are small and discrete depiction of relatively homogeneous conditions in the canvas of a larger representation. This constitutes the building blocks of any pictorial representation (categorical maps). For instance if one has to denote in a map a lake or a small patch of land , it has to be portrayed in a different colour as a homogenous piece that can be clearly separable from its immediate surroundings. In most applications, once patches have been established, the within-patch heterogeneity is ignored. In digital terms, a cluster of homogeneous pixels can be referred to as a patch.

A landscape can defined as a mosaic of patches across a given area. Forman and Godron (1986) de-fined landscape as a heterogeneous land area composed of a cluster of interacting ecosystems that is repeated in similar form throughout.

Various environmental phenomena occur in and among these patches over a period of time. According to the need of representation of an environmental phenomenon, the scale and extent of the image can be chosen which in turn affect the exactitude of depiction of its constituents. Unfortunately, according to Gustafson (1998), “the distinction between what can be mapped and measured and the patterns that are ecologically relevant to the phenomenon under investigation or management is sometimes blurred.”

4.6.2 Measurement of Landscape

Landscape metrics quantify the pattern of a landscape at a point in time and they refer to indices de-veloped for categorical map patterns. These indices quantify specific spatial characteristics of patches, classes of patches, or entire landscape mosaics. Numerous metrics have been developed for the pur-pose. These metrics are of two types: one that quantify the composition of the map without reference to spatial attributes, and the others that quantify the spatial configuration of the map, requiring spatial information for their calculation (McGarigal and Marks 1995, Gustafson 1998).

Landscape metrics may be defined at three levels. Patch-level, Class-level & Landscape-level. Patch-level metrics represent the spatial character and context of individual patches. Class-level metrics rep-resent the amount and spatial distribution of a single patch type and may be interpreted as fragmenta-tion indices. Landscape-level metrics represent the spatial pattern of the entire landscape mosaic and may be interpreted more broadly as landscape heterogeneity indices because they measure the overall landscape structure. In most of the applications, patch metrics serve mainly as the computational basis for several of the landscape metrics.

4.6.3 Characterization of Patches/ TOF Patches under consideration

Patches may be classified and delineated qualitatively through visual interpretation of the data (e.g., delineating TOF polygons through interpretation of high resolution data), as is typically the case with vector maps constructed from digitized lines. Alternatively, with raster grids (constructed of grid cells); quantitative information at each location may be used to classify cells into discrete classes and to delineate patches by outlining them. While individual patches possess relatively few fundamental


23

spatial characteristics (e.g., size, perimeter, and shape) collections of patches may have a variety of aggregate properties, depending on whether the aggregation is over a single class (patch type) or mul-tiple classes, and whether the aggregation is within a specified sub region of a landscape or across the entire landscape.

Patches comprising the landscape are not self-evident; patches must be defined relative to the phe-nomenon under consideration. For example, in the present case from the timber management and eco-logical valuation perspective patches of TOF can be considered as the significant unit. TOF, with its unique distribution in a semi urban landscape depicts mainly three types of patches and this can be classified based on the shape of its occurrence viz. those that exhibit a linear appearance, those that are in clumps of varying densities and those standing in isolation (single tree). These clumps can be ob-served as closed entities of various shapes. These patches at any given scale has an internal structure that is a reflection of patchiness at finer scales, and the mosaic containing that patch has a structure that is determined by patchiness at broader scales (Kotliar and Wiens 1990

4.6.4 Metrics for the Measurement of Patch & its Shape Complexity

Patch-level metrics are defined for individual patches, and characterize the spatial character and con-text of patches. The computed values for each individual patch may have little interpretive value; however, sometimes patch indices can be important and informative in landscape-level investigations. Patch level metrics can help in understanding the spatial character and context of individual patches to a great extent. Patch shape, type, area, edge and neighbour type are some of the few measurements applicable to patch analysis.

Patch shape complexity relates to the geometry of patches--whether they tend to be simple and com-pact, or irregular and convoluted. Shape is an extremely difficult spatial attribute to capture in a metric because of the infinite number of possible patch shapes. Hence, shape metrics generally index overall shape complexity rather than attempt to assign a value to each unique shape.

Most of these shape metrics are based on perimeter-area relationships. Perhaps the simplest shape in-dex is a straightforward perimeter-area ratio, or as a fractal dimension, and often standardized to a simple Euclidean shape (e.g., circle or square). The interpretation varies among the various shape met-rics, but in general, higher values mean greater shape complexity or greater departure from simple Euclidean geometry.

4.6.4.1 Perimeter-Area Ratio (PARA)

PARA = Pij / Aij

Pij = Perimeter (m) of patch ij.

Aij = Area (sq.m) of patch ij.

It is a simple measure of shape complexity, but without standardization to a simple Euclidean shape (e.g., square). A problem with this metric as a shape index is that it varies with the size of the patch. For example, keeping the shape constant, an increase in patch size will cause a decrease in the perime-ter-area ratio.


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4.6.4.2 Shape Index (SHAPE)

This shape index (SHAPE) measures the complexity of patch shape compared to a standard shape (square or almost square) of the same size, and therefore to some extent reduces the size dependency problem of PARA. This shape index is widely applicable in landscape ecological research (Forman and Godron 1986).

SHAPE = Pij / Min Pij

Pij = Perimeter of patch ij in terms of number of cell surfaces.

Min Pij = Minimum perimeter of patch ij in terms of number of cell surfaces.

SHAPE equals patch perimeter (given in number of cell surfaces) divided by the minimum perimeter (given in number of cell surfaces) possible for a maximally compact patch (in a square raster format) of the corresponding patch area. If Aij is the area of patch ij (in terms of number of cells) and n is the side of a largest integer square smaller than Aij, and m = Aij - n2, then the minimum perimeter of patch ij, Min-Pij will take one of the three forms (Milne 1991, Bogaert et al. 2000):

Min-Pij = 4n, when m = 0, or

Min-Pij = 4n + 2, when n2 < Aij ≤ n (1+n), or

Min-Pij = 4n + 4, when Aij > n (1+n).

SHAPE ≥ 1, without limit.

SHAPE = 1 when the patch is maximally compact (i.e., square or almost square) and increases without limit as patch shape becomes more irregular. Shape index corrects for the size problem of the perime-ter-area ratio index by adjusting for a square (or almost square) standard. Hence it is a more meaning-ful and appropriate measure of overall shape complexity

4.6.4.3 Fractal Dimension Index (FRAC)

Fractal Dimension index is another basic type of shape index which is based on perimeter-area rela-tionship. In landscape ecological research, patch shapes are frequently characterized via the fractal dimension (Krummel et al. 1987, Milne 1988, Turner and Ruscher 1988, Iverson 1989, Ripple et al. 1991). It is computed as:

FRAC = 2 ln (0.25 Pij) / ln Aij

Pij = Perimeter (m) of patch ij.

Aij = Area (sq.m.) of patch ij.

FRAC equals 2 times the logarithm of patch perimeter (m) divided by the logarithm of patch area (m2); the perimeter is adjusted to correct for the raster bias in perimeter.

1 ≤ FRAC ≤ 2


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A fractal dimension greater than 1 for a 2-dimensional patch indicates a departure from Euclidean ge-ometry (i.e., an increase in shape complexity). FRAC approaches 1 for shapes with very simple pe-rimeters such as squares, and approaches 2 for shapes with highly convoluted, plane-filling perimeters.

4.6.4.4 Square Pixel Index (SqP)

SqP index is an equivalent shape index, which represents the landscape in a better way than fractal index. It was well proven by Robert C. Frohn, Research Professor at Miami University, California. It represents the shape complexity in a more effective manner.

It is calculated as per the following formula:

SqP = P / 4 √A

Where P= Perimeter of the patch

A= Area of the patch

The value of SqP can range from 1 to infinity.

More the SqP value more is the complexity.

4.6.5 Focus of the study & Method followed

To look into how the shape of some select patches of the TOF change across various spatial resolu-tions.

The Various patches selected for shape analysis were

1. A compact patch of TOF consisting of mango trees with more than 80 percent crown density approximately roughly of dimension 150m x 75m.

2. Two patches of road side plantation of mixed species (R1&R2) consisting of 150 m long and 15 m wide along both sides of the road in the study area.

3. Two linear patches (150m x 5m) and (115m x 5m) of eucalyptus along field bunds. These are referred to as L1 and L2 respectively.

4. A stand alone mango tree of crown diameter approx. 16m in the paddy field.

These patches delineated on IKONOS – PAN image are as given in Fig. 4.5

Fig. 4.5 IKONOS – PAN image of selected TOF patches for Shape Analysis

R1 R2

L2 L1


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The selection of various TOF patches was made on the fact that these are the most commonly occur-ring patterns in the Indian context. Two patches of equal dimension of TOF (R1& R2) on road side were selected as the typical linear alignment. This will also help to analyse the capacity of the various sensors to discern the road corridor of around 20m. in between. Linear patches of TOF along the field bunds were selected as the represent an important feature of agro forestry plantation feature in India. Selection of single tree of average crown diameter was made as it is the basic unit and most common type of TOF.

These patches were masked out from all the classified images and analysed. Visual perception of the patches in various classified image being the first method employed followed by the computation of all the four shape indices viz., PARA, SHAPE, FRAC, SqP. The results and analysis are discussed in the section 5.3.1 of chapter 5.

The methodology followed for the shape analysis is as depicted in Fig. 4.6

Fig. 4.6. Methodology for Shape Analysis

4.7 Minimum Detectable Patch

Individual TOF crown was taken as the smallest recognisable unit of patch. The recognition of individual TOF crowns was possible only over a certain range of spatial resolution. For example at finer resolution, even the branches may be evident whereas in a coarser scale, individual TOF crowns may merge with their neighbours. Hence it will be interesting to evaluate the minimum detectable TOF patch for each resolution and its neighbourhood effect.

The various steps followed in determing the minimum detectable patch were as shown in Fig. 4.7 From the unsupervised classified output images, TOF patches were delineated by making use of the

LINEAR TOF PATCH ON FIELD BUNDS

UNSUPERVISED CLASSIFIED OUTPUTS (REAL SENSORS, SIMULATED & FUSED DATA SETS)

SINGLE TREE COMPACT TOF PATCH

LINEAR TOF PATCH ON ROAD SIDE

SUBSETTING FEATURES OF INTER-EST BASED ON SHAPE

COMPARATIVE ANALYSIS OF TOF SHAPE AMONGST VARIOUS RESOLU-

TIONS

VISUAL PERCEPTION

CALCULATION OF SHAPE INDICES


27

clump operation. From this three different isolated single TOF pixels with varying degree of TOF were selected from each outputs.Though the RMS error for georeferencing for all images were kept to a minimum, a maximum shift of one pixel with respect to the reference image was expected. Hence a one pixel buffer area was considered around all the selected pixels and the TOF present in the pixel in question as well as in its neighbourhood were visually interpreted by on screen digitisation. The TOF area pertaining to the selected and the neighbourhood pixels were estimated from the TOF vector layer obtained through visual interpretation as marked in Fig. 4.8.

