Abstract
Objectives: To study the land cover change Salem city as a case study of urban expansion in India covering the span of 35 years from 1990 to 2025. Method: Remote sensing methodology is adopted to study the geographical land use changes occurred during the study period (year 1990- 2025). Landsat images of TM and ETM+ of Salem city area are collected from the USGS Earth Explorer website. After image pre-processing, unsupervised image classification has been performed to classify the images into different land use categories. Seven land use classes have been identified as road, urban (Build up), vegetation, water bodies, fallow land, mines and barren land. Classification accuracy is also estimated using the field knowledge obtained from field surveys. Findings: The obtained accuracy is between 83 to 86% of all the classes. Change detection analysis shows the built-up area has been increased by 1.49 km2 , vegetation area has been decreased by 11.55km2 . Application: Information on Urban growth, land use and land cover change study is very useful to local Government and Urban Planners for the betterment of the future plans of sustainable development of the city. Keywords: Land use /Land cover; Urban Sprawl; Urbanization; Remote Sensing; Landsat data; Salem city
Highlights
A huge amount of remote sensing data by a traditional manual method is daunting
Mining areas account for 11.81km2 (3.49%) and barren land covers about 55.83km2 (16.50%)
It reveals that urban areas will have moved up from 57.75 to 66.47 km2 with an increase of about 8.72 km2
Summary
A huge amount of remote sensing data by a traditional manual method is daunting. The advent of computers eliminates the manual data processing job by automatically processing and analyzing the remotely sensed data with the aid of powerful digital image processing software. Image classification is the process of grouping and labelling each pixel within the original image to a land use/cover information class [1,2]. Computer aided classification based on the pixel values are performed on the assumption that each spectral class corresponds to a spectral cover. This classification approach helps us to more quickly study the earth surface features from the image data and take necessary action immediately [3,4].
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