Developing countries are faced with the problem of increasing urban poverty levels, higher population growth rates and rising numbers of slums or squatters resulting out of sprawl. This is in contrast to developed countries, where the problem of sprawl has to be addressed in terms of transport, energy, land use, and environment. It is in this context that the study on landscape dynamics focusing on urban sprawl gains importance.

Nevertheless, in a majority of the cases there are inadequacies to ascertain the nature of uncontrolled growth. This necessitates prior planning, coordinated decision-making and visualisation of the consequences of urbanization to ensure the sustainability of the resources.

In order to address urban growth challenges without compromising the environment and their local communities, land use planning considering landscape dynamics is necessary and crucial, especially to developing countries under severe environmental and demographic transitions (Food and Agriculture Organization, 1995). Urban land expansion and urban land use/land cover change has been one of the key subjects for study on dynamic changes of urban land use (Dewan & Yamaguchi, 2009; Wu et al., 2006)

It is thus imperative to carry out better regional planning through proper understanding of the implications associated with the problem of unplanned urban growth or sprawl. Given the benefits and constraints of image acquisition and data, there needs to be bank of data across various levels of administration that gives planners and administrators the way to define policies, plan and execute the programme efficiently and sustainably.

Objectives

The objective of the proposed research is to understand and model the spatio temporal patterns of landscape dynamics. This involves

1. Analysis of Landscape dynamics using multi-resolution (spatial, temporal) data.
2. Quantifying landscape dynamics using landscape metrics and associated landscape parameters.
3. Modeling of landscape dynamics using these parameters.
4. Model the landscape metrics using soft computing techniques.

Methods

1. Preprocessing: The remote sensing data will be obtained and geo-referenced, rectified and cropped pertaining to the study area. Preprocessing techniques required will be applied
2. Land Cover Analysis: Normalised Difference Vegetation index (NDVI) will be computed temporally to understand the change of land cover during the study period. NDVI is the most common measurement used for measuring vegetation cover. It ranges from values -1 to +1. Very low values of NDVI (-0.1 and below) correspond to barren areas of rock, sand, or urban/builtup. Zero indicates the water cover. Moderate values represent low density of vegetation (0.1 to 0.3), while high values indicate vegetation (0.6 to 0.8).
3. Land use analysis: This will be carried out using available data using both supervised and unsupervised pattern classifiers (whichever is suitable). For the purpose of accuracy assessment, a confusion matrix is used. Land Use analysis will be done using the temporal data through open source GRASS GIS - Geographic Resource Analysis Support System (http://wgbis.ces.iisc.ernet.in/grass).
4. Density Gradient Analysis: The classified image will then divided into four zones based on four directions based on the city center (Central Business district). The zones are named as– Northwest (NW), Northeast (NE), Southwest (SW) and Southeast (SE) respectively (Figure 2). The growth of the urban areas will be monitored in each zone separately through the computation of urban density for different periods.
5. Division of these zones to concentric circles and computation of metrics: Each zone will be further divided into incrementing concentric circles of 1km radius from the center of the city. The built up density in each circle is monitored overtime using time series analysis.
6. Analyzing and evaluating the efficiency of various landscape matrices.
7. Modeling the outcomes of the concentric circle study using suitable modeling techniques (including soft computing techniques)