Power politics and remote sensing

[Excerpted from my book ‘Research Methods in Remote Sensing’]

Remote sensing has a very long history dating back to the end of the 19th century when cameras were first made airborne using balloons and kites. The advent of aircraft further enhanced the opportunities to take photographs from the air. Then satellite mounted sensors had been developed to operate it from the space. Whatever the developments we see in the field of remote sensing were primarily for the military (for the power and politics). It was completely driven by power and politics. Remote sensing had been nourished within the core of power and politics. Initially it was not available to the civilian researchers. Most of the significant developments in remote sensing came just for World War-I and II. 

Although, many civilian remote sensing satellites have been launched since 1972 (starting from Landsat-I), still spy satellites, nano satellites, and high resolution sensors are being launched by the governments for power and politics. Very high resolution images are still restricted to the civilians in many countries including United States. For example, GeoEye-1 captures imagery at a spatial resolution of 0.41 m; however, it is downsampled to 0.5 m for the civilians because the US government does not allow higher than 0.5 m resolution to the civilians. Arial photography is still performed only by the governments in many countries and photographs are restricted; for example, India. In India (and many other countries), an individual researcher is not entitled to purchase even a low resolution satellite image. She/he needs to be associated with some institution and some sort of declaration is mandatory by the head of institution to obtain the imagery. That means, as an individual, one cannot perform the research with their own fund.

This type of restrictions is everywhere and was always there. On December 3, 1986, the United Nations had faced the difficulty to pass “Principles Relating to Remote Sensing of the Earth from Outer Space”. The United States’ position had been that collection and distribution of civilian remote sensing imagery should be unrestricted. The Soviet Union’s position was to ensure that acquisition and distribution of imagery should only be allowed with consent of the state that is overflown. It was the case for the outer space; if it comes within the air? Aerial remote sensing, till date, cannot be performed beyond the political boundary. Matthew (1983) is an essentially referred text in this context. Whether these political restrictions are good or bad is not the issue of this discussion. The issue is, rather, these political restrictions have created a knowledge gap in civilian remote sensing research and applications. 

Why are these restrictions imposed? India restricts its residents (individuals) to the access of very high resolution images; whereas Pakistani (or any other) military can purchase one such image covering India from a commercial vendor (from other country) without having any problem. Does it make any sense? Does it suggest rethinking on the data policy? Whatever the answer may be, politics does not want to make everything free, especially the remote sensing data of having high value and importance. It can be seen as an internal conflict. A good example of this internal conflict is the politics of remote sensing capabilities. No country wants to be left behind, but on the other hand, why should countries expend scarce resources acquiring the launch vehicles, satellites, and infrastructure needed to support a remote sensing program when much of the end product (images, etc.) can be purchased at a modest cost from commercial vendors. Remote sensing technology provides countries with the ability to evaluate others’ capabilities to a degree that is totally unprecedented in the history of relations between countries. The countries that employ this technology can assess others’ military and—to some degree—economic capabilities (refer Ammons 2010). It also has the effect of lessening the deception possible by a closed society in concealing its capabilities. This technology, in another way, could be said to have the potential to stabilize the international system.

Now, the question is whether this technology actually makes a country more secure or if it increases the perception, both internally and externally, that it is more secure. Perhaps it is a little of both. Countries will always seek more information about their adversaries and any technology that will increase the quantity and quality of that information is valuable because of its real or perceived contribution to the country’s security. Most countries that own remote sensing technology profess to employ this technology for peaceful purposes. It is difficult to argue that activities such as resource management and disaster management are anything other than positive pursuits. However, it would be a simplified thinking to assume that a country concerned about its security (all countries are concerned about security) would not employ every available means to protect itself (Ammons 2010). This is particularly true if these means are defensive rather than offensive and can be accomplished with some measure of privacy. However, we must assume all countries that own remote sensing technology gather imagery intelligence of other countries’ military capabilities. This information are collected mainly for three reasons—firstly, to monitor whether a country is violating any international agreement (defensive in nature); secondly, to prepare its own military capabilities to that standard (or higher) of other countries (defensive in nature); and thirdly, to use this information during a war or to attack a country (offensive in nature). The war among countries, perhaps, will not be stopped ever. Therefore, offensive use of remote sensing will also be continued for ever.

