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.


References

Acioly, C.C. and Davidson, F. (1996). Density in Urban Development. Building Issues, 8(3): 3–25.
Banerjee, A. (2005). Population growth, environment and development: some issues in sustainability of the mega city of Kolkata (Calcutta), West Bengal. Proceedings of the National Seminar on Population Environment and Nexus, 21 October, Deonar, Mumbai: Population Environment Centre, IIPS. Available at:http://www.iipsenvis.nic.in/paper/fp_anuradhab.pdf Accessed 13.02.08.
Barnes, K.B., Morgan III, J.M., Roberge M.C. and Lowe, S. (2001). Sprawl development: Its patterns, consequences, and measurement. Towson University: A white paper. Available at:http://chesapeake.towson.edu/landscape/ ... _paper.pdf
Belkina, T.D. (2007). Diagnosing Urban Development by an Indicator System. Studies on Russian Economic Development, 18(2): 162–170.
Bhatta, B. (2010). Analysis of Urban Growth and Sprawl from Remote Sensing Data. Springer-Verlag, Heidelberg, pp. 170.
Brueckner, J.K. (2000). Urban sprawl: Diagnosis and remedies. International Regional Science Review, 23(2): 160–171.
Buiton, P.J. (1994). A Vision for Equitable Land Use Allocation, Land Use Policy, 12(1): 63–68.
Clark, D. (1982). Urban Geography: An Introductory Guide. Taylor & Francis.
Downing, P.B. and Gustely R.D. (1977). The Public Service Costs of Alternative Development Patterns: A Review of the Evidence. In P.B. Downing (ed.), Local Service Pricing Policies and Their Effect on Urban Spatial Structure, Vancouver, B.C: University of British Columbia Press.
Frank, J.E. (1989). The Costs of Alternative Development Patterns: A Review of the Literature. Washington D.C.: Urban Land Institute.
Geymen, A. and Baz, I. (2008). Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environmental Monitoring Assessment, 136: 449–459.
Harvey, R.O., and Clark, W.A.V. (1965). The nature and economics of urban sprawl. Land Economics, 41(1): 1–9.
Hasna, A.M. (2007). Dimensions of sustainability. Journal of Engineering for Sustainable Development: Energy, Environment, and Health, 2(1): 47–57.
Hedblom, M. and Soderstrom, B. (2008). Woodlands across Swedish urban gradients: Status, structure and management implications. Landscape and Urban Planning, 84: 62–73.
Heimlich, R.E. and Anderson, W.D. (2001, June). Development at the urban fringe and beyond: Impacts on agriculture and rural land. ERS Agricultural Economic Report No. 803, pp. 88.
Kirtland, D., Gaydos, L., Clarke, K., DeCola, L., Acevedo, W. and Bell, C. (1994). An analysis of human-induced land transformations in the San Francisco Bay/Sacramento area. World Resources Review, 6(2): 206–217.
Martinuzzi, S., Gould, W.A. and Gonzalez, O.M.R. (2007). Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data. Landscape and Urban Planning, 79: 288–297.
Páez, A. and Scott, D.M. (2004). Spatial statistics for urban analysis: A review of techniques with examples. GeoJournal, 61: 53–67.
Pedersen, D., Smith, V. E. and Adler, J. (1999, July 19). Sprawling, sprawling ……. Newsweek, 23– 27.
Peiser, R.B. (1984). Does It Pay to Plan Suburban Growth? Journal of the American Planning Association, 50(4): 419–433.
Priest, D. et al. (1977). Large-Scale Development: Benefits, Constraints, and State and Local Policy Incentives. Washington D.C.: Urban Land Institute, pp. 37–45.
Puliafito, J.L. (2007). A transport model for the evolution of urban systems. Applied Mathematical Modelling, 31: 2391–2411.
RERC (Real Estate Research Corporation) (1974). The Costs of Sprawl, Detailed Cost Analysis. Washington, D.C.: U.S. Government Printing Office.
Stone, P.A. (1973). The Structure, Size, and Costs of Urban Settlements. London: Cambridge University Press.
UNFPA (United Nations Population Fund) (2007). Peering into the dawn of an urban millennium, State of world population 2007: Unleashing the potential of urban growth. Available at:www.unfpa.org/swp/2007/english/introduction.html
United Nations (2005). World Summit Outcome Document, World Health Organization.
Wang, W., Zhu, L., Wang, R. and Shi, Y. (2003). Analysis on the spatial distribution variation characteristic of urban heat environmental quality and its mechanism – A case study of Hangzhou City. Chinese Geographical Science, 13(1): 39–47.
Wasserman, M. (2000). Confronting urban sprawl. Regional Review of the Federal Reserve Bank of Boston, 9–16.
WCED (World Commission on Environment and Development) (1987). Our Common Future. Oxford: Oxford University Press.
Yanos, P.T. (2007). Beyond “Landscapes of Despair”: The need for new research on the urban environment, sprawl, and the community integration of persons with severe mental illness. Health & Place, 13: 672–676.
Zhang, P. and Atkinson, P.M. (2008). Modelling the effect of urbanization on the transmission of an infectious disease. Mathematical Biosciences, 211: 166–185.
Zhu, M., Xu, J., Jiang, N., Li, J. and Fan, Y. (2006). Impacts of road corridors on urban landscape pattern: a gradient analysis with changing grain size in Shanghai, China. Landscape Ecology, 21: 723–734.

