International Journal of Environmental Sciences

Volume 4 Issue 5 2014- March 2014    Pages: 772-785 <<Previous    Next>>

Biodiversity hot-spot modeling and temporal analysis of Meghalaya using Remote sensing technique

Author Information:

Krishnanjan Pakrasi1, Arya V.S 2, Sudhakar S.3
1- Student (M.Tech Geoinformatics), Department of Environmental Science and Engineering Guru Jambheshwar Univeristy of Science and Technology (Hisar, Haryana).
2- Senior Scientist-SG, Haryana Space Applications Centre (HARSAC)(Department of Science and Technology, Government of Haryana).
3- Director of North Eastern Space Applications Centre, Umiam, Meghalaya, India Govt. of India, Department of space.

ABSTRACT

Biodiversity is variation of leaving creatures within an ecosystem, biome or an entire planet. Health of an ecosystem can be measured through this variation. Remote sensing helps to manipulate and analyze the image data produced by these remote sensors. The primary goal of remote sensing is not only the obtain knowledge, but also its application of gained knowledge. Satellites provide real-time global coverage of the Earth. Biophysical spectral modeling techniques allow stratifying vegetation types based on the canopy closure (Roy et al., 1996). Through this such approach mapping and monitoring of forest condition and degradation processes can be done. This study has been taken up Meghalaya, state of India. The flora of Meghalaya is the richest in India. A wide variety of timber species, medicinal plants, and economically important plants are reported from this region. Due to lack of systemic approach it is being destroyed by weed, firewood, timber, jhum cultivation. Large scale over exploration has led to decrease in population of various orchids found here. Here in this study LISS-III data are used.

Keywords: Classification, NDVI, Patchiness, Porosity, Interspersion, Shape index.

.DOI:10.6088/ijes.2014040404518

© 2014 Copyright by the authors, licensee Integrated Publishing Association.This is an open access article distributed under the Creative Commons Attribution License (3.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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