MATERIALS

The time series spatial data acquired from Landsat Series Thematic mapper (28.5m) sensors for the period 1973 to 2002 were downloaded from public domain (http://glcf.umiacs.umd.edu/data). IRS LISS III (24 m) data coinciding with the field investigation dates were procured from National Remote Sensing Centre (www.nrsc.gov.in), Hyderabad.

Table 1: Materials used in analysis

Survey of India (SOI) topo-sheets of 1:50000 and 1:250000 scales were used to generate base layers of city boundary, etc. Table1 lists the data used in the current analysis. Ground control points to register and geo-correct remote sensing data were collected using handheld pre-calibrated GPS (Global Positioning System), Survey of India Toposheet and Google earth (http://earth.google.com, http://bhuvan.nrsc.gov.in). Table1 lists the data used in the current analysis

3.1 Method

A stepwise normative gradient approach was adopted to understand the dynamics city. Which includes (i) first step to derive land use and land cover (ii) a zonal-gradient approach of 4 zones and 1km radius gradients to understand the pattern of growth during the past 4 decades.(iii)understanding the change in the land use dynamics using Landscape metrics analysis. Various stages in the data analysis are:

Figure 2: Procedure to understand the changes in spatial pattern and its dynamics

3.1.1 Preprocessing

The remote sensing data of landsat were downloaded from GLCF (Global Land Cover Facility) and IRS LISS III data were obtained from NRSC, Hyderabad. The data obtained were geo-referenced, rectified and cropped pertaining to the study area. The Landsat satellites have a spatial resolution of 28.5 m x 28.5 m (nominal resolution) were resampled to uniform 24 m for intra temporal comparisons.

3.1.2 Vegetation Cover Analysis

Vegetation cover analysis was performed using the index Normalized Difference Vegetation index (NDVI) was computed for all the years to understand the change in the temporal dynamics of the vegetation cover in the study region.  NDVI value ranges from values -1 to +1, where -0.1 and below indicate soil or barren areas of rock, sand, or urban built-up. NDVI of zero indicates the water cover. Moderate values represent low density vegetation (0.1 to 0.3) and higher values indicate thick canopy vegetation (0.6 to 0.8).

3.1.3 Land use analysis

Further to investigate the different changes in the landscape land use analysis was performed. Categories included are as listed in Table 2, were classified with the training data (field data) using Gaussian maximum likelihood supervised classifier.  This analysis includes generation of False Colour Composite (bands – green, red and NIR), which basically helps in visualizing the different heterogeneous patches. Further using the training data Polygons were digitized corresponding to the heterogeneous patches covering about 40% of the study region and uniformly distributed over the study region. These training polygons were loaded in pre-calibrated GPS (Global position System). Attribute data (land use types) were collected from the field with the help of GPS corresponding to these polygons. In addition to this, polygons were digitized from Google earth (www.googleearth.com) and Bhuvan (bhuvan.nrsc.gov.in). These polygons were overlaid on FCC to supplement the training data for classifying landsat data.

Gaussian maximum likelihood classifier (GMLC) is applied to classify the data using the training data. GMLC uses various classification decisions using probability and cost functions (Duda et al., 2000) and is proved superior compared to other techniques. Mean and covariance matrix are computed using estimate of maximum likelihood estimator. Estimations of temporal land uses were done through open source GIS (Geographic Information System) - GRASS (Geographic Resource Analysis Support System, http://ces.iisc.ernet.in/grass).  70% of field data were used for classifying the data and the balance 30% were used in validation and accuracy assessment.  Thematic layers were generated of classifies data corresponding to four land use categories. Evaluation of the performance of classifiers is done through accuracy assessment techniques of testing the statistical significance of a difference, comparison of kappa coefficients and proportion of correctly allocated classes through computation of confusion matrix. These are most commonly used to demonstrate the effectiveness of the classifiers (Congalton, 1983; Congalton 1991).

Table 2:  Land use categories

Further each zone was divided into concentric circle of incrementing radius of 1 km (figure 3) from the center of the city for visualising the changes at neighborhood levels.  This also helped in identifying the causal factors and the degree of urbanization (in response to the economic, social and political forces) at local levels and visualizing the forms of urban sprawl. The temporal built up density in each circle is monitored through time series analysis.

Figure 3: Google earth representation of the study region along with the gradients

Direction-wise Shannon’s entropy (Hn) is computed (equation 1) to understand the extent of growth: compact or divergent (Lata et al., 2001, Sudhira et al., 2004). This provides an insight into the development (clumped or disaggregated) with respect to the geographical parameters across ‘n’ concentric regions in the respective zones.

                    ……    (1)

Where Pi is the proportion of the built-up in the ith concentric circle and n is the number of circles/local regions in the particular direction. Shannon’s Entropy values ranges from zero (maximally concentrated) to log n (dispersed growth).

3.1 5 Spatial pattern analysis

Landscape metrics provide quantitative description of the composition and configuration of urban landscape.  These metrics were computed for each circle, zonewise using classified landuse data at the landscape level with the help of FRAGSTATS (McGarigal & Marks, 1995). Urban dynamics is characterised by 7 prominent spatial metrics chosen based on complexity, and density criteria. The metrics include the patch area, shape, epoch/contagion/ dispersion and are listed in Table 3.

Table 3: Landscape metrics analysed