Fig. 4.7 Steps followed in determing the minimum detectable patch

UNSUPERVISED CLASSIFIED OUTPUTS

SEGREGATION OF ISOLATED PIXELS OF TOF

(CLUMP OPERATION)

SELECTION OF PIXEL OF INTEREST

RECODING

BUFFERING THE SELECTED PIXEL

MASK OUT THE SELECTED AREA FROM REFERENCE DATA

(IKONOS – PAN)

TOF AREA ESTIMATION IN THE SELECTED PIXEL AND IN BUFFER PIXELS

TOF AREA COMPARISON IN THE SELECTED PIXELS


28

A

B Fig.4.8 Single Pixel of TOF and its Buffer A A sketch showing the TOF pixel (violet colour) and its buffer area (surrounding 8 pixels). The green and red patches represent TOF area. B shows the IKONOS PAN image equivalent of 9 ETM pixels, the central pixel in violet (classified TOF pixel) and the buffer area around it. 4.8 Comparison of Simulated and Original Data Sets The four simulated data sets (simulated from IKONOS MSS - 4m spatial resolution and 3 band spectral resolution (Green, Red and Infra red) viz. at 16m, 24m, 32m and 60m were intended to be compared with real sensor data sets which were available for the study. As the real sensor data sets were not available at exact spatial resolution for comparison, hence they were resampled. Aster data of 15m spatial resolution and 3 band spectral resolution (Green, Red and Infra red) was resampled to 16m and Landsat ETM of 30m spatial resolution and 3 band spectral resolution was resampled to 32m using Nearest Neighbor resampling technique. IRS 1D LISS III and Resource Sat AWIFS were already resampled at 24m and 60m respectively from the begining of the study. A three pronged ap-proach was applied to make a comparison as shown in Fig. 4.9. Fig. 4.9 Aspects in Simulated and Real Data Set Comparison

COMPARISON OF SIMULATED

DATA SETS WITH REAL SENSOR DATA

STATISTICAL COMPARISON

CLASSIFICATION ACCURACY

COMPARISON

VISUAL COMPARISON


29

4.8.1 Visual Comparison

Visual comparison was the initial step employed to have an idea how the simulated data sets depicted the various features compared to those in the real sensor dat sets, more so the TOF.

4.8.2 Statistical Comparison

In the statistical comparison mean, standard deviation and coeffficient of correlation betwen the respective bands of the simulated data sets and the data from real sensors were intended to be compared and consequently were computed as such. Though the standard deviation is usually the best measure of spread, since the radiometery of the IKONOS MSS sensor data set is 11 bits (sensor recording DN values from 0 to 2047) while the real sesors data sets have 8 bits radiometry (DN values recorded from 0 to 255), it was futile to compare the mean and standard deviation as such. Hence another measure to compare the two data sets viz. the coefficient of variation was employed.

Co-efficient of Variation is defined as the ratio of standard deviation to its mean i.e.,

Coefficient of Variation = Std. Dev. / Mean

It is given as a percentage and is used to compare the consistency or variability of two or more series. The higher the C. V., the higher the variability and lower the C. V., the higher is the consistency of the data. It is especially useful if a comparison is to be made between two sets of data that have different units of measure. It also helps to express the variation in a single data set as a percentage of the mean value. Hence to help compare the variations of the two data sets, relative measures of standard devia-tion, i.e. CV was calculated.

The coefficient of variation and the coefficient of correlation were therefore computed and their sig-nificance was found out. The results are discussed in Chapter 5.

4.8.3 Classification Accuracy Comparison

Correct Classification of the land cover classes is one of the important factors dertermining the usabilty of the satellite data. In an attempt to find the similarity and differences between the simulated and the corresponding real sensor data of the same spatial resolution the clasified images available in the study were compared. In this direction the overall classification accuracy, producer’s accuracy and the TOF area estimates were compared. The results are tabulated in Chapter 5.


30

Chapter 5: Results & Discussion 5.1 Classification Accuracy The error matrices generated by taking reference points in various ways as discussed in chapter 4.5.3 are as given in the Appendices A-III to A-VII. The various images and their classified outputs are as shown in the Figure 5.1 (a) and (b). A cursory look of the various classified outputs gives the im-pression that finer resolutions up to 16 meter could demarcate TOF effectively. Even the scattered and isolated TOF could be classified from its undergrowth vegetation especially in IKONOS fused image. Linear plantation of TOF along the field bunds could only be classified in IKONOS MSS (4m) and IKONOS fused (1m) as can be clearly seen in the Fig.5.1

Fig. 5.1 (a) Standard FCC and classified outputs (simulated data sets)


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Fig. 5.1 (b) Standard FCC and Classified Outputs (original sensors and fused data sets)

The Fig.5.2 shows the variation in overall accuracy, producer’s accuracy, user’s accuracy and overall kappa statistics for the simulated as well as for original sensors and fused data sets when 100 refer-ence points each on the two classes (TOF and non TOF) were taken from visually interpreted IKO-NOS PAN image. It shows that the overall accuracy recorded highest in 8m resolution (in case of simulated data) and in 4m (in case of real sensors) and then gradually declines before it again reaches a higher value around 32m in simulated data sets while among original data sets it record higher value for ETM sensor, and then declines. The producer’s accuracy also shows the same trend as that of over all accuracy. The kappa value follows exactly the same pattern of variation as that of producer’s accuracy but is lower than producer’s accuracy. The user’s accuracy recorded much higher values in all the cases compared to producer’s accuracies, signifying there by higher errors of omissions for the TOF class. Among the fused images, viz. 1m and 6m, 1 m image shows lower overall, producer’s and user’s accuracies s compared to its constituent MSS image, but the other fused image of 6m shows higher overall as well as higher producer‘s accuracies but only slightly lower user’s accuracy com-pared to its constituent MSS image. How ever the both the fused images recorded higher accuracies in comparison to other data sets.


32

VARIATIO N O F ACCURACIES IN SIMULATED DATA SETS

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Fig.5.2 Variation in Overall, Producer’s & User’s Accuracies and Overall Kappa statistics. The Fig. 5.3 shows the variation in overall accuracy for three different ways of reference points selec-tion as marked from A to C. The over all accuracy for the simulated data sets show that when the ref-erence points are taken from IKONOS PAN image, the overall accuracy is highest for 8m and beyond which starts declining but does reach a peak at 32m and starts declining once again. When the refer-ence points are taken from LISS III classified image highest accuracy occurs around 32m and shows lower values on both the sides of 32m. At 16m, 24m and also at 40m shows almost the same value. In the third case when the reference points are taken from AWIFS classified image, the highest accuracy is recorded at 60m and declines as the resolution becomes finer for 48 and 40m before which rises again at 32m, drops again at 24m and then rises once again at 16m before another decline.

As far as the original and fused data sets are concerned, when the reference points are from IKONOS PAN image the highest overall accuracy is for IKONOS MSS (4m), drops for ASTER and LISS III successively and then records a higher value for ETM and drops again. When the reference points are taken from LISS III classified image overall accuracy drops on both sides of 24m resolution. When the reference points are taken from AWIFS there is a successive drop in the overall accuracies for ETM, LISS III and ASTER before it rises at IKONOS MSS.

For the IKONOS fused image the overall accuracy was low in comparison to its constituent IKONOS MSS but the case was reverse for the IRS fused image when the reference points are taken from visu-


33

ally interpreted IKONOS PAN image. It may be due to the fact that aggregation of the TOF features in IKONOS MSS occurred in same way as the visually interpreted IKONOS PAN image., while IK-ONOS fused image could record more heterogeneity. IRS fused image also recoded higher overall accuracy next to IKONOS MSS showing that the aggregation occurred in a more or less similar man-ner.

Fig. 5.4 shows the variation in producer’s accuracy for simulated, original and fused data sets. When the reference points are taken from IKONOS PAN image simulated data sets show highest accuracy at 8m and shows a decline till 24m and another peak at 32m and then drops further. When the reference points are from LISSIII classified image the highest value is reached at 32m and drops on both the sides as shown but increases again for 60m. In the third case when the reference points are from AWIFS classified image are taken, a zig zag pattern of rise and fall is noticed. The highest value how-ever is recorded at 32m and 60m.In the original and fused data sets for the case A of Fig.5.4 the high-est producer’s accuracy is recorded for IKONOS MSS followed by IRS fused and then IKONOS fused. AWIFS and LISS III recorded lowest values. For case B drop in accuracy is noticed on both sides of LISS III. Fused images however recorded higher values, IRS fused (45%), and IKONOS fused (23%) in contrast to 13% by IKONOS. For the case C. it shows successively lower values in

VARIATION OF OVERALL ACCURACY IN SIMULATED DATA SETS

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Fig. 5..3 Variation in Overall accuracies for different methods of reference points selection A. Reference points taken from visually interpreted IKONOS pan image B. Reference points taken from LISS III classified image C. Reference points taken from AWIFS classified image


34

ETM, LISS III, ASTER however there is a marginal increase in IKONOS MSS. But overall very low values in other resolutions. IRS fused image and IKONOS fused images recorded 20 and 16 percent respectively, higher than 10% recorded by IKONOS MSS

VARIATION IN PRODUCER'S ACCURACY FOR SIMULATED RESOLUTION

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Fig. 5.5 (a) and Fig. 5.5 (b) shows the variation in overall accuracy, overall kappa, producer’s accuracy and user’s accuracy by two ways of accuracy estimation for the simulated data sets and original and fused data sets respectively. Series A refers to the variation in accuracies when the random reference points are generated on the IKONOS PAN visually interpreted image and transferred on to the classified images and accuracies estimated Series B refers to the variation in accuracies when the random points are taken on the individual classified im-ages and compared with the references obtained from visually interpreted IKONOS PAN image.. It is evident that overall accuracy, overall kappa and producer’s accuracy all are higher in case of B at all levels , only the user accuracy remains higher in case A . It is also noticed that the overall accuracy and producer’s accuracy is highest near 32 meters of spatial resolution.

Fig. 5.4 Variation in Producer’s accuracies of TOF for different methods of reference points selection A. Reference Points Taken From Visually Interpreted IKONOS PAN Image B. Reference points taken from LISS III classified image C. Reference points taken from AWIFS classified image


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VARIATION IN OVERALL ACCURACY

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Fig 5.5 (b) Accuracy variation in original and fused data sets A. Random reference points taken from on visually interpreted IKONOS PAN image B. Sample test points taken on individual classified images & reference from IKONOS PAN

VARIATIO N O F O VERALL ACCURACY

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Fig.5.5 (a) Accuracy variation in simulated data sets A. Random reference points taken from visually interpreted IKONOS PAN image B. Sample test points taken on individual classified images & reference from IKONOS PAN


36

Fig 5.6 shows the accuracy variation when those reference points are considered which were falling on the edges and small patches of TOF on visually interpreted IKONOS image. As expected the pro-ducer’s accuracy for TOF was low in almost all cases, so also were Kappa values and user’s accuracy in comparison to when random reference points were selected from the IKONOS PAN image. Proba-bly this could be one of the reasons of lower producer’s accuracies of TOF in various classified im-ages.

VARIATION IN ACCURACY

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Fig. 5.6 Variation in accuracy due to edge points

5.2 TOF Area Estimation Table 5.1 gives the details of TOF estimates in different data sets while Fig. 5.7 shows the graphical representation of the same. It is noticed that the area estimate closest to the reference map was re-corded in the fused images of IKONOS and IRS sensors, followed by 32 simulated data set and ETM sensor.


