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Analysis of Carbon Sequestration Pattern in Tropical Fruit Trees
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 30 - Number 7 |
| Year of Publication: 2011 |
| Authors: G. Arulselvi, V. Ramalingam, S. Palanivel |
10.5120/3654-5107
|
G. Arulselvi, V. Ramalingam, S. Palanivel . Analysis of Carbon Sequestration Pattern in Tropical Fruit Trees. International Journal of Computer Applications. 30, 7 ( September 2011), 24-29. DOI=10.5120/3654-5107
Abstract
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 2008-2009 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 50-128 g C/m2. The RBFNN is trained to provide an output value of 0, 1 and 2 for carbon sequestration which ranged from 50-75, 76-100 and 101-125 g C/m2, respectively. The experimental results shows that the RBFNN classifies the carbon sequestration with an accuracy of 95.2%.
References
- X. Xiao, B. Braswell, Q. Zhang, S. Boles, S. Frolking, B. Moore, Sensitivity of vegetation indices to atmospheric acrosols: Continental-scale observations in Northern Asia, Remote Sensing of Environment. 84 (2003) 385–392.
- A. R. Huete, H. Q. Liu, K. Batchily, W. VanLeeuwen, A comparison of vegetation indices global set of TM images for EOS-MODIS, Remote Sensing of Environment. 59 (1997) 440 451.
- X. Xiao, D. Hollinger, J. D. Aber, M. Goltz, E. A. Davidson, Q. Y. Zhang, satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sensing of Environment. 89 (2004a) 519–534.
- C. B. Field, J. T. Randerson, C. M. Malmstrom, Global net primary production-combining ecology and remote-sensing, Remote Sensing of Environment. 51 (1995) 74–88.
- S. D. Prince, S. N. Goward, Global primary production: A remote sensing approach, Journal of Biogeography. 22 (1995) 815–835.
- A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment. 83 (2002) 195–213.
- S. Boles, X. Xiao, J. Liu, Q. Zhang, S. Munkhutya, S. Chen, Land cover characterization of Temperate East Asia: Using multi-temporal image data of VEGETATION sensor, Remote Sensing of Environment. 90 (2004) 477–489.
- X. Y. Zhang, M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, Monitoring vegetation phenology using MODIS, Remote Sensing of Environment. 84 (2003) 471–475.
- P. Ceccato, S. Flasse, S. Tarantola, S. Jacquemoud, J. M. Gregoire, Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sensing of Environment. 77 (2001) 22–33.
- P. Ceccato, N. Gobron, S. Flasse, B. Pinty, S. Tarantola, Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 Theoretical approach, Remote Sensing of Environment. 82 (2002) 188–197.
- X. M. Xiao, S. Boles, J. Y. Liu, D. F. Zhuang, M. L. Liu, Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data, Remote Sensing of Environment. 82 (2002) 335– 348.
- M. Maki, M. Ishiahra, M. Tamura, Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data, Remote Sensing of Environment. 90 (2004) 441–450.
- H. Rahman, G. Dedieu, SMAC: A simplified method foratmospheric correction of satellite measurements in the solar spectrum, International Journal of Remote Sensing. 15 (1994) 123–143.
- S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson Education, Singapore, 2001.
- C. Field, Leaf-age effects on stomatal conductance. in E. Zeiger, G. Farquhar and I. R. Cowan (Eds), Global Biogeochemical Cycles. (1987) 367–384.
- D. A. Roberts, B. W. Nelson, J. B. Adams, F. Palmer, Spectral changes with leaf aging in Amazon caatinga, Trees-Structure and Function. 12 (1998) 315–325.
- Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, JohnWiley-Interscience, New York, 2001.
- R. R. Nemani, C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, Climate-driven increases in global terrestrial net primary production from 1982 to 1999, Science. 300 (2003) 1560–1563.
- X. Xiao, Q. Zhang, B. Braswell, S. Urbanski, S. C. Wofsy, Modeling gross primary production of a deciduous broadleaf forest using satelite images and climate data, Remote Sensing of Environment. 91 (2004) 256–270.
- A. Huete, P. Ratana, K. Didan, Y. Shimabukuro, H. Barbosa, L. G. Ferreira, Seasonal biophysical dynamics along an Amazon eco-climatic gradient using MODIS vegetation indices. anais Xi SBSR, Belo Horizonte, Brasil7 Instituto Nacional de Pesquisas Espaciais. 05-10 (2003) 665–672.
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