Global Navigation Satellite System (GNSS) and its Definitions

Often my students ask about a clear definition of Global Navigation Satellite System (GNSS); since in many instances the definitions of GNSS are application specific and not lucid.

As it is known to us that GNSS is a satellite based navigation and positioning system. This system provides autonomous spatial positioning with global coverage. A GNSS allows small electronic receiver to determine its location using signals transmitted from navigation satellites. For anyone with a GNSS receiver, the system can provide location (and time) information in all weather conditions, day and night, anywhere in the world.

GNSS is made up of three segments: (1) satellites orbiting the earth; (2) control and monitoring stations on the earth; and (3) the GNSS receivers owned by users. GNSS satellites broadcast signals from space that are picked up and identified by GNSS receivers. Each GNSS receiver then provides three-dimensional location (latitude, longitude, and altitude), precise time information, and other information for calibration purposes.

Individuals may purchase GNSS receivers that are readily available through commercial retailers. Equipped with these receivers, users can accurately locate where they are and can easily navigate to where they want to go, whether walking, driving, flying, or sailing. GNSS has become a mainstay of transportation systems worldwide, providing navigation for aviation, ground, and maritime operations. Disaster relief and emergency services depend upon GNSS for location and timing capabilities in their life-saving missions. Activities such as banking, mobile phone operations, and even the control of power grids, are facilitated everyday by the accurate timing provided by GNSS. Engineers, surveyors, geologists, geographers, and countless others can perform their work more efficiently, safely, economically, and accurately using the GNSS technology.

There are currently two GNSSs in operation: the United States’ NAVigation Satellite Timing And Ranging Global Positioning System (NAVSTAR GPS, commonly known as GPS) and the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema (GLONASS). A third system, Galileo, is currently being developed in Europe; and a fourth, Compass Navigation Satellite System (or Beidou II; commonly referred as Compass) has been initiated by China. Other than these global systems there are some regional or local systems as well.

With the advent of GPS and GLONASS, and soon with the addition of Galileo and Compass, the application by civil users of global positioning, navigation, and timing services has mushroomed around the world and has popularized the concept of GNSS. Unfortunately we have yet to come up with a commonly accepted and actionable definition of GNSS (Swider 2005). Swider (2005) has defined GNSS as:
GNSS collectively refers to the worldwide civil positioning, navigation, and timing determination capabilities available from one or more satellite constellations. 

A definition of GNSS given by International Civil Aviation Organization is (ICAO 2005):
GNSS is a world-wide position and time determination system that includes one or more satellite constellations, aircraft receivers, system integrity monitoring augmented as necessary to support the required navigation performance for the intended operation. 