37

Table 5.1. TOF area estimation in different data sets

TOF AREA ESTIMATION

0

5

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20

25

30

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m)

IKONOS FUSED (1

m)

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SS(4m)

IRS FUSED

SIMULATED 8m

ASTER(15m)

ASTER(16m)

SIMULATED 16

m

LISSIII (24

m)

SIMULATED 24

m

ETM(30m)

ETM(32m)

SIMULATED 32

m

SIMULATED 40

m

SIMULATED 48

m

AWIFS(60m)

SIMULATED 60

m

DATA SETS

TO

F A

RE

A (h

a)

TOF AREA

Fig.5.7 TOF area estimation in different data sets

SL NO. DATA SET SPATIAL TOF RESOLUTION(m) (ha) 1 SIMULATED 8 15.03 16 13.85 24 12.15 32 25.09 40 17.28 48 15.9 60 23.4 2 ORIGINAL SENSORS IKONOS MSS 4 18.85 ASTER 15 21.71 LISSIII 24 10.89 ETM 30 22.14 AWIFS 60 18.36 3 RESAMPLED DATA SETS ASTER 16 20.22 ETM 32 16.69 4 FUSED DATA SETS IKONOS (MSS & PAN) 1 27.18 IRS 1D LISS III & PAN 6 26.97 REFERENCE 5 IKONOS PAN VECTOR 27.15 IKONOS PAN RASTER 27.05


38

5.3 Shape Analysis

5.3.1 Visual Appeal on Various Images The images of various patches of TOF viz., compact, roadside, along field bunds and single tree as seen in different spatial resolutions (standard FCC) along with their classified outputs are shown in Fig. 5.8 (a) to (d). Standard FCCs are shown for all the patches in the resolution dealt with, however only those classified images are given in which patches could get classified, others are shown blank Table 5.2 gives the summary of the occurrence and non occurrence of the various patches of TOF in the classified outputs. From the classified images TOF areas other than patch are suppressed for better visualization. This was done by overlaying the visually interpreted IKONOS PAN vector layer on the images. .


39

A. COMPACT PATCH Fig:5.8(a) FCC CLASSIFIED IMAGE FCC CLASSIFIED IMAGE

SIMULATED (8m) IKONOS MSS (4m)

SIMULATED (16m) ASTER (15m)

SIMULATED (24m) IRS 1D LISS III (24m)

SIMULATED (32m) ETM (30m)

SIMULATED (40m) AWIFS (60m)

SIMULATED (48m) IKONOS FUSED (MSS & PAN) (1m)

SIMULATED (60m) IRS FUSED (LISS III & PAN) (6m)


40

B. ROADSIDE LINEAR PATCH Fig:5.8(b) FCC CLASSIFIED IMAGE FCC CLASSIFIED IMAGE


SIMULATED (16 m) ASTER (15m)

SIMULATED (24 m) IRS 1D LISS III (24m)

SIMULATED (32 m) ETM (30m)

SIMULATED (40 m) AWIFS (60m)

SIMULATED (48 m) IKONOS FUSED (MSS & PAN) (1m)

SIMULATED (60 m) IRS FUSED (LISS III & PAN) (6m)


41

C. LINEAR PATCHES ALONG FIELD BUNDS Fig:5.8(c) FCC CLASSIFIED IMAGE FCC CLASSIFIED IMAGE

SIMULATED (8m) IKONO S MSS (4m)

SIMULATED (16 m) ASTER (15 m)

SIMULATED (24 m) IRS 1D LISS III (24m)

SIMULATED (32 m) ETM (30m)

SIMULATED (40m) AWIFS (60m)


SIMULATED (60m) IRS 1D (LISS III & PAN) (6m)


42

D. SINGLE TREE Fig:5.8(d) FCC CLASSIFIED IMAGE FCC CLASSIFIED IMAGE


SIMULATED (16m) ASTER (15m)

SIMULATED (24m) IRS 1D LISS III (24m)

SIMULATED (32m) ETM (30m)

SIMULATED 40m) AWIFS (60m)


SIMULATED (60m) IRS 1D FUSED (LISS III & PAN) (6m)


43

Table 5.2. Summary of occurrence of various patches of TOF in different resolutions

TOF PATCH APPROX. DIMEN-SIONS

IKONOS PAN(REF)

SIMULATED DATA SET (RESOLUTION IN M)

ORIGINAL SENSORS FUSED

1m 8 16 24 32 40 48 60 IKONOS MSS

ASTER

IRS 1D LISSIII

LANDSAT ETM

RESOURCE SAT AWIFS

IKONOS ) (MSS & PAN) 1m

IRS 1D (LISS III & PAN)(6m)

COMPACT 150mX75m √ √ √ √ √ √ √ √ √ √ X √ X √ √

R1: 150m X 15m √ √ √ √ X √ X √ X √ √

LINEAR ALONG ROADSIDE

R2: 150mX 15m √ √ √

√ √ √

X √

√

X X X √ √

L1: 150m X 5m √ √ X X X X X X √ X X X X √ X

LINEAR ALONG FIELD BUNDS L2: 115m X

5m √ X X X X X X X √ X X X X √ X

SINGLE TREE

Crown dia: 16m √ X X X X X X X X X X X X √ X

√:Presence X: Absence Merged cells represent merging of the patches


44

5.3.2 Shape Indices

Various shape indices to understand the shape complexity namely PARA, SHAPE, FRAC and SqP are calculated for the different patches and the values are as shown in Appendix VIII. The variation in the two of the indices, SHAPE and SqP are discussed in the subsequent paragraphs.

A. Compact Patch

The compact patch could be seen in all the resolutions of simulated data set as well as in both the fused data sets of IKONOS and IRS. So far as the original sensors are concerned it was visible in IK-ONOS MSS, ASTER and ETM but could not be captured by IRS 1D LISS III and Resource Sat AWIFS. Almost all the shape indices show a decrease in value as the resolution becomes coarser sug-gesting shape simplicity of the patch with the coarser resolution as shown in Fig. 5.9. All the shape indices had the highest value for the fused IKONOS data set.

COMPARISON OF SHAPE COMPLEXITY FOR COMPACT PATCH

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

IKONOS P

AN(REF)

FUSE

D IKONOS

IKONOS M

SS

FUSE

D IRS

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSE

D IRS

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATA SETS

SHA

PE

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

SqP

SqPSHAPE

Fig. 5. 9. Variation of Shape indices of compact patch with resolution

B. Linear Patch – Road Side Both the rows of roadside plantation viz., R1 and R2 were seen separately in 8m and 16m of simulated data sets as well as in IKONOS –MSS and IKONOS – Fused. Only R1 was seen in 24m simulated and Landsat ETM while a single row (merging of R1 and R2) appeared in 32m, 40m, 48m of simulated data sets and in Aster and also in IRS – Fused. Here also the shape indices show that complexity of shape reduces as the resolution becomes coarser. IKO-NOS Fused image recorded highest value for all indices. The SHAPE and SqP variation for different patches obtained is shown in 5.10 (a), (b), (c).


45

'SHAPE' & 'SqP' - PATCH(R1)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

IKONOS P

AN(REF)

FUSED

IKONOS

IKONOS M

SS

FUSED IR

S

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSE

D IRS

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATASETS

SHA

PE

0.00

2.00

4.00

6.00

8.00

10.00

12.00

SqP SHAPE

SqP

Fig. 5.10 (a) Variation of SHAPE and SqP for patch R1

'SHAPE' & 'SqP' -PATCH (R2)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

IKONOS P

AN(REF)

FUSED IK

ONOS

IKONOS M

SS

FUSED IR

S

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSED

IRS

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATSETS

SHA

PE

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

SqP SHAPE

SqP

Fig. 5.10 (b) Variation of SHAPE and SqP for patch R2

'SHAPE' & 'SqP'- R1 & R2 MERGED

0

0.5

1

1.5

2

2.5

IKONOS P

AN(REF)

FUSED IK

ONOS

IKONOS M

SS

FUSED IR

S

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSED IR

S

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATA SETS

SHA

PE

0

0.5

1

1.5

2

2.5

SqP SHAPE

SqP

Fig. 5.10(c) Variation of SHAPE and SqP for patch R1 and R2 merged


46

C. Linear Patch – Along Field Bunds

In the simulated data set of 8m, only one (L1) out of the two linear patches considered could be classified. Both the linear patches got classified in IKONOS MSS and IKONOS Fused im-ages. Other data sets could not classify any of the two patches. SHAPE and SqP variations are shown in Table 5.11 (a) and (b).

'SHAPE' & 'SqP'-TOF PATCH(L1)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

IKONOS P

AN(REF)

FUSED IK

ONOS

IKONOS M

SS

FUSED IR

S

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSED IR

S

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATASETS

SHA

PE

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

SqP SHAPE

SqP

Fig. 5.11(a) Variation of SHAPE and SqP for linear patch L1

'SHAPE' & 'SqP' -TOF PATCH (L2)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

IKONOS

PAN(REF)

FUSED IK

ONOS

IKONOS M

SS

FUSED IR

S

SIM

ULATE

D 8m

ASTER

SIMULA

TED16

m

FUSED IR

S

SIMULA

TED24

m E

TM

SIMULA

TED32

m

SIMULA

TED 40

m

SIMULA

TED 48

m

AW

IFS

SIMULA

TED60

m

DATASETS

SHA

PE

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

SqP SHAPE

SqP

Fig. 5.11(b) Variation of SHAPE and SqP for linear patch (L2)

D. Single Tree

The single tree measured 195 sq.m and had perimeter 66 m but it could not be captured in any of the resolutions except in classified IKONOS Fused image and that too only as few scattered pixels falling along the periphery of the crown making its shape complex.


47

5.4 Minimum Patch Area

The pixels classified as TOF in all the resolutions depicted varying degree of TOF presence.

The various statistics related to this exercise are shown in Table 5.12 & 5.13. As an example three single pixels analysed from 48 m simulated data set are shown in Fig. 5.12. TOF pixel II in the simu-lated data set of 48m spatial resolution had an only an area of 75.32 sq. m of TOF but was classified into TOF pixel, its area wise contribution was only 3.27 percent of the pixel area and represented trees and its surroundings on the field bund. This can be attributed to a shift in one pixel with respect to the reference image as the buffer area TOF presence estimates to 1131.35 sq.m. Also it is observed that in many cases, that the neighbourhood sugar cane and paddy fields also had an impact on the classifi-cation of this particular type of TOF (along field bunds).

Fig. 5.12 Single TOF pixels of 48m simulated resolution and its neighbourhood as seen in the reference image (Classified TOF pixel is shown as square)

Among the original sensors, IKONOS MSS recorded a patch of area 8.01 sq.m as the minimum detectable patch which correspond to almost half the area of the recorded pixel. This forms a part of the tree crown only and the nearby pixels are classified into other category.

TOF PIXEL I TOF PIXEL II TOF PIXEL III


48

Table 5.12 Minimum detected TOF area in different data sets

MIN DETECTED PATCH

TOF AREA (sq. m.) IN

% TOF AREA IN (sq m) % TOF

TOF TOF TOF TOF TOF TOF

SL NO.

SENSOR

SPATIAL RESOLUTION

(m)

AREA OF ONE PIXEL

(sq.m) PIXEL I PIXELII PIXELIII PIXELI PIXEL II PIXEL III

1 IKONOS MSS 4 16 13.92 8.01 12.54 86.99 50.09 78.34 8.01 50.09

2 ASTER 15 225 56.74 191.18 150.99 25.22 84.97 67.11 56.74 25.22

3 LISS III 24 576 360.06 233.48 486.74 62.51 40.53 84.50 233.48 40.53

4 ETM 32 1024 nil 308.61 559.60 0.00 30.14 54.65 308.61 30.14

5 AWIFS 60 3600 2048.41 394.00 1629.86 56.90 10.94 45.27 394.00 10.94

SIMULATED

8 64 21.348 22.887 42.99 33.35 35.76 67.17 21.348 33.35

6 16 256 242.64 171.26 116.77 94.78 66.90 45.61 116.77 45.61

7 24 576 142.90 123.98 305.83 24.81 21.52 53.10 123.98 21.52

8 32 1024 351.23 621.00 253.00 34.30 60.64 24.71 253.00 24.71

9 40 1600 206.30 504.35 515.56 12.89 31.52 32.22 206.30 12.89

10 48 2304 437.12 75.32 840.87 18.97 3.27 36.50 75.32 3.27

11 60 3600 1655.33 576.67 305.16 45.98 16.02 8.48 305.16 8.48


49

TOF AREA PRESENT (sq.m) AROUND

SL. NO.

