This is a literature review written for a class, Introduction to Remote Sensing, in 2010.


Applications of Remote Sensing to Urban Planning

            Urban planning determines how human activities should be altered so that they have a positive effect on the future; urban planners must identify goals, gather and analyze related information, and then make decisions accordingly (Prenzel 2004).  According to Masser (2001), a major demographic shift over the next twenty years will result in a increase of about 5 billion people to urban areas, especially to megacities that have a population of 5 million or greater, while approximately 80 percent of this change will take place in developing countries. New problems will accompany these shifts and will need to be met by new solutions (Devas and Rakodi 1993). The applicability of remote sensing and geographic information systems (GIS) technologies to the exploration of problems, the manipulation of solutions, and the implementation of urban planning techniques is key (Masser 2001; Prenzel 2004).

Although the development and perfection of a spectral library is a time-consuming task, the results are necessary and invaluable for the task of urban analysis (Herold et al. 2004; Heiden et al. 2007). Herold et al. (2004) recognize this need for this resource in their endeavor to actually develop a spectral library that includes more than 4500 spectra ranging from 350 to 2400 nm. In applying laboratory, ground, and aerial spectrometers to data collection within the visible and near infrared (VNIR) as well as shortwave infrared (SWIR) to the diverse surface types in Santa Barbara, CA, this study showed promising results despite the lack of spectral variance of low albedo impervious surfaces within urban areas (Herold et al. 2004).

The growth of spectrometry in the 1980s exposed the obvious advantage of enhanced detail offered by hyperspectrometry; significant recognition of the benefits of hyperspectrometry led to the acquisition  of more detailed data as well as the subsequent need for better ways to analyze the new data (Herold et al. 2004; Heiden et al. 2007). The small spectral variance between the spectral signatures of certain land covers such as bare soil and concrete roads, asphalt roads and shingle, gray tile roofs and shingle or tar, and road surface materials, can make it difficult to infer distinctions but the use of hyperspectral data has helped to better identify spectral features.

To minimize the negative effects of poor spectral variance, procedures are implemented to choose the pixels which have the best end-members (Heiden et al. 2004). End-member spectra define the appearance of a pixel (Winter 1999) and so refining end-member selection helps to distinguish between distinct surfaces. A field library that holds data on more than 21,670 spectra data is the HyMap data of Dresden and Potsdam, Germany (HYSL) and also helps  to separate spectral variations (Heiden et al. 2001; Heiden et al. 2007). Herold et al. (2004) affirm that better spectral recognition, in particular of impervious surfaces, aids urban planning such that more knowledge permits better resource management, hazard prediction, and pollution control.

The observable urban heat island (UHI) effect is important to urban planning due to the consequent effects on biodiversity, climate, weather, and human health (Imhoff et al. 2010). A technique applied by Imhoff et al. 2010 to the issue of  UHI  involves the integration of Landsat data representing impervious surface area (ISA) which is linked to urban areas and MODIS data representing land surface temperatures (LST) throughout the continental US. The results revealed a correlation between the LST and the ISA thereby demonstrating this method as an objective, effective measure of UHI (Imhoff et al. 2010).

Increasingly, land use change can be monitored with better accuracy which is especially important to resource management related to development. Empirical methodologies are used to analyze land use change via two methods: from-to change and binary change/no change (Prenzel 2004). Whereas from-to change methods attempt to estimate a quantified value,  binary change involves the use of a predetermined threshold calculated by the standard deviation of the mean which, when breached, signifies land use change (Im et al. 2007). A simple study in 1998 used historical black and white aerial photographs to analyze the growth and change of Villavicencio, Columbia (Massser 2001). Masser (2001) also cites the ability of sensors like the SPOT (Satellite Pour l’Observation de la Terre) panchromatic system and the 1 m resolution of the IKONOS satellite to monitor land use changes worldwide; however, the superiority of aerial photography on the smaller scale which is necessitated by  land parcels is acknowledged.  Later, Im et al. (2007) used high resolution panchromatic imagery from QuickBird, integrated several variables and used a binary method to mask change.

