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Analysis of Carbon Sequestration Pattern in Tropical Fruit Trees

Author(s): G. Arulselvi | V. Ramalingam | S. Palanivel

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 30;
Issue: 7;
Start page: 24;
Date: 2011;
Original page

Keywords: Carbon sequestration | Pattern classification | Radial Basis Function Neural Network (RBFNN) | Cashew | Gross Primary Production (GPP)

Terrestrial carbon sequestration is an important pathway of minimizing CO2 concentration in the atmosphere. Tropical evergreen trees like cashew have some adaptive mechanism in an environment with strong seasonal variation of light and water.In this study, multilayer satellite images from the vegetation VGT sensors on board the Spot satellite 01/2006 to 12/2009 for Cashew plantations areas of Tamilnadu, India were analyzed for temporal variability. The temporal analysis of vegetation indices was done and the GPP Gross Primary Productivity was calculated using the satellite based vegetation photosynthesis model VPM. The enhanced vegetation index EVI identified subtle changes in the seasonal dynamics of leaf phenology in Cashew plantation area, as supported by leaf moisture content and leaf area index. The land surface water index LSWI, indicates that the plantation experienced water stress during the dry seasons. The VPM model which uses EVI, LSWI and site specific climate data for 20082009 predicted high GPP in the late wet season than in summer season. The GPP calculated from the remote sensing data are classified into three classes using Radial Basis Function Neural Network RBFNN. The calculated GPP of different months in a year showed that the monthly GPP ranged from 50128 g C/m2. The RBFNN is trained to provide an output value of 0, 1 and 2 for carbon sequestration which ranged from 5075, 76100 and 101125 g C/m2, respectively. The experimental results shows that the RBFNN classifies the carbon sequestration with an accuracy of 95.2%.
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