References
Ammons, A.A. (2010). Competition Among States: Case Studies in the Political Role of Remote Sensing Capabilities. PhD Dissertation, Catholic University of America, Washington. URL:http://aladinrc.wrlc.org//handle/1961/9175.
Matthew, M. (1983). The Technical, Legal and Political Implications of Remote Sensing Satellites. Theses and Dissertations (Comprehensive), Paper 54. http://scholars.wlu.ca/etd/54.

Remote Sensing Ontology

[Excerpted from my book ‘Research Methods in Remote Sensing’]


Ontology
Ontology is the largest branch of metaphysics in philosophy, and traditionally deals with questions of existence or reality. Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. Ontology is often used by philosophers as a synonym for metaphysics―a term which was used by early students of Aristotle to refer to what Aristotle himself called first philosophy. Sometimes ontology is used in a broader sense, to refer to the study of what might exist, where metaphysics is used for the study of which of the various alternative possibilities is true of reality (Smith 1999). Ontology thus provides the basis for exchange of information, and is a fundamental pre-requisite to description and explanation, in science and elsewhere.

In simple words, ontology seeks the classification of entities. Typically, philosophical ontologists produce theories that are very much like scientific theories, but of a far more general nature. Ontology is both a branch of philosophy and a fast-growing component of computer science concerned with the development of formal representations of the entities and relations existing in a variety of application domains. Ontology has been shown to have considerable potential on the level of both pure research and applications. It provides foundations for diverse technologies in areas such as information integration, natural language processing, data annotation, and the construction of intelligent computer systems. 

Recently, the term ontology has been used by information scientists to refer to canonical descriptions of knowledge domains, or associated classificatory theories. In this sense, ontology is “a neutral and computationally tractable description or theory of a given domain which can be accepted and reused by all information gatherers in that domain” (Smith 1999).

Often it is said that remote sensing can provide the ‘true’ representation of the earth’s surface. This statement is never true. Remote sensing provides an impression of the earth-surface features in pictorial format. Pictures are not the real truth; e.g., picture of a flower and the flower itself are not same. Therefore, ontology of remote sensing is primarily the inquiry about the existence or reality through the images. This inquiry is directed towards understanding and defining earth-surface features, spatial relations, processes, their categories, and so on. It would include not only the basic data models, concepts, and representations or classifications of earth-surface features, but also the ontological principles.

Objects and Fields
Remote sensing requires the classification (either object-based or field-based) of earth-surface features. The most widely accepted conceptual data model for spatial information considers that the geographic reality is represented as either fully definable entities (objects) or continuous spatial variations (fields). Objects are with discrete boundaries represented by geometric features; e.g., Roads, buildings, water bodies, etc. Fields are continuous phenomena such as elevation, temperature, and soil chemistry; they exist everywhere. These statements are very simple to the geospatial community. Unfortunately, remote sensing cannot handle them with that much of simplicity. 

If we consider individual bands of remote sensing images, they are two-dimensional functions, arising from the sampled response of a region of the earth to an external energy source (the sun or a radar beam) as measured by a passive or active sensor, respectively. In case of thermal and passive microwave remote sensing, the earth itself is the source of energy. Whatever the source of energy or the techniques involved in capturing that energy, remote sensing images are always continuous, not discrete. Further the properties of each sensor (i.e., the number of bands as well as the spectral, temporal, radiometric, and spatial resolutions) are the results of a compromise between the needs of various research communities and the availability of sensor technology. The continuous variation of the spectral response of the land-cover, which is the specific phenomena captured by the pixel values, often misses what a domain scientist considers as relevant. These measurements are merely components of the more complex information content of an image. Most image classification techniques do not rely explicitly on the conversion between digital counts (pixel values) and the actual energy captured by the sensor, but they use the digital counts to extract features. As a consequence, viewing images as fields of values of reflected energy is insufficient for their ontological characterization (Câmara et al. 2001). The limitations of the field perspective to the ontology of images have led some researchers to view a remotely sensed image as a container of an implicit set of objects, which are extracted by manual or semi-automated analysis procedures. But important to realize, the real-world boundaries exist independent of human cognitive acts. Further, measurement of reflected/emitted light and the identification of objects via manual or semi-automated analysis are independent, not controlled or operated by one another. For example, identification of objects does not consider the real atmospheric contributions that might have recorded by the sensor. Furthermore, the pixel is a generalized representation of reality; mostly they are mixed in nature―mixel (mixed pixel). A pixel cannot be divided further to represent an object smaller than the pixel size.

