complexity of a dynamic phenomenon such as urban sprawl could be understood with
the analyses of land use changes, sprawl pattern and computation of sprawl
indicator index. As a prelude to this analysis, GIS base layers such as, road
network and the administrative boundaries from the toposheets as shown in Table
1 were created. The highway passing between the cities was digitised 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 analyses were done using remote sensing data.
growth of urban sprawl over a period of three decades was determined by
computing the area of all the settlements from toposheets of 1972 and comparing
it with the area obtained from the classified satellite imagery for the built-up
area. The detailed methodology followed is depicted in the flow chart (Figure
4). The toposheets (Table 1) in digital format were scanned and then
geo-registered. The area under built-up (for 1972) was added to this attribute
database after digitisation of the toposheets for the built-up feature for the
is a process, which can affect even the smallest of villages; hence each and
every village was analysed. Attribute information like village name, taluk it
belongs to, population density, distance to the cities, were extracted from
census books of 1971 & 1981 and were added to the database. The area under
built-up (for 1972) was computed and appended to this attribute database.
multispectral IRS – LISS III satellite imagery procured from National
Remote Sensing Agency (NRSA), Hyderabad, India, was
used for the analysis using IDRISI 32 (http://www.clarklabs.org). The image
analyses included bands extraction, restoration, classification, and
extraction was done initially through a programme written in C++ and
subsequently IDRISI 32 was used for image analyses. Geo-registered LISS III data
obtained from NRSA (bands 2, 3 and 4 corresponding to G, R, MIR) were
geo-corrected using resampling techniques. This is done with the help of known
points on the Survey of India toposheets or / and ground control points (GCPs)
using GPS. The data acquired in bands -Green, Red and Near Infrared were used to
generate a False Colour Composite (FCC). To create the composite image from
three input bands, each of the three bands is stretched to 6 levels (6 * 6 * 6 =
216). The composite image consists of colour indices where each index = Green +
(Red * 6) + (Near Infrared * 36) assuming a range from 0-5 on each of the three
bands. For example, a pixel value of 3, 5, 1 respectively for the three bands,
Green, Red and Near Infrared would have an index of 3 + (5 * 6) + (1 * 36) = 69.
The 256 Colour Composite palette colours correspond to the mix of Green, Red and
Near Infrared in the stretched images. In the composite image, heterogeneous
patches were identified and the corresponding attribute data was collected using
GPS (Global Positioning System).
to the training data, signature files with attribute information were created.
For the image classification supervised classification by the Maximum Likelihood
Classifier (MLC) or Gaussian classifier was employed.
Area under built-up theme was
recognised and extracted from the imagery and the area for 1998-99 was computed.
Further, by overlaying village boundaries, villagewise built-up area was
of an area covered by impervious surfaces such as asphalt and concrete is a
straightforward measure of development (Barnes et al, 2001). It can be safely
considered that developed areas have greater proportions of impervious surfaces,
i.e. the built-up areas as compared to the lesser-developed areas. Further, the
population in the region also influences sprawl. The proportion of the total
population in a region to the total built-up of the region is a measure of
the built-up area as a potential and fairly accurate parameter of urban sprawl
has resulted in making considerable hypothesis on this phenomenon. Since the
sprawl is characterised by an increase in the built-up area along the urban and
rural fringe, this attribute gives considerable information for understanding
the behaviour of such sprawls. This is also influenced by parameters such as,
population density, population growth rate, etc.
recognition helps in finding meaningful patterns in data, which can be extracted
through classification. Digital image processing through spectral pattern
recognition wherein the spectral characteristics of all pixels in an image were
analysed. By spatially enhancing an image, pattern recognition can also be
performed by visual interpretation.
Figure 4: Flow Chart of Methodology of Analysis of Urban Growth
pattern involves detecting them, quantifying with appropriate scales and
summarising it statistically. The agents of pattern formation include the
physical abiotic component, demographic responses to this component, and
disturbance regimes overlaid on these. An interest in landscape dynamics
necessarily invokes models of some sort because landscapes are large and they
change over timescales that are difficult to embrace empirically. Spatial
heterogeneity matters to populations, communities, and ecosystems and these are
the essentials of conservation and ecosystem management. Various landscape
metrics were applied to analyse the built-up theme for the current study. The
landscape pattern metrics are used in studying forest patches (Trani and Giles,
1999; Civco, et al., 2002). The landscape metrics applied to analyse the
built-up theme for the current study is discussed next.
scores of metrics now available to describe landscape pattern, but there are
still only two major components--composition and structure, and only a few
aspects of each of these. Most of the indices are correlated among themselves,
because there are only a few primary measurements that can be made from patches
(patch type, area, edge, and neighbour type), and all metrics are then derived
from these primary measures.
computing indices of landscape pattern:
For comparative purposes, to
summarise the differences between or among study areas or landscapes
goal is fundamental to landscape ecology if not ecology in general.
Also, the task of attributing causal mechanism (process) to observed
pattern is more daunting than expected. Some of the common objectives of
landscape studies are:
To detect and quantify pattern
in the spatial heterogeneity of landscapes;
To develop and test a set of
indices that capture important aspects of landscape pattern;
To relate the indices with
To link small-scale ecological
information (i.e., field data) with pattern at the landscape level.
Shannon’s entropy (Yeh and Li, 2001) was computed to detect and quantify the
urban sprawl phenomenon. The Shannon’s entropy, Hn is given by,
= - S
= Proportion of the variable in the ith zone (i.e. proportion of
built up area in each village)
= Total number of zones (i.e. number of villages in the region)
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 (refer annexure for computation details).
NDC (Number of Different Classes) is the measurement of the density of patches
of all types or number of clusters within the n*n window. In other words, it is
a measurement of the number of polygons over a particular area. The greater the
patchiness, the more heterogeneous the landscape is (Murphy 1985).
In order to
compute the map density initially the class frequency of the required feature is
computed. The class frequency is the number of times a specified characteristic
value occurs within a kernel. The kernel can be of 3 x 3, 5 x 5, or 7 x 7. The
kernel is centred on each built-up pixel of the classified image in the manner
of a moving window. A new value for the centre pixel is assigned to the
corresponding position of the output image. For the value to be counted it must
fall within one of the positions marked by a 1 in the selected kernel: 3 x 3
The kernel size selected depends upon the scale of the information to be derived.