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Mapping and Prediction of Urban Growth using Remote Sensing, Geographic Information System, and Statistical Techniques for Tiruppur Region, Tamil Nadu, India.
- Source :
- Journal of the Indian Society of Remote Sensing; Aug2023, Vol. 51 Issue 8, p1657-1671, 15p
- Publication Year :
- 2023
-
Abstract
- Tiruppur is one of India's major textile cities in Tamil Nadu, India, and it is seeing fast urbanization. Tiruppur, like other cities throughout the world, confronts urbanization issues. The prediction of urban expansion supports planners in identifying the urbanization trend and thereby planning appropriately. There are several approaches for forecasting urbanization trends, and this study attempts to forecast the urbanization trend using logistic regression (LR) and frequency ratio (FR) to create the Urban Growth Probability Index (UGPI) map for the year 2001. The FR and LR models can be used to predict the urbanization trend for the year 2021. For the current study, elevation, slope, aspect, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), distance from roads, rivers, lakes, the Land Use Land Cover (LULC) map for the years 1991 and 2001, and the population density map for the years 1991 and 2001 are regarded as independent variables and urban growth (UG) from 1991 to 2001 as the dependent variable. The UGPI map of the LR and FR models clearly showed population density, NDVI, NDBI, and LULC as isolated factors that have a strong correlation with urban development, and by changing the factors to the model, prediction of urban growth for any year is possible. The results clearly conclude that the urban sprawl is more towards the north and north-western regions due to the presence of more commercial and industrial centres, whereas the southern region shows very less urban sprawl due to the absence of industrial centres and witness more agricultural activities. It is now easier for planners to predict where the real growth in the study area will occur based on the results of the current work in the application of FR and LR models for UGPI attempts to determine the variables that are connected directly. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0255660X
- Volume :
- 51
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Journal of the Indian Society of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 170397768
- Full Text :
- https://doi.org/10.1007/s12524-023-01725-w