Classes
Class refers to a group of features identical or similar types that have taxonomic significance. Classification is an abstraction mechanism that maps individuals into a common class. Classes are often interrelated by a generalization processes, capturing different levels of detail about the same individuals. For example, deciduous and evergreen are two classes that can be generalized as forest. In this case, ontology describes a hierarchy of concepts created by a generalization process. Although the object perspective captures a fundamental component of the ontology of images and forms a basis for a large set of image classification techniques, it is still incomplete. In many cases, there is no corresponding object in the world, since we deal with purely physical phenomena; e.g., vegetation health—that may change in every pixel. Actually, object-based classification is an attempt for geospatial segmentation of earth-surface features based on the pixels and their digital values. This helps us to understand the physical earth-surface as a distinctly segmented space. This concept overlooks the existence of ‘mixel’ (mixed pixel).

Field-based sub-pixel classification, on the other hand, considers the geographic space as continuous phenomena. It tries to quantify the amount of a feature or material within a pixel. The entire pixel or a part of it may be occupied by a material. Therefore, it calculates the percentage or amount of that material within a pixel. This concept handles the problem of mixel well, but ignores the identification of other surface materials. Therefore, there is no spatial segmentation among the features, and thus, no objects in existence.

The preceding discussion shows that neither of the classification approaches is sufficient by itself to support the full process of knowledge representation for remotely sensed images. The underlying reason is that images have a dual nature: earth surface features within them can be interpreted as fields as well as objects; although, they are fields at the measurement level.

Relations
Remote sensing ontologies are different from many of other ontologies in that it embraces spatial relations that play a major role in the geospatial domain. Earth-surface features can be connected or contiguous, scattered or separated, closed or open, near or far, and so on. These relations not only exist in geographic form but also in concepts. For example, two large forests may be connected by a narrow forested passage (geographic form), and these two forests may be connected in the sense that both are of same species (conceptual form). Therefore, ‘part and whole’ relations and other spatial relations are needed to be described in remote sensing ontologies.

Functions
Functions are mappings or transformations applied on remotely sensed images, and can be of many types. Basic image processing functions, such as filtering, principal component transformations, resampling, or interpolation, are familiar to and used by almost all researchers working with remote sensing data. Other generic functions exist in the statistical analyses of spatial domain, e.g., spatial statistics or spatial metrics. Of more significance here are functions that map the state of earth’s surface can be applied as a process because earth-surface features are not static―they are continuously changing with the change of time. For example, spatial metrics can determine the built-up pattern; but how the built-up pattern changes with time is essentially a process that requires temporal consideration. Our concept of remote sensing ontology thus includes notions both of form (shape/size/pattern) and process. The process does not refer only to historical process but simulations for the future as well.

Câmara et al. (2001) argued that “a geographic landscape is an ever-changing scenario, and the process of data capture by remote sensing satellites implies that an image is a measurement that captures snapshots of change trajectories. Therefore, the focus of the ontological characterization of images should be on the search for changes instead of the search for content. The emphasis of such ontologies should not be placed on simple object matching and identification procedures, but on capturing dynamics over a finite landscape.”