Another simple definition is:
GNSS is a satellite-based system that is used to pinpoint the geographic location of a user’s receiver anywhere in the world. 

The above definition is short, simple, and memorable; however, it is technologically not sound enough. A better definition of GNSS is (Bhatta 2008):
GNSS is a network of satellites that continuously transmits coded information, which makes it possible to precisely identify locations on the earth by measuring distances from the satellites. 

Whatever the earlier definitions we may find in the literature, a good definition of GNSS can be given as (Bhatta 2010):
GNSS is a system consisting network of navigation satellites monitored and controlled by ground stations on the earth, which continuously transmit radio signals that are captured by the receivers to process, and thus to make it possible to precisely geolocation of the receiver by measuring distances from the satellites and to provide precise time information any were in the world at any time.
'Geolocation' refers to identifying the real-world geographic location of a GNSS receiver.


References
Swider, R.J. 2005, Can GNSS Become a Reality?, GPS World, 16(12): 20–20.
ICAO 2005, Draft Galileo SARPS – Part A, Working Paper, International Civil Aviation Organization NSP/WG1: WP35, 12 pp.
Bhatta, B. 2008, Remote Sensing and GIS, Oxford University Press, New York, 872 pp.
Bhatta, B. 2010, Global Navigation Satellite Systems : Insights into GPS, GLONASS, Galileo, Compass, and Others, BS Publications, Hyderabad, 438 pp.

Geostatistics

Geostatistics is an application of the theory of random functions for estimating natural phenomena. ‘Geostatistics offers a way of describing the spatial continuity of natural phenomena and provides adaptations of classical regression techniques to take advantage of this continuity’ (Isaaks and Srivastava 1989). The data that we have are never complete; we have either the wrong kind or insufficient or partial coverage. Naturally, we seek ways to predict the values between, or to extrapolate beyond, the limits of our data.

The basic concept of geostatistics is that of scales of spatial variation. Data which are spatially independent show the same variability regardless of the location of data points. However, spatial data in most cases are not spatially independent. Data values which are close spatially show less variability than data values which are farther away from each other. A fundamental concept in geography is that nearby entities often share more similarities than entities which are far apart (Miller 2004). This idea is often labelled ‘Tobler’s first law of geography’ and may be summarised as ‘everything is related to everything else, but near things are more related than distant things’ (Tobler 1970). The exact nature of this pattern varies from data set to data set; each set of data has its own unique function of variability and distance between data points. This variability is generally computed as a function called semivariance.

In one respect geostatistics might be viewed as simply a methodology for interpolating data on an irregular pattern but this is too simplistic. A number of interpolation methods/algorithms were already well known when geostatistics began to be known; for example, inverse distance weighting (IDW) and trend surface analysis as well as the much simpler nearest neighbor algorithm. Interpolation techniques use sample points to produce surfaces of the phenomena of interest. The interpolation techniques are divided into two main types: deterministic and geostatistical methods.

Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (e.g., IDW) or the degree of smoothing (e.g., radial basis functions). These techniques do not use a model of random spatial processes. A deterministic interpolation can either force the resulting surface to pass through the data values or not. An interpolation technique that predicts a value that is identical to the measured value at a sampled location is known as an exact interpolator (e.g., IDW and radial basis functions). An inexact interpolator (e.g., global and local polynomials) predicts a value that is different from the measured value. The latter can be used to avoid sharp peaks or troughs in the output surface.

Geostatistics assume that at least some of the spatial variation of natural phenomena can be modeled by random processes with spatial autocorrelation. Geostatistical techniques produce not only prediction surfaces but also error or uncertainty surfaces, giving us an indication of how good the predictions are. Geostatistical interpolators exhibit probabilistic behaviour, i.e., it can be considered that for one known condition there are many possible outcomes, some of which will be more likely than others.