Urban sprawl is the extension of metropolitan areas by way of outer development (Ji et al. 2006; Jat, Gar, and Khare et al. 2008; Bhatta, Saraswati, and Bandyopadhyay et al. 2010) . Problems related to urban sprawl include traffic congestion and the subsequent air pollution, resource depletion, the destruction of open space, and other unpredictable and uncontrollable characteristics (Ji et al. 2006).  Ji (2001) cites sprawl as a phenomena typically observed as changes in human behavior as exemplified by an increase in population growth, a shift in commuting patterns, employment changes, commercial development  and increase of traffic congestion. Bhatta, Saraswati, and Bandyopadhyay et al. (2010) accede that there is not yet an exact definition assigned to urban sprawl although certain common characteristics, such as change in land use, are evident. Techniques for spatial analyzing urban sprawl are developed based on these general relationships.

A study by Ji et al. (2001) applies landscape indices to aerial photographs and multi-temporal IKONOS imagery in order to quantify change in land use and analyze the related trends at different scales including cities, counties, and census-defined areas. Landsat images were rectified and georeferenced by USGS and then purchased and other topographic maps were integrated into the analysis by image to image registration for most accurate classification of land cover (Ji et. al. 2001). Jat, Gar, and Khare et al. (2008) state that there is a need for cost-effective measures that can be applied in developing countries. A study in India used multi-spectral Landsat images, Indian Remote Sensing (IRS) LISS-III images, and topo-sheets for four separate years between 1977 an 2002. ArcGIS software by the Environmental Systems Research Institute, Inc. (Esri) was used to classify data and then compared to settlement maps obtained from the Ajmer Town Planning Department; landscape metrics were then applied and it was found that sprawl increased 160.8 percent as compared to the population growth (Jat, Gar, and Khare et al. 2008).


Devas, N. and Rakodi, C. 1993. Managing fast growing cities: new approaches to urban planning and management in the developing world. Harlow: Longman.


Heiden, U., Roessner, S., Segl, K., & Kaufmann, H. 2001. Potential of hyperspectral HyMap data for material oriented identification of urban surfaces. C. Juergens (Ed.) Remote Sensing of Urban Areas, Regensburger Geographische Schrifien, 35, Abstracts and Full Papers (on Supplement CD-ROM of the 2nd International Symposium of Remote Sensing in Urban Areas. Regensburg, Germany.

Heiden, U., Segl, K., Roessner, S., and Kaufmann, H. 2007. Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sensing of Environment 111: 537-552.

Herold, M., Roberts, D.A., Gardner, M.E., and Dennison, P.E. 2004. Spectrometry for urban area remote sensing-development and analysis of a spectral library from 350-2400 nm . Remote Sensing of Environment 91: 304-319.

Bhatta, B., Saraswati, S., and Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Applied Geography 30: 731-740.

Im, J., Rhee, J., Jensen, J.R., and Hodgson, M.E. 2007. An automated binary change detection model using a calibration approach. Remote Sensing of Environment 106: 89-105.

Imhoff, M., Zhang, P., Wolfe, R.E., and Bounoua, L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment 114: 504-513.

Jat, , M.K., Gar, P.K., and Khare, D. 2008. Monitoring and modeling of urban sprawl using remote sensing and gis techniques . International Journal of Applied Earth Observation and Geoinformation 10: 26-43.

Ji, W., Ma, J., Twibell, R.W., and Underhill, K. 2006. Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics. Computers, Environment and Urban Systems 30: 861-879.

Masser, I. 2001. Managing our urban future: the role of remote sensing and geographic information systems. Habitat International 25: 503-512.

Prenzel, B. 2004. Remote sensing-based quantification of land-cover and land-use change for planning. Progress in Planning 61: 281-299.

Winter, M. E. 1999. N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data. Proceedings of SPIO Imaging Spectrometry V: 266-275

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