Image Ontology
Câmara et al. (2001) proposed a multi-level ontology for images, based on the concept of action-driven ontologies for GIS (Câmara et al. 2000). The authors considered that remote sensing images are ontological instruments to capture landscape dynamics. The proposal takes into account that images have a particular, distinct description independent of the domain ontology a scientist would employ to extract information. The ontology domain for images has three interrelated components:
(1) Physical ontology – describes the physical process of the image creation, focusing the knowledge about the relation between the reflected energy by terrain surface and measures obtained by the sensor.
(2) Structural Ontology – contemplates geometric, functional and descriptive structures that can be extracted using techniques for feature extraction, segmentation, classification, and so on.
(3) Method Ontology – it is composed of a set of algorithms (that perform transformations from the physical level to the structural level) and data structures that represent reusable knowledge in the form of image processing techniques (filtering, smoothing, and others).

The algorithms that are part of the method ontology, perform transformations from the physical level to the structural level, a process than can be called structural identification. When applied to an image (or a set of images), this process results in a set of structures strongly related to the measurement device properties and its interaction with the physical landscape. These structures may be geometric (e.g., regions extracted by a segmentation procedure, i.e., per-pixel classification) or functional (e.g., normalized differential vegetation index or sub-pixel classification) (Câmara et al. 2001).

Image Mining and Image Ontology 
Image mining deals with extraction of implicit knowledge, image data relationship or other patterns not explicitly stored in images and uses ideas from computer vision, image processing, image retrieval, data mining, machine learning, databases and artificial intelligence. The fundamental challenge in image mining is to determine how low-level pixel representation contained in an image or an image sequence can be effectively and efficiently processed to identify high-level spatial objects and relationships. Typical image mining process involves pre-processing, transformations and feature extraction, mining (to discover significant patterns out of extracted features), evaluation and interpretation and obtaining the final knowledge. Various techniques from existing domains are also applied to image mining and include object recognition, learning, clustering and classification, just to name a few (Zhang et al. 2002).

Extracting information from images remains a complex and tedious process; sometimes inferior in respect of our needs. Our capacity to build sophisticated remote sensing sensors is not matched by our means of producing information from these data sources. Currently, most image processing techniques are designed to operate on a single image, and we have few algorithms and techniques for handling multi-temporal images. This situation has lead to a ‘knowledge gap’ in the process of deriving information from images and digital maps (MacDonald 2002). This ‘knowledge gap’ has arisen because there are currently few techniques for image mining and information extraction in large image datasets; thus we are failing to exploit our large remote sensing archives. Image ontology facilitates the deployment of the concept for various classes of models for the information extraction from the remote sensing imagery. Silva and Câmara (2004) presented the architecture of ontology based image mining. Durand et al. (2007) also explained the ontology-based object recognition for remote sensing image interpretation. 


REFERENCES
Câmara, G., Egenhofer, M., Fonseca, F., and Monteiro, A.M.V. (2001). What’s in an Image?. In Spatial Information Theory: Foundations of Geographic Information Science, International Conference, COSIT.
Câmara, G.M., Monteiro, A.M.V., Paiva, J.A.C, and Souza, R.C.M. (2000, October). Action-Driven Ontologies of the Geographical Space: Beyond the Field-Object Debate. In GIScience, Savanah, GA: AAG, pp. 52–54.
Durand, N., Derivaux, S., Forestier, G., Wemmert, C., & Gancarski, P. O. Boussaid D, et A. Puissant (2007). Ontology-based Object Recognition for Remote Sensing Image Interpretation. In IEEE International Conference on Tools with Artificial Intelligence, Patras, Greece, pp. 472–479.
MacDonald, J. (2002). The Earth Observation Business and the Forces that Impact it. Earth Observation Business Network.
Silva, M.P.S., and Câmara, G. (2004). Remote Sensing Image Mining Using Ontologies. Online. URL:http://www.dpi.inpe.br/~mpss/artigos/Im ... g2004.pdf.
Smith, B., (1999). Ontology: Philosophical and Computational. Unpublished manuscript. URL:http://wings.buffalo.edu/philosophy/fac ... ogies.htm.
Zhang, J., Hsu, W., and Lee, M. (2002). Image Mining: Trends and Developments. Dordrecht: Kluwer Academic. 

