Optimal Support Vector Machines for forest above-ground biomass estimation from multisource remote sensing data

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2012, Pages 6388-6391

  Guo, Ying ^a,b   Li, Zengyuan ^a,b   Zhang, Xu ^a,b   Chen, Er Xue ^a,b   Bai, Lina ^a,b   Tian, Xin ^a,b   He, Qisheng ^c   Feng, Qi ^a,b   Li, Wenmen ^a,b  

^a RESEARCH INSTITUTE OF FORESTRY   (China)

^b State Forestry Administration   (China)

^c HOHAI UNIVERSITY   (China)

Author keywords

BPNN; forest above ground biomass; KNN; multisource remote sensing estimation; random forest; RBFNN; SVM

Indexed keywords

ABOVE GROUND BIOMASS; BPNN; KNN; MULTISOURCES; RANDOM FORESTS; RBFNN; SVM;

BIOMASS; DECISION TREES; ESTIMATION; GEOLOGY; OPTIMIZATION; REMOTE SENSING;

SUPPORT VECTOR MACHINES;

EID: 84873118986     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2012.6352721     Document Type: Conference Paper

References (17)

1. S. Brown, J. Sathaye, M. Canell, "Mitigation of carbon emission to the atmosphere by forest management," Commonwealth Forestry Review, Commonwealth Forestry Association, United Kingdom, vol.75, pp. 80-91, 1996.
2. R. A. Houghton, "Above-ground forest biomass and the global carbon balance," Global Change Biology, John Wiley and Sons, United States, vol. 11, pp. 945-958, 2005.
3. G.M. Foody, D.S. Boyd, M. E. J. Cutler, "Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions," Remote Sensing of Environment, Elsevier Science, United States, vol.77, pp. 463-474, 2003.
4. D. Lu, "The potential and challenge of remote sensing-based biomass estimation," International Journal of Remote Sensing, Taylor and Francis Group, United States, vol.27, no.7, pp.1297- 1328, 2006.
5. Lu, D., "Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon Basin," International Journal of Remote Sensing, Taylor and Francis Group United States, vol.26, pp.2509-2525, 2005.
6. D. Zheng, J. Rademacher, J. Chen, T. Crow, M. Bresee, J. Moine, S. Ryu, "Estimation aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin," Remote Sensing of Environment, Elsevier Science, United States, vol.93, pp.402-411, 2004.
7. Hector Franco-Lopeza, R. Ek. Alan, E. Marvin, "Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method," Remote Sensing of Environment, Elsevier Science, United States, vol.77, pp. 251-274, 2001.
8. G. Chirici, "Non-parametric and parametric methods using satellite images for estimation growing stock volume in alpine and Mediterranean forest ecosystems," Remote Sensing of Environment, Elsevier Science, United States, vol.112, pp. 2686- 2700, 2008.
9. WANG Shujun, GUAN Dongsheng, "Remote sensing method of forest biomass estimation by artificial neural network models," Ecology and Environment, A.P.H. Publishing Corporation, India vol.16, no.1, pp. 108-111, 2007.
10. Vapnik, V., The nature of statistical learning theory. New York:Springer-Verlag, pp. 87-189,1995.
11. Haigang Zhan, Ping Shi, Chuqun Chen, "Retrieval of Oceanic Chlorophyll Concentration Using Support Vector Machines" IEEE Transactions on geosciences and remote sensing, IEEE Inc, United States, vol.41, no.12, pp. 2947-2951, 2003.
12. Gang Chen, J. Hay. Geoffrey, "Estimation of Forest Height, Biomass and Volume Using Support Vector Regression and Segmentation From Lidar Transects and Quickbird Imagery," Geoinformatics, 2010 18th International Conference.
13. L. Breiman, "Random forests," Machine Learning, MIT Press, United States, vol.45, no.1, pp. 5-32, 2001.
14. Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot, "Variable selection using random forests," Pattern Recognition Letters, Elsevier Science, United States, vol. 31, issue 14, 15, pp. 2225-2236, 2010.
15. I. H. Witten, F. Eibe, "Data Mining: Practical Machine Learning Tools and Techniques," 2nd ed. San Francisco, CA: Morgan Kaufmann, 2005.
16. Y. W. Chen, C. J. Lin, "Combining SVMs with Various Feature Selection Strategies" Studies in Fuzziness and Soft Computing, Springer, vol. 207, pp.315-324, 2006.
17. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001.