Journal of the Geological Society of India

, Volume 90, Issue 5, pp 609–615 | Cite as

Remote sensing for recognition and monitoring of vegetation affected by soil properties

  • M. C. Sashikkumar
  • S. Selvam
  • N. Karthikeyan
  • J. Ramanamurthy
  • S. VenkatramananEmail author
  • C. Singaraja


Vegetation is an intricate event with large amount of intrinsic spectral, spatial and temporal inconsistency and it is naturally characterized by strapping assimilation in the red wavelengths and towering reflectance in the near infra-red (NIR) wavelengths of the electromagnetic spectrum. The image descriptions generating from various vegetation index like NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index) etc., from multispectral imagery be able to provide exclusive vegetation information about an area. Soil environment circumstances are considerable influence on partial canopy spectra and vegetation index. Consequently, it is significant to monitor the vegetation vitality changes with reverence to the soil background circumstances. The present study an appropriate remote sensing based algorithm, i.e. soil adjusted vegetation index (SAVI) was selected. The investigation of vegetation vigor variations was done for dissimilar time sequence in the part of Andhra Pradesh State, India. The MODIS vegetation index images of 250m resolution are used. NDVI and NDWI images are derivative for red and black soil types and SAVI model was fashioned and executed in ERDAS IMAGINE platform. In SAVI equation, the soil accustomed factor ‘L’ was personalized with dissimilar values and multivariate SAVI images are derived for both red and black soil regions. In the an assortment of red soil regions, the SAVI with different ‘L’ values of 0.25, 0.3, 0.4, 0.5 and black soil region, the vegetation envelop is medium and SAVI with ‘L’ values of 0.3 and 0.4 fashioned fair result on variations of soil and vegetation reflectance over the crop period. The present study was done with the two types of soil regions and with accessible datasets. The psychoanalysis fraction of the study can be extended with multiple data sets and dissimilar seasons.


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Copyright information

© Geological Society of India 2017

Authors and Affiliations

  • M. C. Sashikkumar
    • 1
  • S. Selvam
    • 2
  • N. Karthikeyan
    • 1
  • J. Ramanamurthy
    • 3
  • S. Venkatramanan
    • 4
    • 5
    Email author
  • C. Singaraja
    • 6
  1. 1.Department of Civil EngineeringAnna University: Tirunelveli RegionTirunelveliIndia
  2. 2.Department of GeologyV.O. Chidambaram CollegeTuticorinIndia
  3. 3.Scientist/Engineer, ‘SG’, GH, PPEGNational Remote Sensing CentreHyderabadIndia
  4. 4.Department for Management of Science and Technology DevelopmentTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Applied SciencesTon Duc Thang UniversityHo Chi Minh CityVietnam
  6. 6.Department of GeologyPresidency CollegeChennaiIndia

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