Whether Remote Sensing is Science, Art, or Technology

[Excerpted from my book ‘Research Methods in Remote Sensing’]

A frequently raised question in remote sensing community is that whether remote sensing is science, or technology, or art. Many of the literature preferred to define remote sensing as “science and art of obtaining and interpreting information about an object, area, or phenomenon through the analysis” (e.g., Jensen 2006; Bhatta 2011). However, remote sensing is a perfect blend of science, technology, and art. Lillesand et al. (2007) stated “Remote sensing is the science, technology, and art of obtaining information about an object, area, or phenomenon by analyzing data acquired by a device that is not in physical direct contact with the object, area or phenomenon under investigation”. Alavipanah et al. (2010) have shown a conceptual diagram of blending science, technology, and art as remote sensing.

Science is a system of acquiring knowledge based on the scientific methods, as well as the organized body of knowledge gained through such research. It is the understanding and continuous exploration of the natural world. Science is often driven by whim or curiosity without having any application goal. Science, as defined here, is sometimes termed pure science to differentiate it from applied science that is the application of scientific research to specific human needs. Science refers to a system of acquiring knowledge. This system uses observation and experimentation to describe and explain natural phenomena. Technology is applying the outcome of scientific principles to innovate and improve the man-made things in the world. The output of Technology is a new or better process of doing. In human society, it is a consequence of science. Art is the product or process of deliberately arranging items in a way that influences and affects one or more of the senses, emotions, and intellect. In simple words, art is the expression or application of human creative skill and imagination. If one can use different process to create a thing (output) using the same inputs it is called art. Generally, in science or technology, we use a standard process to create a thing (output) using same inputs. Science becomes art when one crosses the boundaries of set rules or explicit instructions and run on instinct or intuition. It is much more evident in areas of science that have not yet been fully discovered.

Remote sensing is a tool or technique similar to mathematics. Using sophisticated sensors to measure the amount of electromagnetic energy exiting an object or geographic area from a distance, and then extracting valuable information from the data using mathematically and statistically based algorithms is a scientific and technologic activity. It functions in harmony with other spatial data collection techniques or tools of the mapping sciences, including cartography and GIS. The synergism of combining scientific knowledge with real-world analyst experience allows the interpreter to develop heuristic rules of thumb to extract valuable information from the imagery. It is a fact that some image analysts are much superior to others because they: (1) understand the scientific principles better, (2) are more widely travelled and have seen many landscape objects and geographic areas first hand, and (3) can synthesize scientific principles and real-world knowledge to reach logical and correct conclusions (Jensen 2006).

Interpreting remotely sensed images is an open-ended task (Hoffman and Markman 2001). The perception of image in the part of the interpretation of remotely sensed images are the most outstanding and artistic parts of remote sensing. Human is created such that he is able to percept the realities of the entity. In other words, human is equipped with intellect by which he can percept his surrounding world.

Automatic image processing techniques (by using computers) remain inadequate for remote sensing data analysis (Friedl et al. 1988). The human must be in the ‘loop’; since the human, unlike the computer, can perceive and can form and reform concepts (Hoffman and Markman 2001). Important to realize, human interpreter can derive very little information using a point-by-point approach. Many of original interpretations depended not only on the imagery itself but also on the skill and experience of interpreter (Campbell 1996).

For the purpose of the perception of image in interpretation of remotely sensed images, the necessity of using artistic outcomes and in particular applied arts become more prominent. Having ability to make a visual inspection along with ‘visual knowledge’, beautiful selection and the efficiency of colours by considering the principles of compatibility and lack of compatibility of colour, increase of idea fertilization and ability to have a specific observance with the help of imagination and mental creativity and order are among the consequences which make possible utilization of this issue and having access to that will increase the ability to interpret (Alavipanah et al. 2010). Since the visual interpretation of remotely sensed images is mostly accompanied with individual judgment, a researcher should know how to employ the scientific and proper methods to reach the goal. In most of the cases, the conditions of the earth which appear in the image are complex. As a result, sometimes, knowledge and experience of an interpreter fails to make a link between the phenomena of the earth and the information content of an image.