DATA SETS

TOF PIXEL I

TOF PIXELII

TOF PIXELIII

1 IKONOS MSS 126.439 42.718 43.125 2 ASTER 381.68 654.11 371.60

3 LISS III 1100.22 464.81 1658.38 4 ETM 542.77 2557.83 4382.50

5 AWIFS 6762.92 2749.09 14735.07

SIMULATED 6 8m 0 16.518 97.371

7 16m 1350.08 617.94 159.03 8 24m 481.99 234.67 2303.12 9 32m 1144.22 1408.58 995.46

10 40m 1135.07 1100.36 1279.70 11 48m 1276.35 1131.35 1592.63

12 60m 5305.75 3108.2 2857.34

Table 5.13 TOF area in neighboring eight pixels of the selected TOF pixel

5.5 Comparative Analysis of Data Sets – Original and Simulated

5.5.1 Visual Analysis

The simulated data sets and their corresponding original data sets are shown as in Fig 5.13. The standard FCC of simulated data set at 16m when compared with ASTER (resampled at 16m) show better clarity of features especially the TOF and the field boundaries. Linear TOF patches as well as compact patches were more prominent in the simulated data sets. Almost similar effect was noticed when indivdual bands were compared. The infra red band of the simulated data show a high contrast which make the features more distinct.While comparing the standrad FCC of simulated data sets at 24m, 32m and 60m with IRS LISS III, ETM (resampled to 32m) and AWIFS(60m) respectively show that within class heterogeneity is more pronounced in simulated data sets than their original counterparts.


50

SIMULATED (16M) ASTER (RESAMPLED 16M)

STANDARD FCC

STANDARD FCC

GREEN BAND

GREEN BAND

RED BAND

RED BAND

INFRA RED BAND

INFRA RED BAND


51

STANDARD FCC

IRS ID LISS IIISTANDARD FCC

SIMULATED (24M)

STANDARD FCC

LANDSAT ETM (32M)STANDARD FCC

SIMULATED (32M)

STANDARD FCC

RESOURCE SAT AWIFS (60M)STANDARD FCC

SIMULATED (60M)Fig. 5.13 Visual Comparison of Simulated images and corresponding original sensor images

5.5.2 Statistical Analysis

A. Comparison of Coeffiecient of Variation

The Coefficient of Variations of different bands of the data sets, viz., simulated and original sensor are shown in the Table 5.14 It is noticed that for the ASTER and LISS III, the Coefficient of Variation falls within a range of roughly 10% of that in corresponding simulated data sets in all the bands, while for ETM and AWIFS their Coefficient of Variations vary more than 10% with their counterpart in most of the bands, only exception being red band of AWIFS. But it can not be ascertained as to if it is always the Original sensor data sets which show more variability per unit mean since it changes be-tween different data sets and also in between bands.


52

* Higher values of means & standard deviation in simulated data sets due to 11 bits of radiometric resolution in contrast to 8 bits for other sensors considered here.

Table 5.14 Comparison of coefficient of variations

MEANS

STANDARD DEVIATION COEFFICIENT OF VARIATION SL NO.

DATA SETS

IR BAND

RED BAND

GREEN BAND

IR BAND

RED BAND

GREEN BAND

IR BAND

RED BAND

GREEN BAND

ASTER(16m) 41.45 33.252 47.134 6.252 7.939 6.733 6.63 4.19 7 1 SIMULATED(16m) 513.661* 338.712 399.986 85.889* 75.908 64.566 5.98 4.46 6.2

LISSIII(24m) 83.4 59.793 72.342 14.842 12.515 9.574 5.62 4.78 7.56 2 SIMULATED(24m) 513.569 338.906 400.151 77.686 70.205 59.316 6.61 4.82 6.75

ETM (32m) 69.879 40.137 38.364 15.929 13.535 7.763 4.39 2.97 4.94 3 SIMULATED(32m) 513.877 338.474 399.839 71.566 66.131 55.647 7.18 5.12 7.19

AWIFS(60m) 173.551 74.575 85.85 15.694 11.952 6.998 11.06 6.24 12.27 4 SIMULATED(60m) 514.282 338.764 400.152 58.502 57.341 48.042 8.79 5.91 8.33


53

Test of Significance of Coeffiecient of Variation

Test of signifance was conducted as to find out if the difference between the Coefficient of Variation was significant or not. The critical value of Z for this exercise is as shown in the Table 5.15 It is found that in all cases the differece was significant (at 95% confidence) as Z value was much more than 1.96, the only exception being Red bands of LISSIII and AWIFS sensors which recorded lower value, suggesting thereby that the Coefficient of Variation of red bands in LISSIII and AWIFS sensors were not significnatly different from thier simulated counterpart while for all others the difference was significant.

Z Value SL NO.

DATA SETS IR BAND RED BAND GREEN BAND ASTER(16m) 1

SIMULATED(16m) 12.255

7.435

14.39

LISSIII(24m) 2


0.664

9

ETM (32m) 3


45

21.609

AWIFS(60m) 4


1.763

12.198

Table 5.15 Significance of Coeffiecient of Variation

B. Comparison of Coeffiecient of Correlation between various bands

The coefficients of correlations were computed between the respective bands of the original sensor datasets and those from the simulated ones. The results are shown in the Table:5.16

Table 5.16 - Comparison of coefficient of correlation

SL NO. DATA SETS IR BAND

RED BAND

GREEN BAND

NUMBER OF PIXELS

ASTER(16m) 14012 1 SIMULATED(16m)

0.0298

0.4852

0.4841 14364

LISSIII(24m) 6308 2 SIMULATED(24m)

(-) 0.0089

0.2293

0.2338 6384

ETM (32m) 3534 3 SIMULATED(32m)

0.0325

0.3521

0.3886 3591

AWIFS(60m) 1054 4 SIMULATED(60m)

(-) 0.1672

0.3086

0.2542 1054


54

Test of Significance of correlation coefficients

The critical value of coefficient of correlation at α = 0.01 level(two sided) is equal to 0.182 (for n = 200). The number of data (pixels) in all the cases exceed 200. Hence the coefficient of correlations for the red and green bands of original sensors and simulated data sets in all cases exceed the critical value and are therefore significant. In case of infra red bands in all the four cases it is noticed that the coefficient of correlation is below the critical value of 0.182 , and hence not significant (Stein,1999).

5.5.3 Classification Accuracy & TOF Area Comparison

Table 5.17 gives the comaprison of overall accuracy and TOF area estimated between the simulated data sets and those from the original at similar spatial resolution. The the Figures 5.14(a) and (b) give its graphic representation. It is noticed that simuated data set at 32m spatial resolution comes closet to its counetrpart ETM (32m)in terms of overall and producer’s accuracies. But for the TOF area estimated simulated data set at 24m came closet to its counterpart, though none of them were near the reference value.

SL.NO DATA SETS

OVERALL CLASSIFICATION ACCURACY (%)

PRODUCER ACCURACY

OF TOF

TOF AREA

(ha)

1 ASTER(16m) 65 33 20.22 2 SIMULATED 16m) 63 28 13.85 3 LISSIII(24m) 54.5 10 10.89 4 SIMULATED(24m) 60.5 23 12.15 5 ETM(32m) 64 31 16.69 6 SIMULATED(32m) 63.5 33 25.09 7 AWIFS(60m) 53.5 10 18.36 8 SIMULATED(60m) 58 18 23.4

Table 5.17 Comparison of accuracy


55

ACCURACY COMPARISON

0

10

20

30

40

50

60

70

80

90

100

ASTER(16

m)

SIMULA

TED 16

m

LISSIII(

24m)

SIMULA

TED (2

4m)

ETM(32

m)

SIMULA

TED (3

2m)

AWIFS

(60m)

SIMULA

TED (6

0m)

DATA SETS

% A

CC

UR

AC

Y

OVERALL ACCURACYPRODUCER'S ACCURACYUSER'S ACCURACY

Fig. 5.14(a) Comparison of overall, producer’s and user’s accuracy

TOF AREA

0

5

10

15

20

25

30

ASTER(16

m)

SIMULA

TED (1

6m

LISSIII(

24m)

SIMULA

TED (2

4m)

ETM(32

m)

SIMULA

TED(32

m)

AWIFS

(60m)

SIMULA

TED (6

0m)

IKONOS P

AN(1m)

DATA SETS

TO

F A

RE

A (h

a)

TOF AREA

Fig. 5.14(b) TOF Area estimation


56

6. Conclusions & Recommendation

A. The effect of change in spatial resolution on classification accuracy and area statistics The TOF in the study area exhibit a variety in spatial patterns and distribution. The extent and shape of the patches comprising these TOFs also vary widely. The study explored the potential of various available data sets, to discern these objects of interest and how unsupervised classification results matched with the reference data (visually interpreted IKONOS PAN image). It is assumed in the study that there is a relation between sensor regularisation/aggregation and ground scene attributes (TOFs). To evaluate this generalisation and there by positional accuracy, different ways of reference points selection and accuracy testing were conducted and error matrices generated. 1. The highest overall classification accuracy was recorded by IKONOS MSS at 73% followed by simulated data set at 8m which recorded 68%, however the area estimates of the TOF were low, 18.55 ha and 15.03 ha respectively in comparison to the reference area of approximately 27 ha. However both the fused data sets viz. IKONOS (MSS & PAN) and IRS (LISSIII & PAN) recorded reasonably high accuracies at 65 and 66.5% and also gave a very accurate estimate of the area, both recording al-most the reference value area of 27 ha. Among others, relatively higher value of overall accuracy was recorded by ETM at 64% and by simulated data of 32m at 63.5 % respectively and also giving rea-sonably good area estimate at 21.14 ha and 25.04 ha respectively. The lowest accuracy was recorded by 40m spatial resolution at 54.5% while for real sensors it was for AWIFS at 53.5%.The same trend was noticed for the Producer’s accuracy for the TOF class. IKONOS MSS recoding the highest at 48% followed by 8m simulated data set (36%), IRS fused (38%), IKONOS fused (35%), 32 m simulated data set (33%) and ETM (31%). As TOF patches in the study area exhibit spatial heterogeneity, sensors with smaller instantaneous field of view (smaller than the average size of the ground scene) viz IKONOS PAN (1m) in this case, produce images with high variance. The fused image produced from this IKONOS PAN could inherit this heterogeneity. The classified output produced from this fused product is expected to show high accuracy. But in the present study the highest overall accuracy was recorded by IKONOS MSS (73%) which has a resolution of 4m. One of the reasons might be the limits imposed by minimum mapping unit of visual interpretation of the IKONOS PAN image i.e. though visual interpretation is done at largest possible scale; digitally classified image may contain many small clusters of single class pixels that were too small to be included in the interpretation process. 2. It was also noticed that the higher accuracy figures were recorded for all spatial resolutions when the random points were generated on the various classified images independently and accuracies cal-culated with the reference rather than when the same reference points were used for checking the ac-curacies. IKONOS MSS over-all accuracy rose to 80% from 73%, 8m simulated data set from 68% to 78.57%, IKONOS fused from 65% to 73.85% and IRS fused from 66.5% to 80%.

