H. S. Sudhira1, T. V. Ramachandra1,*, Karthik S. Raj1, and K. S. Jagadish2

Methodology to measure urban sprawl

The complexity of a dynamic phenomenon such as urban sprawl could be understood with land use change analyses, sprawl pattern and computation of sprawl indicator index. As a prelude to this analyses, various GIS base layers such as drainage network, roads and railway network and the administrative boundaries from the toposheets were created. The highway passing between the two cities was digitized separately and a buffer region of 4 km around this was created. This buffer region is created to demarcate the study region around the road. Following this, land cover and land use analyses was done using remote sensing data.

Urban sprawl over the period of three decades (1972-98) was determined by computing the area of all the settlements from the digitized toposheets of 1971-72 and comparing it with the area obtained from the classified satellite imagery for the built-up theme. The toposheets (Table 1) in digital format were first geo-registered. The area under built-up (for 1972) was added to this attribute database after digitization of the toposheets for the built-up feature for the study area.

Satellite image – IRS - LISS data scenes covering Path 99 – Row 65 and Path 100 – Row 64 was procured from National Remote Sensing Agency (NRSA), Hyderabad for the years 1998.

The standard processes for the analyses of LISS data such as band extraction, restoration, classification, and enhancement were carried out. Band extraction was done initially through a programme written in C++ and subsequently IDRISI 32 was used for image analyses. Supervised classification approach was adopted as it was found more accurate compared to unsupervised classification. The Maximum Likelihood Classifier (MLC) or Gaussian classifier was employed for the image classification. The original classification of land-use of 16 categories was aggregated to vegetation, built-up (residential & commercial), agricultural and open lands, and water bodies.

Area under built-up theme after classification was extracted from classified images, which gave the urban area of 1998. Further, by applying vector analyses, the built-up area under villages selected for the region between Bangalore – Mysore was computed.  

Shannon’s Entropy

The Shannon’s entropy approach (Yeh and Li, 2001) was computed to detect and quantify the urban sprawl phenomenon. The Shannon’s entropy, Hn is given by,

Pi = Proportion of the variable in the ith zone
n = Total number of zones

The value of entropy ranges from 0 to log n. Value of 0 indicates that the distribution is very compact, while values closer to log n reveal that the distribution is very dispersed. Higher values of entropy indicate the occurrence of sprawl.

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