Therefore, from the preceding discussion, it is evident that remote sensing is a blend of science, technology, and art. The important thing one should realize is that information extracted from remote sensing data may vary from analyst to analyst to some extents and achieving one hundred percent accuracy is never possible.

References
Alavipanah, S.K., Ghazanfari, K., and Khakbaz, B. (2010). Remote Sensing and Image Understanding as Reflected in Poetical Literature of Iran. Proceedings of Remote Sensing for Science, Education, and Natural and Cultural Heritage, 30th Symposium of European Association of Remote Sensing Laboratories, 31th May — 3rd June, UNESCO Headquarters, Paris, France. URL:http://www.earsel.org/symposia/2010-sym ... 0_1-02.pdf, Accessed 29 July 2011.
Bhatta, B. (2011). Remote Sensing and GIS (2nd Ed.). New Delhi: Oxford University Press.
Campbell, J.B. (1996). Introduction to remote sensing, New York: Guilford. 
Hoffman; R.R., and Markman, A.B. (Eds.) (2001). Interpreting Remote Sensing Imagery: Human Factors. Boca Raton: CRC press. 
Hoffman; R.R., and Markman, A.B. (Eds.) (2001). Interpreting Remote Sensing Imagery: Human Factors. Boca Raton: CRC press. 
Jensen, J.R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective (2nd Ed.). Upper Saddle River, NJ: Prentice Hall.

My new book (Research Methods in Remote Sensing)

I am going to publish my new book "Research Methods in Remote Sensing" from Springer, Germany.

This book introduces the overall concepts of research methods in Remote Sensing. It also addresses the entire research framework, ranging from ontology to documentation. As such, it covers the theory while providing a solid basis for engaging in concrete research activities. It is not intended as a textbook on remote sensing; rather, it offers guidance to those conducting research by examining philosophical and other issues that are generally not covered by textbooks. Various stages of research are discussed in detail, including illustrative discussions and helpful references. The topics considered in this book cover a part of the research methodologies explored in Master of Philosophy (M.Phil.) and Doctor of Philosophy (Ph.D.) programs. The book’s physical format has been kept to a compact, handy minimum in order to maximize its accessibility and readability for a broad range of researchers in the field of remote sensing.

In the early days of remote sensing, concerns of research were primarily ranged over contemporary physical and biological (biophysical) space and their arrangements as they could be documented. The methods that were used to explain, model, and predict different biophysical aspects became progressively more quantitative. Further, the new technologies and theoretical perspectives that emerged in the last few decades helped to redefine the objects of inquiry and extend the methods in use for collecting and analyzing remote sensing data and evaluating researches. 

Being a blend of science, art, and technology, and being multidisciplinary in nature, remote sensing generally associates complex non-linear research methods. Remote sensing has many different sensors and a wide variety of application areas. As a result, the research methods in this emerging field became more complicated and diverse. With the advent of new generation sensors and computer-based techniques for image analysis, remote sensing imageries are now being used more and more in several new folds of scientific researches. Because of its vastness, often, remote sensing becomes a distinct field of study rather than being utilized as a tool in a scientific field. As a result, new researchers in this field often get confused and overlook several issues important to be considered.

This book is an introduction to research methods in remote sensing. A research method is a way of collecting and analyzing the data. This sounds very ‘nuts and bolts’, but there is no way to properly engage in research (or in methods) without also tackling some of the fundamental theoretical questions. These questions are philosophical in nature, e.g., ontology, epistemology, paradigm, ethics, etc. This book is to furnish the overall concepts of research methods in Remote Sensing; starting from the theoretical ontology to the documentation of research. This book, therefore, covers the theory while providing a solid basis for engaging in concrete research activities. This book is not intended to become a textbook of remote sensing; rather, it has the intention to guide a researcher in conducting their research by documenting the issues that are generally not covered by a textbook.