3. The overall accuracy changed depending upon different methods of reference points selection, which were selected form reference IKONOS PAN image, LISSIII classified image and from AWIFS classified images. The overall accuracy for the simulated data sets showed that when the reference


57

points are taken from IKONOS PAN image, the overall accuracy was highest for 8m and beyond which it started to decline but reached a peak at 32m once again before declining. When the reference points were taken from LISS III classified image highest accuracy occurred around 32m and showed lower values on both the sides of 32m. At 16m, 24m and also at 40m showed almost the same value. When the reference points were taken from AWIFS classified image, the highest accuracy was re-corded at 60m and declined as the resolution became finer for 48 and 40m before, rose at 32m, and dropped again at 24m and then showed increase in value at 16m before another decline occurred.

Among the original sensor and fused data sets when the reference points were taken from IKONOS PAN image the highest overall accuracy was obtained for IKONOS MSS (4m), lowerer for ASTER and LISS III successively. For ETM it was higher value and a drop for AWIFS. When the reference points were from LISS III classified image overall accuracy dropped on both sides of 24m resolution. When the reference points are taken from AWIFS there was a successive drop in the overall accuracies for ETM, LISS III and ASTER before it showed increase at IKONOS MSS.

Hence in the finer domain of scale, fused images of IKONOS and IRS appear to be a better choice as they can be used effectively to classify TOF patches of minimal dimension i.e. those which are located on road side and field bunds. In the medium domain of scale ETM of 30 m resolution found to provide satisfactory results with respect to accuracy and area statistics. Other than spatial resolution variables like size and distribution of TOF, IFOV of the sensor, the support area and the spectral band widths ot the data sets also contribute to the classification accuracy and there by area statistic. Hence for ascer-taining the results arrived at from the present study, a similar study may be conducted on a different location with a different TOF distribution pattern.

B. Shape analysis of TOF patches

1. The compact patch of TOF or approx. dimension 150m X 75 m could be classified in all the resolu-tions of simulated and in both the fused data sets of IKONOS and IRS. However among the original sensors it got classified in IKONOS MSS, ASTER and ETM but could not be captured by IRS 1D LISS III and Resource Sat AWIFS. Almost all the shape indices of the classified patch showed a de-crease in value as the resolution became coarser suggesting shape simplicity of the patch. All the shape indices had the highest value for the classified patch in fused IKONOS.

2.. The two rows of roadside plantation viz., R1 and R2, both of dimension 150m x 15m each could be classified and were seen separately in 8m and 16m of simulated data sets, as well as in IKONOS MSS and fused IKONOS. However only R1 was classified in 24m simulated and ETM. The two rows got merged as a single row in 32m, 40m, and 48m of simulated data sets, in ASTER and also in fused IRS. The shape indices showed that complexity of shape re-duced as the resolution became coarser. IKONOS Fused image recorded highest value for all indices. 3. The two linear patches of TOF roughly of dimension 150m x 5m and 115m x 5m along the field bunds got classified in IKONOS MSS and fused IKONOS; however one of the two lin-ear patches only could be classified in the simulated data set of 8m. Other data sets could not classify any of the two patches.

4. The single tree measuring 195 sq.m and perimeter of 66 m could not be captured in any of the resolutions except in IKONOS Fused image and that too only as few scattered pixels fal-ling along the periphery of the crown making its shape complex.


58

C. Minimum detectable TOF patch

1.. The minimum patch areas of TOF on ground which could be classified as one pixel in dif-ferent data sets were IKONOS MSS(8.01 sqm), ASTER(56.74 sq.m), LISS III (233.48 sq.m), ETM (308.61 sq.m), AWIFS (394.00), for simulated 8m(21.34 sq.m), 16m (116.77 sq.m), 24m (123.98 sq.m), 32m (253.00sq.m), 40m (206.30 sq.m), 48m (75.32 sq.m) and 60m (305.16).

D. Statistical comparison of simulated and original sensor data sets

The statistical comparison of simulated data set at 16m, 24m, 32m and 60m with ASTER resampled to 16m, LISS III (24m), ETM resampled to 32m and AWIFS (60m) revealed that the coefficient of variation of the respective bands of the corresponding simulated and original sensor data sets were significantly different at 95% confidence for almost all the pairs in all the bands, the only exception being red bands of LISSIII and AWIFS sensors , suggesting thereby that the Coefficient of Variation of red bands in LISSIII and AWIFS sensors were not significnatly different from thier simulated counterpart while for all others the difference was significant. The coefficient of correlation computed for the corresponding bands of all the four pairs showed that at α = 0.01 level though the red and green bands of original and simulated data sets were significantly correlated but the infra red bands were not. It was noticed that simuated data set at 32m spatial resolution came closet to its counetrpart ETM (32m) in terms of overall and producer’s accuracy of TOF class.


59

7. References

Amrhein, C., and H. Reynolds (1996). Using spatial statistics to assess aggregation effects, Geo-graphical Systems, Vol. 3, pp. 143-158.

Allen, T.F.H., and Hoekstra, T.W. (1991). Role of heterogeneity in ecological systems under analysis. In Ecological Studies 86: Ecological Heterogeneity, J.Kolasa, and S.T.A. Pickett, (eds). Springer-Verlag, pp. 47-68.

Atkinson, P. M. and Curran, P. J. (1995). Defining an optimal size of support for remote sensing in-vestigations. IEEE Transactions on Geoscience and Remote Sensing, 33, 768-776.

Beals, E.W. (1969). Vegetation change along altitudinal gradients, Science, Vol. 165, pp. 981-985

Benson, B.J., and M.D. MacKenzie (1995). Effects of sensor spatial resolution on landscape structure parameters, Landscape Ecology, Vol.10, No.2, pp.113-120.

Bloschl, G., and M. Sivapalan. (1995). Scale issues in hydrological modelling: a review, Hydrological Processes, Vol. 9, pp. 251-290.

Cao, Changyong and Nina Siu-Ngan Lam (1997) Understanding the Scale and Resolution Effects in Remote Sensing and GIS. Scale in Remote Sensing and GIS. Dale A. Quattrochi and Michael F. Goodchild, Eds. Boca Raton, FL: CRC Lewis, pp 57-72.

Carlile et al., (1989). Methods for detecting scale and dispersion of plant cover. Landscape Ecology, Landscape Ecology, Vol. 2, pp. 203-213.

Congalton, R.G., (1991). A Review of assessing the accuracy of classifications of remotely sensed Data, Remote Sensing of Environment, 37:35-46.

Curtis, E. Woodcock., Alan, H. Strahler, and David L.B. Jupp.(1988).The use of Variograms in Re-mote Sensing: Scene Models and Simulated Images. Remote Sensing of Environment, Vol. 25 pp. 323-348.

De Cola, L., (1989), Fractal analysis of a classified Landsat scene, PE&RS, 55, 601.

De Jong S.M. & P.A. Burrough, (1995), A Fractal Approach to the Classification of Mediterranean Vegetation Types in Remotely Sensed Images. Photogram metric Engineering & Remote Sensing 61, No.8, pp.1041-1053.

Davis, I.G.S (2001). WP9 Report: Field site analysis. Tegucigalpa, Hondura: IHCAFE.

Ehleringer, J.R., and C.B. Field. 1993. Scaling Physiological Processes. Leaf to Globe. Academic Press. pp.388.

Foody, G.M. & Curran, P.J. (1994). Scale and environmental remote sensing. In Environmental Re-mote Sensing from Regional to Global Scales. Foody, G.M. & Curran, P.J. (eds) Wiley, Chichester, pp.223-232.


60

Forman, R.T.T. & Godron, M. (1986). Habitat fragmentation. Definition Landscape ecology. 69:468_475. Wiley. New York.

Frohn, R.C., (1988). Remote Sensing for landscape ecology. Lewis Publishers, pp. 9-32.

FSI. (2002). State of Forest Report 2001. Dehradun: Forest Survey of India.

Goodall, D.W. (1974). A new method for analysis of spatial pattern by random pairing of quadrats, Vegetation, Vol. 29, pp. 135-146.

Goodchild, M.F., and D.A. Quattrochi (1997). Scale, multiscaling, remote sensing and GIS. In Scale in Remote Sensing and GIS, Quattrochi, D.A., and M.F.Goodchild. eds., pp.1-11.

Hay, G.J., Marceau, D.J., Dube, P. and Bouchard, A., (2001). A multiscale framework for landscape analysis: object-specific analysis and upscaling. Landscape Ecology 16 6, pp.471-490.

Hunt, L., and B. Boots (1996). MAUP effects in the principal axis factoring technique, Geographical Systems, Vol. 3, pp. 101-121.

Iverson, L. R. 1989. Land use changes in Illinois, USA: the influence of landscape attributes on cur-rent and historic land use. Landscape Ecology 2:45-61.

Kotliar, N.B. & Wiens, J.A. (1990). Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos 59:253-260.

Krummel, J.R., Gardner, R.H., Sugihara, G., O'Neill, R.V., Colman, P.R. (1987). Landscape patterns in a disturbed environment. Oikos 48:321-324.

Gustafson, E.J. (1998). Using spatial models to link forest management, landscape pattern and wildlife response. Proceedings, 1998 NCASI Central-Lake States Regional Meeting, Duluth, MN, May 12-13, 1998.

Jarvis, (1995) Scaling processes and Problems Plant, Cell, Environment, Vol.18, pp.1079 -1089.

Jensen, John R.1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Engle-wood Cliffs, New Jersey: Prentice – Hall.

Lam, N and Quattrochi, D.A., (1992).On the issues of scale, resolution, fractal analysis in the mapping sciences, Prof. Geogr., 44, pp. 88-98.

Legendre, P., and Fortin, M.-J. (1989). Spatial pattern and ecological analysis. Vegetation 80: pp. 107- 138.

Levin, S.A., (1992). The problem of pattern and scale in ecology, Ecology, Vol.73, pp.1943-1967.

Levin, S.A., (1993). Concepts of scale at the local level. In Scaling Physiological Processes: Leaf to Globe, ed. J.R. Ehleringer, and C.B. Field, Academic Press, pp. 7-19.

Lovejoy, S. & Schertzer, C. (1995). How bright is the coast of Brittany? In Fractals in Geoscience & Remote Sensing. Wilkinson, G.G., Kanellopoulos, I. & Megier, J. (eds). Joint Research Centre, ECSC-EC-ESAC, Brussels, pp.102-151.


61

Ludwig, J.A., and D.W. Goodall. (1978). A comparison of paired with blocked-quadrat variance methods for the analysis of spectral pattern, Vegetation, Vol. 38, pp. 49-59.

Ludwig, J.A., and Cornelius. J.M (1987). Locating discontinuities along ecological gradients, Ecol-ogy, Vol. 68, pp. 448-450.

Marceau, D.J (1999). The scale issue in social and natural sciences. Canadian Journal of Remote Sens-ing, Vol. 25, No. 4, pp. 347-356.

Marceau, D.J., and G.J. Hay. (1999). Remote sensing contributions to the scale issue, Canadian Jour-nal of Remote Sensing, Vol. 25, pp.357-366.

Martin, D.J., 1988, Census Geography (2001) designed by and for GIS? In S.Carver(ed), Innovations in GIS 5(London: Taylor and Francis), 198-209.