Many methods are associated with geostatistics, but they generally fall in the kriging family, e.g., ordinary, simple, universal, probability, indicator, and disjunctive kriging, along with their counterparts in cokriging. Kriging is a method of estimation based on the trend and variability from the trend. Variability, in this context, refers to random errors about the trend or mean. In this context, ‘error’ does not imply a mistake but a fluctuation (error) about the trend is unknown and is not systematic; the fluctuation could be positive or negative. Kriging may be considered exact (or smoothed) or inexact. Kriging incorporates the principles of probability and prediction, and like the IDW, is a weighted average technique except that a surface produced by kriging may exceed the value range of the sample points while still not actually passing through them. Various statistical models can be chosen to produce map outputs (or surfaces) from the kriging process; such as, interpolated surface (the prediction), the standard prediction errors (variance), probability (that the prediction exceeds a threshold) and quantile (for any given probability) (Liu and Mason 2009). One may refer Isaaks and Srivastava (1989) for an introductory text on Geostatistics.


References
Isaaks, E.H. and R.M. Srivastava 1989, An Introduction to Applied Geostatistics, Oxford University Press, New York, 561 pp.
Miller, H.J. 2004, ‘Tobler’s first law and spatial analysis’, Annals of the Association of American Geographers 94: 284–289.
Tobler, W. 1970, ‘A computer movie simulating urban growth in the Detroit region’, Economic Geography 46: 234–240.
Liu, J.G. and P.J. Mason 2009, Essential Image Processing and GIS for Remote Sensing, Wiley-Blackwell, New York, 450 pp.

Classification of Remote Sensing

[Excerpted from my book Remote Sensing and GIS]

Remote sensing is a complex technique and may vary based on the application and technological development. Considering technological development, for example, in the earlier days remote sensing was performed from balloons, but nowadays satellites are being used. Earlier, photographic cameras remained the only option, but nowadays digital cameras/sensors are dominating. Considering applications, for example, mapping purposes can be fulfilled by optical images, but information about temperature needs thermal image.

Remote sensing may be classified from many perspectives, for example, based on platform, source of energy, number of bands, and so on. The following sections explain remote sensing from the perspective of different classification schemes.


Classification Based on Platform

In order for a remote sensor to collect and record energy reflected or emitted from a target or surface, it must reside on a stable platform away from the target or surface being observed. Platforms for remote sensors may be situated on the ground, on an aircraft, or balloon (or some other platform within the earth’s atmosphere), or on a spacecraft or satellite outside the earth’s atmosphere.

Ground-based sensors are often used to record detailed information about the surface that is compared with information collected from aircraft or satellite sensors. In some cases, this can be used to better characterize the target that is being imaged by these other sensors, making it possible to better understand the information in the imagery. Sensors may be placed on a ladder, scaffolding, tall building, cherry-picker, crane, etc.

However, remotely sensed data are mainly collected either from the platforms within the earth’s atmosphere (air), or platforms in the space (outside of earth’s atmosphere). Platforms within the air are called aerial or airborne platforms, and platforms in the space are called space-borne or space platforms. Accordingly remote sensing is also referred as aerial or airborne or sub-orbital remote sensing, and space or space-borne or orbital remote sensing.

Different aerial platforms are balloons, kites, pigeons, aircrafts, etc. Balloons, kites, and pigeons are the early platforms of remote sensing and currently not used. Aircrafts are the main aerial platforms. An aircraft is a vehicle which is able to fly by being supported by the air. In remote sensing, aircrafts are primarily stable wing aeroplanes, although helicopters are also occasionally used.

In space, remote sensing is conducted mainly from satellites, and it is called satellite remote sensing. It is also known as satellite-borne remote sensing. Satellites are objects which revolve around another object--in this case, the earth. For instance, the moon is a natural satellite, whereas man-made satellites include platforms that are launched for remote sensing, communication, and telemetry (location and navigation) purposes.

Remote sensing, in space, may also be performed from space stations (such as, International Space Station); however it is a rare case. Other rarely used sensor platform is space transport system, commonly known as space shuttle. Data acquired from space station and space shuttle are used for scientific experimentations; they are not available commercially or widely.