The book is comprised with eight chapters. Chapter 1 is mainly aimed to document the definitions and overview. It begins with the definition and application areas of remote sensing of the earth’s surface, and proceeds towards the research types and research framework in the light of remote sensing. Chapter 2 is intended to discuss the entire research framework—ontology, epistemology, paradigm, methodology, methods, conclusions and recommendations. Chapter 3 is aimed to discuss the data and their collection/selection methods and related issues. First it discusses the factors influencing the selection of remote sensing data for different types of applications; and then it addresses the ground truth and other ancillary data. Chapter 4 emphasizes the general discussion of remote sensing data analysis. This chapter is based on concepts rather than tools and techniques; constraints and freedoms are also addressed in context. Chapter 5 deals with the research design and its parts— sampling design, observational design, analytical design, and operational design. Chapter 6 helps to understand the nature of power and politics and the critical role of ethics in scientific research, especially remote sensing research. Chapter 7 is aimed to discuss the methods and issues involved in documenting a research outcome. It is a guide on how to write a research paper, dissertation, and thesis.

I recommend this book for every researcher in the remote sensing community. 

MERRY X-MAS AND HAPPY NEW YEAR 2013

May this Christmas and New Year bring SMILES at your doorstep, JOY in your heart, and LOVE & TOGETHERNESS of friends and family in your home -- MERRY X-MAS AND HAPPY NEW YEAR 2013. 

My new book (Urban Growth Analysis and Remote Sensing)

My new book has been published from Springer, Germany. The title the book is "Urban Growth Analysis and Remote Sensing: A Case Study of Kolkata, India 1980–2010".

This book documents research conducted on the analysis of urban growth and sprawl by using remote sensing data and GIS techniques. The research was conducted between 1980-2010 in the city of Kolkata, India. The aim of the research was to use metrics that were less demanding in terms of data and computation than normal metrics. However, it has been found that most of them were inferior in capturing insights of urban sprawl. For this book, some of these metrics have therefore been modified and new ones are proposed. The research focuses on problems associated with the analysis of urban growth by using remote sensing data from a technological perspective. 

Sustainable Development, Urban Growth/Sprawl, and Infrastructure System

In the recent years ‘sustainable development’ is a commonly used terminology among various sections of the society. Sustainable development is defined as, “development that meets the needs of the present without compromising the ability of the future generations to meet their own needs” (WCED 1987). Sustainable development is a pattern of resource-use that aims to meet human needs while preserving the environment so that these needs can be met not only in the present, but also for future generations. In order to sustain a development, the supply and quality of major consumables and inputs to our daily lives and economic production--such as air, water, energy, food, raw materials, land, and the natural environment need to be taken care of.

Sustainable development does not focus solely on environmental issues. The United Nations 2005 World Summit Outcome Document refers to the “interdependent and mutually reinforcing pillars” of sustainable development as economic development, social development, and environmental protection (United Nations 2005). According to Hasna (2007), sustainability is a process which tells of a development of all aspects of human life affecting sustenance. It means resolving the conflict between the various competing goals, and involves the simultaneous pursuit of economic prosperity, environmental qualityand social equity; hence it is a continually evolving process. The ‘journey’ (the process of achieving sustainability) is of course vitally important, but only as a means of getting to the destination (the desired future state). However, the ‘destination’ of sustainability is not a fixed place in the normal sense that we understand destination. Instead, it is a set of wishful characteristics of a future system.


2. Urban Growth and Sprawl

Urban growth is a spatial and demographic process and refers to the increased importance of towns and cities as a concentration of population within a particular economy and society. It occurs when the population distribution changes from being largely hamlet and village based to being predominantly town and city dwelling (Clark 1982). The spatial configuration and the dynamics of urban growth are important topics of analysis in the contemporary urban studies. Several studies have addressed these issues with or without the consideration of demographic process and urbanisation which have dealt with diverse range of themes (e.g., Acioly and Davidson 1996; Wang et al. 2003; Páez and Scott 2004; Zhu et al. 2006; Belkina 2007; Puliafito 2007; Yanos 2007; Martinuzzi et al. 2007; Hedblom and Soderstrom 2008; Zhang and Atkinson 2008; Geymen and Baz 2008).

