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Space remote sensing for spatial vegetation characterization

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Abstract

The study area, Madhav National Park (MP) represents northern tropical dry deciduous forest. The national park, due to its unique location (nearest to township), is under tremendous biotic pressure. In order to understand vegetation structure and dynamics, vegetation mapping at community level was considered important. Prolonged leafless period and background reflection due to open canopy poses challenge in interpretation of satellite data. The vegetation of Madhav National Park was mapped using Landsat TM data. The ground data collected from sample points were subjected to TWINSPAN analysis to cluster sample point data into six communities. The vegetation classification obtained by interpretation (visual and digital) of remote sensing data and TWINSPAN were compared to validate the vegetation classification at community level. The phytosociological data collected from sample points were analysed to characterize communities. The results indicate that structural variations in the communities modulate spectral signatures of vegetation and form basis to describe community structure subjectively and at spatial level.

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Author notes

  1. Shirish A. Ravan

    Present address: IGCMC, WWF-India, 172B, Lodi Estate, 110 003, New Delhi, India

Authors and Affiliations

  1. Forestry and Ecology Division, Indian Institute of Remote Sensing, 4, Kalidas Road, 248 001, Dehradun, India

    Shirish A. Ravan, P. S. Roy & C. M. Sharma

  2. Department of Forestry, HNB Garhwal University, 246 174, Srinagar Garhwal, India

    C. M. Sharma

Authors
  1. Shirish A. Ravan
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  2. P. S. Roy
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  3. C. M. Sharma
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Ravan, S.A., Roy, P.S. & Sharma, C.M. Space remote sensing for spatial vegetation characterization. J Biosci 20, 427–438 (1995). https://doi.org/10.1007/BF02703846

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  • DOI: https://doi.org/10.1007/BF02703846

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