Cost is often a significant factor in choosing among the various platform options. Moreover, each of the platforms has its own advantages and disadvantages. Satellite remote sensing can significantly enhance the information available from traditional data sources because it can provide synoptic view of large portions of the earth. Satellite imagery can also expand the spatial dimensions of limited and sometimes costly field or point-source sampling efforts. Some satellite sensors cover areas that may be physically or politically inaccessible, or that are too vast to survey with traditional methods. Satellite remote sensing can also provide consistent repeat coverage at relatively frequent intervals, making detection and monitoring of change feasible. Satellite-derived data and information are also useful for applications that require fine spatial resolution such as surveys of urban and suburban land-use/land-cover, for agricultural purposes, and natural resources; surveys for coastal management; and measurements of water quality in limnological (concerning lake and other fresh waters) and oceanographic applications.

The disadvantages of satellite remote sensing include the inability of many sensors to obtain data and information through cloud cover (although microwave sensors can image the earth through clouds) and the relatively low spatial resolution achievable with many satellite-borne earth remote sensing instruments.

In addition, the need to correct for atmospheric absorption and scattering, and for the absorption of radiation through water on the ground can make it difficult to obtain desired data and information on particular variables. Satellite remote sensing creates large quantities of data that typically require extensive processing as well as storage and analysis.

Finally, data from satellite remote sensing are often costly if purchased from private vendors or value-adding resellers, and this initial cost, together with intellectual property restrictions, can limit the dissemination of products from such sources.

In many instances, there may be an advantage of combining the large-scale, synoptic data that are accessible from space with higher-resolution surveys of key locations that can be made from other platforms, such as aircraft. Aerial photography, for instance, has a competitive advantage in applications that require fine spatial resolution of small areas or that involve areas subject to frequent cloud cover, especially in cases where repeat coverage is not needed (mobilizing the aircraft repeatedly will be a costly process). Another advantage of aerial photography is that surveys can be scheduled for specific purposes, time, and locations. But aircraft cannot be mobilized in politically inaccessible areas.


Classification Based on Energy Source

As we know the sun is the natural source of energy or radiation. The sun provides a very suitable source of energy for remote sensing. This energy is either reflected, as it is for visible and reflective IR wavelengths, or absorbed and then reemitted, as it is for thermal infrared wavelengths. Remote sensing systems which measure energy that is naturally available are called passive remote sensing. Passive sensors can only be used to detect naturally occurring energy. Passive remote sensing can only take place during the time when the sun is illuminating the earth, because the sun is the natural source of energy. There is no reflected energy available from the sun at night. Energy which is naturally emitted (such as thermal infrared) can be detected day or night, as long as the amount of energy is large enough to be recorded.

Active sensors, on the other hand, provide their own energy source for illumination. The sensor emits radiation, which is directed towards the target to be investigated. The radiation reflected from that target is then detected and measured by the sensor. Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of the day or season. Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. However, active systems require the generation of a fairly large amount of energy to adequately illuminate targets. A laser fluorosensor and synthetic aperture radar (SAR) are some examples of active sensors.


Classification Based on Imaging Media 

Reflected or emitted energy from terrain may be imaged, either photographically or electronically (digitally). The photographic imaging process uses chemical reactions on the surface of light-sensitive film to detect and record energy variations. In the case of digital imaging, sensors use electronic transducers such as charge coupled devices (CCDs).

Since its inception, photographic imaging system for remote sensing has been widely used from aerial platforms. Other platforms like space shuttle and early experimental spacecrafts had also been used for photographic imaging on experimental basis. However, in applied remote sensing, these platforms do not have any significance and aeroplane is the only platform used for photographic imaging. Digital imaging is rather a new technique that is being used from satellites as well as aeroplanes. Important to realize that satellite remote sensing is based on digital imaging; because a satellite remains on its orbit throughout its life and there is no chance of getting the film if recorded photographically. Digitally recorded data are transmitted from the satellite to the earth via digital communication link.

