Temporal Change Detection in Water Body of Puzhal Lake Using Satellite Images

Nikhitha (), Laxmi Divya (), R. Karthi () and P. Geetha ()
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Nikhitha: Amrita School of Engineering, Department of Computer Science and Engineering
Laxmi Divya: Amrita School of Engineering, Department of Computer Science and Engineering
R. Karthi: Amrita School of Engineering, Department of Computer Science and Engineering
P. Geetha: Amrita School of Engineering, Center for Computational Engineering and Networking (CEN)

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1229-1237 from Springer

Abstract: Abstract Water is an important element to survive for humans and other forms of life. Due to rapid industrialization and urban growth, water bodies are shrinking and affected by anthropogenic activities. Remote sensed images serve as an important tool that captures historic data to understand changes in environment. This paper focus of identifying shrinkage that have occurred in lake water bodies over a period of time. Puzhal Lake located in Chennai city is considered for the study. Landsat image of 1999 and 2009 are used to identify the spatial and temporal changes that have occurred in the lake over a period of time. Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Vegetation Index (NDVI) are applied on the data to differentiate water bodies from land cover. Pre-processing of images are done in ArcGIS followed by unsupervised classification. Classification methods are applied on these images to extract water bodies. Topographic maps are also used as reference to validate the results. From the results obtained in the above process, lake regions are identified and pixel count is calculated. Pixel count from two images over a period is used to identify the change in the area of lakes. The results show that there is shrinkage in the area of Puzhal Lake which is chosen as our study area. The results help us to understand change in water bodies and take measures to conserve them.

Keywords: Remote sensing; GIS; Landsat; NDWI; MNDWI; Lakes; Water bodies; Classification (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_124

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DOI: 10.1007/978-3-030-41862-5_124

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