McBratney, A.B., and R. Webster. (1986). Choosing functions for semi-variograms of soil properties and fitting them to sampling estimates, Journal of Soil Science, Vol. 37 .pp.617-639.

McGarigal, K., and B. J. Marks. (1995). FRAGSTATS: spatial pattern analysis program for quantify-ing landscape structure. USDA For. Serv. Gen. Tech. Rep. PNW-351.

Michael A. W., and Franklin, S.E., (2003). Remote sensing of forest environments, introduction- The transition from theory to Information. In Remote Sensing of Forest Environments. Concepts and Case Studies. Wulder, M.A., and Franklin, S.E., (eds). Kluwer Academic Publishers, pp. 1-12.

Milne, B. T. 1988. Measuring the fractal geometry of landscapes. Applied Mathematics and Computa-tion 27:67-79.

Mulla, D. J. (1988). Using geostatistics and spectral analysis to study spatial patterns in the topogra-phy of south-eastern Washington State, U.S.A. Earth Surface Processes and Landforms, 13, pp. 389-405

Oliver, M.A., and R. Webster. (1986). Semi-variograms for modelling the spatial pattern of land form and soil properties, Earth Surface Processes and Landforms, Vol.11, pp.491-504.

Openshaw, S., and P.J. Taylor. (1979). A million of so correlation coefficients: Three experiments on the modifiable areal unit problem. In Statistical Applications in the Spatial Sciences, (eds). N. Wrig-ley, Pion, London, pp. 127-144.

Quattrochi, D.A. and R.E. Pelletier, (1991). Remote sensing for spatial analysis of landscapes: Ques-tions and examples. In Quantitative Methods in Landscape Ecology, M.G. Turner and R.H. Gardner (eds). Springer-Verlag, New York, NY, 51-76.

Renshaw, E., and E.D.Ford. (1984). The description of spatial pattern using two-dimensional spectral analysis, Vegetation. Vol.56, pp.75-85.

Ripley, B.D. (1978). Spectral analysis and the analysis of pattern in plant communities. Journal of Ecology, Vol.66.pp. 965-981.

Raupach, M.R., and J.J. Finnigan. (1995). Scale issues in boundary-layer meteorology: surface energy balances in heterogeneous terrain, Hydrological Processes, Vol. 9, pp. 589-612.


62

Rawat, J.K., Dasgupta, S., Kumar, Rajesh., Kumar, Anoop., Chauhan, K.V.S. (2003): Training Man-ual on Inventory of Trees Outside Forests published by FAO under the EC-FAO Partnership Pro-gramme.

Ripple, W. J., G. A. Bradshaw, and T. A. Spies. (1991). Measuring landscape pattern in the Cascade Range of Oregon, USA. Biological Conserv. 57:73-88.

Schuerholz, G. (1974). Quantitative evaluation of edge from aerial photographs, Journal of Wildlife Management, Vol. 38, pp. 913-920.

Simonett, David S., (1983) The development and principles of remote sensing. Manual of Remote Sensing, Robert N. Colwell (ed.) American Society of Photogrammetry.

Stein, A. (2002). Some basic elements of statistics. In Spatial statistics for Remote Sensing, Alfred Stein, Freek van Der Meer & Ben Gorte (eds). Kluwer Academic Publications, pp. 9-25.

Turner, M.G., V.H. Dale, and R.H. Gardner. (1989). Predicting across scales: Theory development and testing, Landscape Ecology, Vol. 3, pp. 245-252.

Turner, M.G. & Gardner, R.H., eds. (1991). Quantitative methods in landscape ecology. Springer-Verlag. New York.

Vollenweider, R.A. (1975). Input-output models with special reference to the phosphorus loading con-cept in limnology, Schwiz. Z. Hydrol., Vol. 37, pp. 53-84.

Vani, K and S. Sanjeevi (2002). Fusion of Aster image data for enhanced mapping of landcover fea-tures, Map India Proceedings.(http://www.gisdevelopment.net/proceedings/mapindia/2002/tep.htm)

Wiegert, R.G. (1962). The selection of an optimum quadrat size for sampling the standing crop of grasses and forbs, Ecology, Vol. 43, pp. 125-129.

Wiens, J.A. (1989). Spatial scaling in ecology, Functional Ecology, Vol. 3, pp. 385-397.

Wilson, M.V., and C.L. Mohler. (1983). Measuring compositional change along gradients, Vegetation, Vol. 54, pp. 129-141.

Woodcock, C.E. and Strahler, A.H., (1987). The factor of scale in remote sensing. Remote Sensing of Environment 21, pp. 311-332.


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APPENDIX A-I : CONCEPTS & TERMINOLOGIES

Resolution

a. Spatial resolution

Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the sensor (Jensen, 1996). In remote sensing it refers to the fineness of detail visi-ble in an image. It generally correspond to ground pixel size. For example, the ASTER has a nominal spatial resolution of 15x15 m for its visible-NIR bands, Landsat ETM+ has a nominal spatial resolu-tion of 30x30m for six of its bands and LISS III has spatial resolution of 23.5x23.5 m in its first three bands. The instantaneous field of view a sensor sees is taken as a square of its spatial resolution. How-ever it does not mean that the objects smaller than this size can not be detected by this sensor. It may be possible for object smaller than the pixel size to be detected if either it falls exactly within a pixel or it has large variation in contrast with its surrounding so that its presence affects the whole pixel. The smaller the spatial resolution, the greater the resolving power of the sensor system.

b. Spectral Resolution

Spectral resolution refers to the specific wavelength intervals in the electromagnetic spectrum of that a satellite sensor can record (Simonett, 1983). It can also be defined as the number and dimension of specific wavelength intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive. For example, band 1 of the Landsat TM sensor records energy between 0.45 and 0.52 µm in the visible part of the spectrum. The spectral bands containing wide intervals in the electromagnetic spectrum are referred to as coarse spectral resolution and narrow intervals are referred to as fine spec-tral resolution. For instance the SPOT panchromatic sensor is considered to have coarse spectral reso-lution because it records EMR between 0.51 and 0.73 µm. On the other hand, band 3 of the Landsat TM sensor has a finer spectral resolution because it records EMR between 0.63 and 0.69 µm (Jensen, 1996).

c. Radiometric resolution

Radiometric resolution defines the sensitivity of a detector to differences in signal strength as it records the radiant flux reflected or emitted from the number of just-discriminable signal levels; con-sequently, it can be a significant element in the identification of scene objects. This is referred by the number of bits into which the recorded energy is divided. e.g. MSS (Multispectral scanners on IKO-NOS records the reflected radiant flux in 11 bits (values ranging from 0 to 2047) whereas IRS LISS III does it in 7 bits and then expanded the data in 8 bits (values ranging from 0 to 255) after processing. Improvement in resolution generally increases the probability that phenomenon can be detected with remote sensing more accurately.

d. Temporal Resolution

The temporal resolution of a remote sensing system refers to how often it records imagery of particular area. For example, Landsat ETM+ can view the same area of the globe once very 16 days, while LISS III visits the same area every 24th day. Sensors obtain data repetitively to capture unique discriminat-


64

ing characteristics of the objects. The temporal resolution of a satellite sensor is very much important in change detection and analysis.

Cartographic Scale and Colloquial Scale

Cartographic scale is the ratio of the size of a representation of an object to its actual size. Colloquial scale is a synonym for “size”. For example, a map of a residential compound showing houses would be at a large cartographic scale (e.g., 1:500) but a small colloquial scale (e.g., ‘it’s a small scale phe-nomenon’), while a map of a country would be at a small cartographic scale (e.g., 1:1 million) but a large colloquial scale (e.g., ‘it’s a large scale phenomenon).

What is Scaling?

Scaling means transferring data or information from one scale to another. Upscaling consists of taking information at larger scales (high spatial resolution) to derive processes at smaller scales (lower spatial resolution), while downscaling consists of decomposing information at one scale into its con-stituents at larger scales (Jarvis, 1995).


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APPENDIX A-II: IMAGE INTERPRETATION KEY FOR TOF DELINEATION USING IKONOS PAN DATA

CATEGORY TONE CROWN SHAPE SHADOW LOCATION TEXTURE SIZE PATTERN

Individual Tree 1. Isolated Black to Circular Present in Mostly in Coarse around 2 -18m

whitish grey almost all cases agriculture fields crown diameter. 2. In regular Grey to black Circular Present in all As orchards Very Smooth around 4 -8m Regular

pattern cases crown diameter . 3. In irregular Grey Circular/ Present in majority As orchards Medium around 3- 10 m Irregular

pattern irregular of the cases crown diameter.

Trees as Clusters 1. High crown density Greyish White to not discernible Present on edges As plantation/ Medium to area

(compact) greyish black and gaps orchards Smooth 0.5 – 3 acres 2. Low crown density Whitish grey to discernable in Present plantation/ Medium to

(scattered) greyish black certain cases natural growth Smooth

Trees as linear patches 1. Along road Whitish Grey to discernable in Present As plantation Coarse 150 - 200 m

greyish black certain cases mainly eucalyptus long and Intermittent 12-15m wide linear stretch

2. Along field Whitish grey discernable in Present As plantation Coarse 100 - 150 m bunds certain cases long and

3 – 5m wide


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APPENDIX A-III

ERROR MATRIX WHEN REFERENCE POINTS TAKEN FROM VISUALLY INTERPRETED IKONOS PAN IMAGE

SIMULATED DATA SETS SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional Kappa 1 8m TOF 36 0 36 36 100 68 1 0.36 OTHERS 64 100 164 100 60.98 0.2195 TOTAL 100 100 2 16m TOF 28 2 30 28 93.33 63 0.8667 0.26 OTHERS 72 98 170 98 57.65 0.1529 TOTAL 100 100 3 24m TOF 23 2 25 23 92 60.5 0.84 0.21 OTHERS 77 98 175 98 56 0.12 TOTAL 100 100 4 32m TOF 33 6 39 33 84.62 63.5 0.6923 0.27 OTHERS 67 94 161 94 58.39 0.1677 TOTAL 100 100

100

5 40m TOF 15 6 21 15 71.43 54.5 0.4286 0.09 OTHERS 85 94 179 94 52.51 0.0503 TOTAL 100 100 6 48m TOF 14 4 18 14 77.78 55 0.5596 0.1 OTHERS 86 96 182 96 52.75 0.0549 TOTAL 100 100 7 60m TOF 18 2 20 18 90 58 0.8 0.16 OTHERS 82 98 180 98 54.44 0.0889 TOTAL 100 100

ORIGINAL SENSOR DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional Kappa 1 IKONOS MSS TOF 48 2 50 48 96 73 0.92 0.46 4m OTHERS 52 98 150 98 65.33 0.3067 TOTAL 100 100 2 ASTER TOF 29 6 35 29 82.86 61.5 0.6571 0.23 15m OTHERS 71 94 165 94 56.97 0.1394 TOTAL 100 100 3 LISSIII TOF 10 1 11 10 90.91 54.5 0.8182 0.09 24m OTHERS 90 99 189 99 52.38 0.0476 TOTAL 100 100 4 ETM TOF 31 3 34 31 91.18 64 0.8235 0.28 32m OTHERS 69 97 166 97 58.43 0.1687 TOTAL 100 100 5 AWIFS TOF 10 3 13 10 76.92 53.5 0.5385 0.07 60m OTHERS 90 97 187 97 51.87 0.0374 TOTAL 100 100

