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2017 | 26 | 4 |

Tytuł artykułu

Predictive species habitat distribution modelling of Indian sandalwood tree using GIS

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The Indian sandalwood tree is a highly valuable aromatic wood resource. But its population is declining at an alarming rate that cannot be controlled through manually employed management techniques. Also, the limitations of the automated management techniques in practice, such as the drawbacks of models like MAXENT, GARP, etc., can dealt with the usage of the techniques used in this research. ‘Strategy-oriented prescriptive process modelling’ has been employed in this research so that the management techniques are brought under automation for quicker processing of algorithms. The outcome of this research is a toolbox that contains the ‘machine-learned algorithms’ for performing ecological niche modelling of Santalum album for any given study area, ensuring that all the necessary data (concerned with the study area) are fed as inputs so that the final site suitability map is obtained as the output. The results have been well-discussed in the form of tables in this manuscript. Upon validation (through ground truthing) of the final output we observed that the toolbox designed in this research is able to give 90.625% accuracy in the resulting maps it produces.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

4

Opis fizyczny

p.1627-1642,fig.,ref.

Twórcy

autor
  • Institute of Remote Sensing, Department of Civil Engineering, Anna University Chennai, Tamil Nadu, India
  • Institute of Remote Sensing, Department of Civil Engineering, Anna University Chennai, Tamil Nadu, India

Bibliografia

  • 1. ARUN KUMAR A., GEETA JOSHI, MOHAN RAM H. Sandalwood: history, uses, present status and the future. Current Science, 103, 2012.
  • 2. ORWA, C., MUTUA, A., KINDT, R., JAMNADASS, R., & SIMONS, A. Agro-forestry database 4.0: a tree reference and selection guide, 2009.
  • 3. HIJMANS R.J., CAMERON S.E, PARRAN J.L., JONES P.G., JARVIS A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 2005.
  • 4. JARVIS A., REUTER H.I., NELSON A., GUEVARA E. CGIAR-CSI SRTM 90 m Database. Available online: http://srtm.csi.cgiar.org (accessed on 09 July 2014).
  • 5. MARIANA V., TONY C., TOM H., SYLVIA M., GEORGE R., CARR J.R., NANCY N. Community Climate System Model 3.0. National Center for Atmospheric Research, Boulder, Colorado, USA, 2004.
  • 6. TOMISLAV H., JORGE M.D.J, GERARD B.M.H., MARIA R.G., MILAN K., ALEKSANDAR B., WEI S., MARVIN N.W., XIAOYUAN G., BERNHARD B.M., MARIO A.G., RODRIGO V.R, BAS K. SoilGrids 250 m: global gridded soil information based on Machine Learning, 1, 2016.
  • 7. PEARSON R.G. Species Distribution Modeling for Conservation Educators and Practitioners, Center for Biodiversity and Conservation & Department of Herpetology, American Museum of Natural History, USA, 40, 2008.
  • 8. ROLLAND C., THANOS P. A Comprehensive View of Process Engineering. Proceedings of the 10th International Conference, CAiSE’98. 1410, 1998.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-d47e2ed1-f63b-4172-947d-4296466d0e1c
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