Abstract
Mapping and understanding the spatial distribution of forest aboveground biomass (AGB) is an important and challenging task. This paper describes an exercise of predicting the forest AGB of Guinea-Bissau, West Africa, using synthetic aperture radar data and measurements of tree size collected in field campaigns. Several methods were attempted, from linear regression to different variants and techniques of Genetic Programming (GP), including the cutting edge geometric semantic GP approach. The results were compared between each other in terms of root mean square error and correlation between predicted and expected values of AGB. None of the methods was able to produce a model that generalizes well to unseen data or significantly outperforms the model obtained by the state-of-the-art methodology, and the latter was also not better than a simple linear model. We conclude that the AGB prediction is a difficult problem, aggravated by the small size of the available data set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Campbell, B.: Beyond Copenhagen: Redd plus, agriculture, adaptation strategies and poverty. Global Environmental Change-Human and Policy Dimensions 19(4), 397–399 (2009)
Carreiras, J., Vasconcelos, M., Lucas, R.: Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment 121, 426–442 (2012)
Friedman, J.: Stochastic gradient boosting. Computational Statistics & Data Analysis 38(4), 367–378 (2002)
Gathercole, C., Ross, P.: Dynamic Training Subset Selection for Supervised Learning in Genetic Programming. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 312–321. Springer, Heidelberg (1994)
Gonçalves, I., Silva, S., Melo, J.B., Carreiras, J.M.B.: Random Sampling Technique for Overfitting Control in Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 218–229. Springer, Heidelberg (2012)
Gustafson, S., Vanneschi, L.: Crossover-based tree distance in genetic programming. IEEE Transactions on Evolutionary Computation 12(4), 506–524 (2008)
Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Proceedings of GECCO 1999, vol. 2, pp. 1053–1060 (1999)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Liu, Y., Khoshgoftaar, T.: Reducing overfitting in genetic programming models for software quality classification. In: Proceedings of the Eighth IEEE Symposium on International High Assurance Systems Engineering, Tampa, Florida, USA, March 25-26, pp. 56–65 (2004)
Lucas, R., Armston, J., Fairfax, R., Fensham, R., Accad, A., Carreiras, J., Kelly, J., Bunting, P., Clewley, D., Bray, S., Metcalfe, D., Dwyer, J., Bowen, M., Eyre, T., Laidlaw, M.: An evaluation of the alos palsar l-band backscatter – above ground biomass relationship over Queensland, Australia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3(4), 576–593 (2010)
Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Proceedings of GECCO 2002, pp. 829–836. Morgan Kaufmann (2002)
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric Semantic Genetic Programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)
Pan, Y., Birdsey, R., Fang, J., Houghton, R., Kauppi, P., Kurz, W., Phillips, O., Shvidenko, A., Lewis, S., Canadell, J., Ciais, P., Jackson, R., Pacala, S., McGuire, A., Piao, S., Rautiainen, A., Sitch, S., Hayes, D.: A large and persistent carbon sink in the world’s forests. Science 333(6045), 988–993 (2011)
Paris, G., Robilliard, D., Fonlupt, C.: Applying Boosting Techniques to Genetic Programming. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 267–918. Springer, Heidelberg (2002)
Poli, R., Langdon, W.B., Mcphee, N.F.: A field guide to genetic programming (March 2008), http://www.gp-field-guide.org.uk
Robilliard, D., Fonlupt, C.: Backwarding: An Overfitting Control for Genetic Programming in a Remote Sensing Application. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 245–254. Springer, Heidelberg (2002)
Silva, S., Costa, E.: Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141–179 (2009)
Suen, Y.L., Melville, P., Mooney, R.J.: Combining Bias and Variance Reduction Techniques for Regression Trees. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 741–749. Springer, Heidelberg (2005)
Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP applied to predicting pharmacokinetic parameters. In: Proceedings of EuroGP 2013, Springer (to appear, 2013)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B 67, 301–320 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Silva, S. et al. (2013). Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-37192-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
Online ISBN: 978-3-642-37192-9
eBook Packages: Computer ScienceComputer Science (R0)