RESAMPLED DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional Kappa 1 ASTER TOF 33 3 36 33 91.67 65 0.8333 0.3 16m OTHERS 67 97 163 97 59.51 0.1829 TOTAL 100 100 2 ETM TOF 23 1 24 23 95.83 61 0.9167 0.22 32m OTHERS 77 99 176 99 56.25 0.125 TOTAL 100 100

FUSED DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional Kappa 1 IKONOS (MSS

&PAN PAN)TOF 35 5 40 35 87.5 65 0.75 0.3

& PAN) OTHERS 65 95 160 95 59.38 0.1875 1m TOTAL 100 100 2 IRS 1D (LISS III TOF 38 5 43 38 88.37 66.5 0.7674 0.33 & PAN) OTHERS 62 95 157 95 60.51 0.2102 6m TOTAL 100 100


67

APPENDIX A-IV ERROR MATRIX WHEN REFERENCE POINTS FALLING ON EDGES & SMALL

PATCHES OF TOF TAKEN FROM VISUALLY INTERPRETED IKONOS PAN IMAGE SIMULATED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa

NO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional K

1 8m TOF 7 0 7 12.96 100 69.48 1 0.1621

OTHERS 47 100 147 100 68.03 0.0882 TOTAL 54 100 2 16m TOF 13 2 15 24.07 86.67 72.08 0.7947 0.2647 OTHERS 41 98 139 98 70.5 0.1588 TOTAL 54 100 3 24m TOF 6 2 8 11.11 75 67.53 0.615 0.1133 OTHERS 48 98 146 98 67.12 0.0624 TOTAL 54 100 4 32m TOF 10 6 16 18.52 62.5 67.53 0.4225 0.1494 OTHERS 44 94 138 94 68.12 0.0907 TOTAL 54 100 5 40m TOF 4 6 10 7.41 40 63.64 0.076 0.0173 OTHERS 50 94 144 94 65.28 0.0098 TOTAL 54 100 6 48m TOF 2 0 2 3.7 100 66.23 1 0.0476 OTHERS 52 100 152 100 65.79 0.0244 TOTAL 54 100 7 60m TOF 7 2 9 12.96 77.78 68.18 0.6578 0.1356 OTHERS 47 98 145 98 67.59 0.0756 TOTAL 54 100

ORIGINAL SENSOR DATA SETSSL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics


1 IKONOS MSS TOF 17 2 19 31.48 89.47 74.68 0.8379 0.3465 4m OTHERS 37 98 135 98 72.59 0.2184 TOTAL 54 100 2 ASTER TOF 13 0 13 24.07 68.42 69.48 0.5137 0.2124 15m OTHERS 41 100 141 94 69.63 0.1339 TOTAL 54 100 3 LISSIII TOF 6 1 7 11.11 85.71 68.18 0.78 0.1264 24m OTHERS 48 99 147 99 67.35 0.0688 TOTAL 54 100 4 ETM TOF 13 3 16 24.07 81.25 71.43 0.7113 0.2514 32m OTHERS 41 97 138 97 70.29 0.1527 TOTAL 54 100 5 AWIFS TOF 5 3 8 9.26 62.5 66.23 0.4225 0.0778 60m OTHERS 49 97 146 97 66.44 0.0429 TOTAL 54 100 RESAMPLED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics


1 ASTER TOF 8 3 11 14.81 72.73 68.18 0.58 0.1446 16m OTHERS 46 97 143 97 67.83 0.0826 TOTAL 54 100 2 ETM TOF 10 1 11 18.52 90.91 70.28 0.86 0.2145 32m OTHERS 44 99 143 99 69.23 0.1225 TOTAL 54 100 FUSED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa NO RESOLUTION Classification STATISTICS Statistics


1 IKONOS (MSS & PAN) TOF 17 5 22 31.48 77.27 72.73 0.65 0.3066 1m OTHERS 37 95 132 95 71.97 0.2006 TOTAL 54 100 2 IRS 1D (LISS III &

PAN)TOF 13 5 18 24.07 72.22 70.13 0.5722 0.2253

PAN) OTHERS 41 95 136 95 69.85 0.1403 6m TOTAL 54 100


68

APPENDIX A-V ERROR MATRIX WHEN TEST POINTS TAKEN ON INDIVIDUAL CLASSIFIED

IMAGES SIMULATED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall K

NO RESOLUTION Classification STATISTICS Statistics TOF OTHERS TOTAL Accuracy Conditional

K

1 8m TOF 58 22 80 87.88 72.5 78.57 0.4797 0.5749 OTHERS 8 52 60 70.27 86.67 0.7172 TOTAL 66 74 2 16m TOF 59 21 80 86.76 73.75 76.92 0.4496 0.5335 OTHERS 9 41 50 66.13 82 0.6559 TOTAL 68 62 3 24m TOF 61 19 80 89.71 76.25 80 0.502 0.5957 OTHERS 7 43 50 69.35 86 0.7324 TOTAL 68 62 4 32m TOF 56 24 80 94.92 70 79.23 0.4507 0.5993 OTHERS 3 47 50 66.2 94 0.8678 TOTAL 59 71 5 40m TOF 59 21 80 89.39 73.75 78.46 0.4668 0.5677 OTHERS 7 43 50 67.19 86 0.7242 TOTAL 66 64 6 48m TOF 37 32 69 72.55 53.62 61.34 0.1884 0.2441 OTHERS 14 36 50 52.94 72 0.3467 TOTAL 51 68 7 60m TOF 28 37 65 87.5 43.08 64.35 0.2113 0.3259 OTHERS 4 46 50 55.42 92 0.7125 TOTAL 32 83

ORIGINAL SENSORS DATA SETS

SL SENSOR CLASS PA UA Overall KAPPA (K^) Overall Kappa


TOF OTHERS TOTAL Accuracy Conditional

Kappa 1 IKONOS MSS TOF 57 23 80 95 71.25 80 0.4661 0.607 4m OTHERS 3 47 50 67.14 94 0.87 TOTAL 60 70 2 ASTER TOF 49 31 80 84.48 61.25 69.23 0.3003 0.3995 15m OTHERS 9 41 50 56.94 82 0.2944 TOTAL 58 72 3 LISSIII TOF 47 33 80 87.04 58.75 69.23 0.663 0.4077 24m OTHERS 7 43 50 56.58 86 0.6475 TOTAL 54 76 4 ETM TOF 48 32 80 90.57 60 71.54 0.3247 0.454 32m OTHERS 5 45 50 58.44 90 0.7547 TOTAL 53 77 5 AWIFS TOF 14 37 51 63.64 27.45 55.45 0.0725 0.1139 60m OTHERS 8 42 50 53.16 84 0.2655 TOTAL 22 79

RESAMPLED ASTER & ETM




Kappa 1 ASTER TOF 52 28 80 88.14 65 73.08 0.3592 0.4728 16m OTHERS 7 43 50 60.56 86 0.6915 TOTAL 59 71 2 ETM TOF 56 24 80 84.85 70 73.85 0.3906 0.4751 32m OTHERS 10 40 50 62.5 80 0.6061 TOTAL 66 64

FUSED IKONOS & IRS




Kappa 1 FUSED IKONOS TOF 53 27 80 88.33 66.25 73.85 0.3732 0.486 (MSS & PAN) OTHERS 7 43 50 61.43 86 0.6967 1m TOTAL 60 70 2 FUSED IRS TOF 59 21 80 92.19 73.75 80 0.483 0.6014 (MSS & PAN) OTHERS 5 45 50 68.18 90 0.7969 6m TOTAL 64 66


69

APPENDIX A-VI ERROR MATRIX WHEN REFERENCE POINTS TAKEN FROM LISS III

CLASSIFIED IMAGE SIMULATED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall K

NO RESOLUTION Classification STATISTICS Statistics TOF OTHERS TOTAL Accuracy Conditional Kappa 1 8m TOF 14 4 18 14 77.78 55 0.5556 0.1 OTHERS 86 96 182 96 52.75 0.0549 TOTAL 100 100 2 16m TOF 22 2 24 22 91.67 60 0.8333 0.2 OTHERS 78 98 176 98 55.68 0.1136 TOTAL 100 100 3 24m TOF 22 2 24 22 91.67 60 0.8333 0.2 OTHERS 78 98 176 98 55.68 0.1136 TOTAL 100 100 4 32m TOF 36 5 41 36 87.8 65.5 0.7561 0.31 OTHERS 64 95 159 95 59.75 0.195 TOTAL 100 100 5 40m TOF 24 5 29 24 82.76 59.5 0.6552 0.19 OTHERS 76 95 171 95 55.56 0.1111 TOTAL 100 100 6 48m TOF 18 6 24 18 75 56 0.5 0.12 OTHERS 82 94 176 94 53.41 0.0682 TOTAL 100 100 7 60m TOF 28 3 31 28 90.32 62.5 0.8065 0.16 OTHERS 72 97 169 97 57.4 0.1479 TOTAL 100 100

ORIGINAL SENSOR DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall

KNO RESOLUTION Classification STATISTICS Statistics

TOF OTHERS TOTAL Accuracy Conditional Kappa 1 IKONOS MSS TOF 13 4 17 13 76.47 54.5 0.5294 0.09 4m OTHERS 87 96 183 96 52.46 0.0492 TOTAL 100 100 2 ASTER TOF 30 4 34 30 88.24 63 0.7647 0.26 15m OTHERS 70 96 166 96 57.83 0.1566 TOTAL 100 100 3 LISSIII TOF REFERENCE 24m OTHERS TOTAL 4 ETM TOF 27 5 32 27 84.38 61 0.6875 0.22 32m OTHERS 73 95 168 95 56.55 0.131 TOTAL 100 100 5 AWIFS TOF 12 2 14 12 85.71 55 0.7143 0.1 60m OTHERS 88 98 186 98 52.69 0.0538 TOTAL 100 100

RESAMPLED DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall


TOF OTHERS TOTAL Accuracy Conditional Kappa 1 ASTER TOF 34 2 36 34.44 94.44 66.33 0.8894 0.3245 16m OTHERS 65 98 163 98 60.12 0.1984 TOTAL 99 100 2 ETM TOF 21 6 27 21.65 77.78 58.38 0.5622 0.1582 32m OTHERS 76 94 170 94 55.29 0.0921 TOTAL 97 100

FUSED DATA SETSSL SENSOR CLASS PA UA Overall KAPPA (K^) Overall


TOF OTHERS TOTAL Accuracy Conditional Kappa 1 IKONOS (MSS &PAN) TOF 22 2 24 22.92 91.67 61.22 0.8367 0.2124 1m OTHERS 74 98 172 98 56.98 0.1216 TOTAL 96 100 2 IRS 1D(LISS III & PAN) TOF 45 8 53 45 84.91 68.5 0.6981 0.37 6m OTHERS 55 92 147 92 62.59 0.2517 TOTAL 100 100