The first and foremost reason of urban growth is increase in urban population. The rapid growth of urban areas is the result of two population growth factors: (1) natural increase in population, and (2) migration to urban areas. Natural population growth results from excess of births over deaths. Migration is defined as the long-term relocation of an individual, household or group to a new location outside the community of origin. In the recent time, the movement of people from rural to urban areas within the country (internal migration) is most significant. According to the United Nations report (UNFPA 2007), the number and proportion of urban dwellers will continue to rise quickly. Urban global population will grow to 4.9 billion by 2030. In comparison, the world’s rural population is expected to decrease by some 28 million between 2005 and 2030. At the global level, all future population growth will thus be in towns and cities; most of which will be in developing countries. The urban population of Africa and Asia is expected to be doubled between 2000 and 2030.

This huge growth in urban population will force in uncontrolled urban growth resulting in sprawl. Urban sprawl is the less compact outgrowth of a core urban area exceeding the population growth rate and having a refusal character or impact on sustainability of environment and human. The rapid growth of cities strains their capacity to provide services such as energy, education, health care, transportation, sanitation and physical security. Because governments have less revenue to spend on the basic upkeep of cities and the provision of services, cities will become areas of massive sprawl and serious environmental problems threatening the regional sustainability.


3. Infrastructure System

Infrastructure can be defined as the basic physical and organizational structures needed for the operation of a society or enterprise, or the services and facilities necessary for an economy to function, or a set of assets needed to supply certain desired services. Infrastructure has several layers of implementation, such as, physical infrastructure (e.g. transport, energy, water and telecommunications infrastructure), social infrastructure (e.g. the education and health systems), environmental infrastructure (e.g. national parks system), institutional infrastructure (e.g. the land use planning system), neighbourhood infrastructure (e.g., parcel system, lanes/by-lanes), community-level infrastructure (e.g., water, sewer, power), amongst others. All of these interact with other in complex relations and often these systems are overlapping. In the recent days, studies are essentially needed that address the nature and magnitude of the positive and negative links between infrastructure systems and the other various components of sustainable development: social, economic, and environmental.


4. Urban Sprawl, Infrastructure System and Sustainability

Urban sprawl has widely been discussed due to its ill effects on environment (Kirtland et al. 1994). However, sprawl is also blamed as being inordinately costly to its occupants and to society (Harvey and Clark 1965). It is blamed due to its economic cost (Buiton 1994). Cities have experienced an increase in demand for public services and for the maintenance and improvement of urban infrastructures (Barnes et al. 2001) such as fire-service stations, police stations, schools, hospitals, roads, water mains, and sewers in the countryside. Sprawl requires more infrastructures, since it takes more roads, pipes, cables and wires to service these low-density areas compared to more compact developments with the same number of households. 

The Costs of Sprawl and other studies have shown that development of neighbourhood infrastructure becomes less costly on a per-unit basis as density rises (for a review of literature, see Priest et al. 1977; Frank 1989; Bhatta 2010). As long as developers are responsible for the full costs of neighbourhood infrastructure, and pass such costs on to homebuyers and other end-users of land, lower-density development patterns will meet the test of economic efficiency (at least with respect to infrastructure costs). Where inefficiency is more likely to arise is in the provision of community-level infrastructure. Inefficiency may also arise in the operation and maintenance of infrastructure, and in the provision of public services. Because people are more dispersed and no longer residing in centralized cities, the costs of community infrastructure and public services in suburban areas increases (Brueckner 2000; Heimlich and Anderson 2001; Pedersen et al. 1999; Wasserman 2000). These costs tend to be financed with local taxes or user fees that are generally independent of location, causing remote development to be subsidized. From the standpoint of community-level infrastructure, costs do not vary so much with residential density but with the degree of clustering and/or proximity to existing development (Stone 1973; RERC 1974; Downing and Gustely 1977; Peiser 1984).

The preceding discussion directs our attention towards the challenges for sustainable development of infrastructure systems. This should ground the basis of immediate initiatives from all of the layers of the society—politicians, planners & administrators, enforcements, NGOs, environmentalists, stakeholders, and the general citizens as well. Achieving the goals of sustainability is indeed real challenging.


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