LU/LC Change Detection Using NDVI & MLC Through Remote Sensing and GIS for Kadapa Region

  • A. RajaniEmail author
  • S. Varadarajan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)


This research letter considered land use/land cover (LU/LC) changes in Kadapa region from 2001 to 2016 by using GIS (Geographical Information Systems) LANDSAT 7 and 8 images level-1 data of ETM+ sensor were collected for the years 2001 and 2016 to estimate the NDVI values. Based on index values study area is classified into five classes like water bodies, built-up area, barren land, sparse vegetation and dense vegetation. The proposed method performs two-stage classification. From the results it is observed that, increase in barren land of 6.23%, built-up area of 24.74% and decrease of water resources of 2.87%, sparse vegetation of 2.62% and dense vegetation/forest land of 25.47% in the Kadapa region. Finally, the classification algorithm was assessed by the confusion matrix method. As NDVI is used basis for classification process vegetation and water resources classification is 100% and 86% built up and barren lands are classification, i.e., some of built-up areas classified as barren land and barren land as built-up area. Compared to existing classification algorithms based on Principal Component Analysis (PCA) and False Color Composite (FCC) the proposed NDVI histogram and Maximum Likelihood Classifier (MLC) multiple classification gives the best results to estimate the change detection. The work was carried out by using ArcGIS software.


Normalized Difference Vegetation Index (NDVI) Maximum Likelihood Classifier (MLC) Change detection Remote sensing GIS Multispectral image 



Authors would like to thank USGS Earth Explorer for providing LANDSAT images to carry out the present research work.


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of ECES V University College of EngineeringTirupatiIndia

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