An Adroit Detection of Vegetation Index Covered in Remote Sensing Images

Authors

  • Dr. D. Napoleon  Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
  • M. Sivaranjani  Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
  • R. Saikumar  Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India

Keywords:

Image Analysis, Remote Sensing Images, NDVI, NIR and Vegetation Detection.

Abstract

Detecting the vegetation of land covers with plants and so are determined with the help of image using image processing. Image processing is a common technique which can be used in the field of remote sensing. The presence of vegetation has been detected from an image which can be captured and recorded from the ground to detect the presence of vegetation in a particular region. In this paper, the presence of vegetation covering the land by plants and most biotic elements of biosphere have been detected from the particular region through an image. There are number of researches using the method of vegetation detection are using NIR images which can be particularly suitable for detecting vegetation. Our method uses the features of an image from the visible spectrum of color satellite images. Main intention is to identify the texture feature set for the problematic in vegetation detection. The detection of vegetation is implemented using an algorithm called Normalised Difference Vegetation Index (NDVI).

References

  1. Ahmadi H, Nusrath A. Vegetation change detection of Neka River in Iran by using remote-sensing and GIS. Journal of geography and geology 2010; 2 (1), 58-67.
  2. Hacihaliloglu I, Karta M. DCT and DWT based image compression in remote sensing images. In proceeding IEEE conference on antennas and propagation society international symposium 2004; 4, 3856-3858.
  3. Gonzalez CR, Woods RE, Eddins SL. Digital Image processing Using MATLAB Pearsoned Education. Second Indian, Reprint; 2005.
  4. Karaburun A. Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean journal of applied sciences 2010.
  5. Chouhan R, Rao N. Vegetation detection in multispectral remote sensing images: protective role-analysis of coastal vegetation in 2004 Indian Ocean Tsunami. Geo-Information for disaster management, Turkey; 2011.
  6. Ramachandra TV, Kumar U. Geographic resources decision support system for land use, land cover dynamics analysis. In proceedings of the FOSS/GRASS users conference-Bangkok, Thailand 2004; 12-14.
  7. Xie Y, Zhao X, Li L, Wang H. Calculating NDVI for landsat7-etm data after atmospheric correction using 6s model: a case study in Zhangye City, China. In proceeding IEEE geoinformatics18th international conference on digital object identifier, 2010; 1-4.
  8. Gao, Bo. NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996; 58, 257-266.
  9. Hu Y, Ban Y, Zhang X, Liu J, Zhuang D. Spatial-temporal pattern of GIMMS NDVI and its dynamics in Mongolian Plateau. In Proceeding IEEE International workshop on earth observation and remote sensing applications 2008; 1-6.
  10. Nageswara PPR, Shobha SV, Ramesh KS, Somashekhar RK.. Satellite-Based assessment of agricultural drought in Karnataka State. Journal of the Indian society of remote sensing 2005; 33 (3), 429-434.

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. D. Napoleon, M. Sivaranjani, R. Saikumar, " An Adroit Detection of Vegetation Index Covered in Remote Sensing Images, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.483-487, March-April-2019.