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APPENDIX A-VII

ERROR MATRIX WHEN TEST POINTS TAKEN FROM AWIFS CLASSIFIED IMAGESIMULATED DATA SETS

SL SIMULTED CLASS PA UA Overall KAPPA (K^) Overall Kappa

NO RESOLUTION Classification STATISTICS Statistics TOF OTHERS TOTAL Accuracy Conditional Kappa 1 8m TOF 4 1 5 8.16 80 50.54 0.5773 0.056 OTHERS 45 43 88 97.73 48.86 0.0295 TOTAL 49 44 2 16m TOF 9 1 10 18.37 90 55.91 0.7886 0.154 OTHERS 40 43 83 97.73 51.81 0.0853 TOTAL 49 44 3 24m TOF 6 3 9 12 66.67 53 0.3333 0.06 OTHERS 44 47 91 94 51.65 0.033 TOTAL 50 50 4 32m TOF 10 5 15 20 66.67 55 0.3333 0.1 OTHERS 40 45 85 90 52.94 0.0588 TOTAL 50 50 5 40m TOF 6 4 10 12 60 52 0.2 0.04 OTHERS 44 46 90 92 51.11 0.0222 TOTAL 50 50 6 48m TOF 9 2 11 18 81.82 57 0.6364 0.14 OTHERS 41 48 89 96 53.93 0.0787 TOTAL 50 50 7 60m TOF 10 2 12 20 83.33 58 0.6667 0.16 OTHERS 40 48 88 96 54.55 0.0909 TOTAL 50 50

ORIGINAL SENSOR DATA SETS

SL SENSOR CLASS PA UA Overall KAPPA (K^) Overall NO RESOLUTION Classification STATISTICS Kappa

TOF OTHERS TOTAL Accuracy Conditional Kappa Statistics 1 IKONOS MSS TOF 5 2 7 10.2 71.43 50.54 0.3961 0.054 4m OTHERS 44 42 86 95.45 48.84 0.029 TOTAL 49 44 2 ASTER TOF 4 3 7 8 57.14 47.31 0.0731 0.0096 15m OTHERS 46 40 86 93.02 46.51 0.0051 TOTAL 50 43 3 LISSIII TOF 5 1 6 10.2 83.33 51.61 0.6477 0.0755 24m OTHERS 44 43 87 97.73 49.43 0.0401 TOTAL 49 44 4 ETM TOF 11 1 12 22.45 91.67 58.51 0.8259 0.1957 32m OTHERS 38 44 82 97.78 53.66 0.111 TOTAL 49 45 5 AWIFS TOF REFERENCE 60m OTHERS TOTAL

RESAMPLED DATA SETS

SL SENSOR CLASS PA UA Overall KAPPA (K^) Overall NO RESOLUTION Classification STATISTICS Kappa

TOF OTHERS TOTAL Accuracy Conditional Kappa Statistics 1 ASTER TOF 7 2 9 14.29 77.78 52.69 0.5303 0.0931 16m OTHERS 42 42 84 95.45 50 0.051 TOTAL 49 44 2 ETM TOF 7 0 7 14.29 100 54.84 1 0.1362 32m OTHERS 42 44 86 100 51.16 0.0731 TOTAL 49 44

FUSED DATA SETS


NO RESOLUTION Classification STATISTICS Statistics TOF OTHERS TOTAL Accuracy Conditional Kappa 1 IKONOS TOF 8 5 13 16.33 61.54 50.54 0.1871 0.0476 1m OTHERS 41 39 80 88.64 48.75 0.0273 TOTAL 49 44 2 IRS TOF 10 2 12 20.41 83.33 55.91 0.6477 0.1521 6m OTHERS 39 42 81 95.45 51.85 0.0862 TOTAL


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APPENDIX A-VIII

SHAPE INDICES SL. TYPE OF DATA SET SPATIAL PATCH STATISTICS SHAPE INDICES REMARKS NO TOF RESOLU- AREA PERIMETER NO. OF PERIME- MIN. PERIME- PARA SHAPE FRAC SqP

PATCH (m) (Sq.m) (m) PIXELS (NO. OF (NO. OF CELL SUR- SURFACES 1 COMPACT IKONOS PAN(REF) 1 11689 484 11689 484 434 0.0414 1.1152 1.0240 1.1192 SIMULATED 8 8896 1168 139 146 48 0.1313 3.0417 1.2485 3.0959 16 5376 704 21 44 20 0.1310 2.2000 1.2039 2.4004 24 4608 576 8 24 12 0.1250 2.0000 1.1783 2.1213 32 6144 384 6 12 10 0.0625 1.2000 1.0465 1.2247 40 4800 320 3 8 8 0.0667 1.0000 1.0339 1.1547 48 2304 192 1 4 4 0.0833 1.0000 1.0000 1.0000 60 3600 240 1 4 4 0.0667 1.0000 1.0000 1.0000 ORIGINAL SENSORS IKONOS MSS 4 8688 1760 543 440 94 0.2026 4.6809 1.3422 4.7206 ASTER 15 9450 690 42 46 24 0.0730 1.9167 1.1253 1.7745 IRS 1D LISSIII 24 - - - - LANDSAT ETM 30 6300 360 4 32 8 0.0571 4.0000 1.0287 1.1339 RESOURCE SAT 60 - - - - - - - - - FUSED IKONOS (MSS & PAN) 1 6083 8924 6083 2231 312 1.4670 7.1506 1.7698 28.6049 IRS 1D (LISS III & 6 7308 1248 203 208 58 0.1708 3.5862 1.2910 3.6497 2 LINEAR IKONOS PAN(REF) 1 1979 470 1979 470 178 0.2375 2.6404 1.2559 2.6413 R1 ALONG 1 2105 444 2105 444 86 0.2109 5.1628 1.2309 2.4193 R2 ROAD- SIMULATED 8 640 224 10 38 14 0.3500 0.5000 1.2460 2.2136 R1 448 192 7 24 9 0.4286 2.6667 1.2682 2.2678 R2 16 512 128 2 8 6 0.2500 1.3333 1.1111 1.4142 R1 256 64 1 4 4 0.2500 1.0000 1.0000 1.0000 R2 24 1152 192 2 8 6 0.1667 1.3333 1.0983 1.4142 R1 32 3072 320 3 10 8 0.1042 1.2500 1.0914 1.4434 R1 & R2 40 3200 240 2 8 6 0.0750 1.3333 1.0146 1.0607 R1 & R2 48 4608 384 2 8 6 0.0833 1.3333 1.0822 1.4142 R1 & R2 60 - - - - - - - - - ORIGINAL SENSORS IKONOS MSS 4 576 368 576 368 96 0.6389 3.8333 1.4228 3.8333 R1 480 384 480 384 88 0.8000 4.3636 1.4786 4.3818 R2 ASTER 15 5400 690 24 46 20 0.1278 2.3000 1.1986 2.3474 R1 & R2 IRS 1D LISSIII 24 - - - - - - - - - LANDSAT ETM 30 1800 240 2 8 6 0.1333 1.3333 1.0925 1.4142 R1 RESOURCE SAT 60 - - - - - - - - - FUSED IKONOS (MSS & PAN) 1 1017 1426 1017 1426 128 1.4022 11.1406 1.6972 11.1789 R1 951 1584 951 1584 124 1.6656 12.7742 1.7445 12.8412 R2

IRS 1D (LISS III & PAN) 6 5616 624 156 100 50 0.1111 2.000 1.1698 2.0817 R1 & R2


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Continued. SHAPE ANALYSIS SPATIAL PATCH STATISTICS SHAPE INDICES

RESOLUTION AREA PERIMETER NO. OF PERIME- MIN. PERIME- PARA SHAPE FRAC SqP (m) (Sq.m) (m) PIXELS (NO. OF (NO. OF CELL

SL. NO

TYPE OF TOF PATCH

DATA SET SUR- SURFACES

3 LINEAR IKONOS PAN(REF) 1 656 436 656 436 102 0.6646 4.2745 1.4466 4.2557 L1 ALONG 1 563 342 563 342 94 0.6075 3.6383 1.4048 3.6034 L2

FIELD SIMULATED 8 576 288 9 36 12 0.5000 3.0000 1.3457 3.0000 L1 BUNDS 16 - - - - - - - - - 24 - - - - - - - - - 32 - - - - - - - - - 40 - - - - - - - - - 48 - - - - - - - - - 60 - - - - - - - - - ORIGINAL SEN- IKONOS MSS 4 400 360 25 90 20 0.9000 4.5000 1.5021 4.5000 L1 4 320 320 20 80 18 1.0000 4.4444 1.5193 4.4721 L2 ASTER 15 - - - - - - - - - IRS 1D LISSIII 24 - - - - - - - - - LANDSAT ETM 30 - - - - - - - - - RESOURCE SAT 60 - - - - - - - - - FUSED IKONOS (MSS & 1 578 816 578 816 98 1.4118 8.3265 1.6725 8.4853 L1 1 476 650 476 650 88 1.3655 7.3864 1.6514 7.4482 L2 IRS 1D (LISS III & 6 4 SIN- IKONOS PAN(REF) 1 195 66 195 66 56 0.3385 1.0633 1.1816

GLE SIMULATED 8 - - - - - - - - - TREE 16 - - - - - - - - - 24 - - - - - - - - - 32 - - - - - - - - - 40 - - - - - - - - - 48 - - - - - - - - - 60 - - - - - - - - - ORIGINAL SEN- IKONOS MSS 4 - - - - - - - - - ASTER 15 - - - - - - - - - IRS 1D LISSIII 24 - - - - - - - - - LANDSAT ETM 30 - - - - - - - - - RESOURCE SAT 60 - - - - - - - - - FUSED IKONOS (MSS & 1 20 54 20 54 20 2.7000 2.7000 1.7376 3.0187 IRS 1D (LISS III & 6 - - - - - - - - -


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APPENDIX A-IX: SPECTRAL PROFILE COMPARISON BETWEEN IKONOS MSS & IKONOS FUSED (MSS & PAN)

IKONOS MSS and IKONOS fused (MSS & PAN) images of a small portion of the study area were analysed for the spectral profile of 10 random points. The results are as shown in Fig: A1 below. The coefficient of correlations between the three bands of IKONOS MSS and three layers of IKONOS fused image were also computed and are given in Table A1 and A2 be-low. All the three coefficients of correlations are found to be significant at 0.01 level of sig-nificance since they exceed the critical value of 0.765for 10 numbers of observations. (Stein, 1999).

IKONOS MSS IMAGE (4m) SPECTRAL PROFILE OF 10 RANDOM POINTS ON IKONOS MSS IMAGE

FUSED IMAGE IKONOS(MSS & PAN) (1m) SPECTRAL PROFILE OF SAME 10 RANDOM POINTS ON FUSED IMAGE

Fig. A 1: FCC of IKONOS MSS and Fused IKONOS along with spectral profiles


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Profile

1 Profile

2 Profile

3

Profile

4 Profile

5 Profile

6 Profile

7 Profile

8 Profile

9 Profile

10

TOF TOF SCRUB BUILT

UP FIELD FIELD BUILT

UP TOF SCRUB TOF

GREEN 263 345 303 497 459 377 561 353 342 271

RED 171 242 202 480 442 325 498 264 251 184 IKONOS MSS

INFRA RED 359 669 950 614 490 374 484 567 559 435

BAND I 149 233 81 427 339 277 274 171 209 133

BAND III 72 154 10 415 338 243 248 120 129 64 IKONOS FUSED

BAND III 241 476 677 394 338 243 120 321 362 321

Table A 1: Comparison of spectral profile between IKONOS MSS & fused IKONOS (MSS & PAN)

CORRELATION BETWEEN IKONOS MSS GREEN BAND VS. FUSED IKONOS BAND I 0.79555 CORRELATION BETWEEN IKONOS MSS RED BAND VS. FUSED IKONOS BAND II 0.90213 CORRELATION BETWEEN IKONOS MSS INFRA RED BAND VS. FUSED IKONOS BAND III 0.88931

Table A 2: Comparison of coefficient of correlation between bands

Modelling the Effects of Scale on Mapping Trees Outside ... · IIRS Member : Dr. S. P. S. Kushwaha...

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