Photographic remote sensing is possible only within the range of photographic region (i.e. 0.3–0.9 micrometer) of electromagnetic spectrum. Therefore, digital imaging is the only choice if the sensor uses wavelengths that are outside of this region. Digital technique is capable of much higher spectral resolution than photographic systems. Multi-band or multispectral photographic systems use separate lens systems to acquire each spectral band. This may lead to problems in ensuring that the different bands are comparable both spatially and radiometrically and with registration of the multiple images. Digital systems acquire all spectral bands simultaneously through the same optical system to alleviate these problems. Photographic systems record the energy detected by means of a photochemical process, which is difficult to measure, and to maintain consistency. Because digital image data are recorded electronically, it is easier to determine the specific amount of energy measured, and they can record over a greater range of values. Photographic systems require a continuous supply of film and processing on the ground after the photos have been taken. The digital recording systems facilitate transmission of data to receiving stations on the ground and immediate processing of data in a computer environment.


Classification Based on the Regions of Electromagnetic Spectrum

As discussed in Chapter 1, remote sensing may be performed in different regions of electromagnetic spectrum. Remote sensing can also be classified based on the regions of electromagnetic spectrum in use. Optical remote sensing is performed within the optical region (0.3–3.0 micrometer), photographic remote sensing is performed within the photographic region (0.3–0.9 micrometer), thermal remote sensing uses the thermal region (3.0 micrometer – 1 mm), and microwave remote sensing is conducted within the microwave region (1 mm – 1 m). Optical and photographic remote sensing records reflected energy from the earth’s surface. These sensors generally use the sun as a source of energy (an exception is LiDAR). Thermal and passive microwave remote sensing uses emitted energy from the earth’s surface. However, active microwave remote sensing throws artificially generated energy to the earth’s surface and then the backscattered energy is recorded by the sensor. Backscatter is the term given to reflections in the opposite direction to the incident active microwave rays.

Several other techniques, for example LiDAR and SONAR, are also available. However, these are not widely used and difficult to understand at this point of discussion. The use of different wavelengths (and thereby techniques) is mainly because of different applications.


Classification Based on Number of Bands

Images for a geographic area may be collected in single band or more than one bands. Remote sensing can also be classified based on the number of bands to which a sensor is sensitive.

Panchromatic remote sensing is defined as the collection of reflected, emitted, or backscattered energy from an object or area of interest in a single band of the electromagnetic spectrum. In this case, generally, images are collected within the visible region (i.e., 0.4–0.7 micrometer); however, in some of the instances a wider region is also used (e.g., 0.3–0.9 micrometer). Therefore, if a sensor captures images in single band in microwave region it can not be said as panchromatic image. It must use the visible region or a wider region that essentially contains visible region.

Multi-spectral remote sensing is defined as the collection of reflected, emitted, or backscattered energy from an object or area of interest in multiple bands of the electromagnetic spectrum. In order to increase the spectral discrimination, remote sensing systems designed to monitor the earth’s surface, employ a multi-spectral design. Multi-spectral sensors can detect energy in a less number of broad wavelength bands. Multi-spectral remote sensing may be performed in the optical, thermal, as well as microwave regions. However, sensors and imaging techniques are different for different regions.

Hyper-spectral remote sensing is a major advancement in remote sensing, which is currently coming into its own as a powerful and versatile means for continuous sampling of narrow intervals of the spectrum. It is, in many respects, just an extension of the techniques employed in multi-spectral remote sensing. Multi-spectral remote sensors produce images with a few relatively broad wavelength bands. Hyper-spectral remote sensors, on the other hand, collect image data simultaneously in dozens or hundreds of narrow (as little as 0.01 micrometer in width for each), adjacent spectral bands. Hyper-spectral remote sensing is generally performed within the optical and thermal region of electromagnetic spectrum.

Panchromatic remote sensing can be conducted either photographically or digitally. Similarly, multi-spectral remote sensing can also be conducted photographically or digitally if it is performed within the photographic region. However, if it includes wavelengths outside of the photographic region then it must use digital imaging. Multi-spectral remote sensing, nowadays, is conducted digitally. For the hyper-spectral remote sensing, digital technique is the only method; because, photographic film can not be used to capture such a narrow spectral band.