Articles | Volume 15, issue 7
Development and technical paper
18 Apr 2022
Development and technical paper |  | 18 Apr 2022

Predicting global terrestrial biomes with the LeNet convolutional neural network

Hisashi Sato and Takeshi Ise

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Cited articles

Benkendorf, D. J. and Hawkins, C. P.: Effects of sample size and network depth on a deep learning approach to species distribution modeling, Ecol. Inform., 60, 101137,, 2020. 
Bond, W. J., Midgley, G. F., and Woodward, F. I.: The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas, Global Change Biol., 9, 973–982,, 2003. 
Botella, C., Joly, A., Bonnet, P., Monestiez, P., and Munoz, F.: A Deep Learning Approach to Species Distribution Modelling, in: Multimedia Tools and Applications for Environmental & Biodiversity Informatics, edited by: Joly, A., Vrochidis, S., Karatzas, K., Karppinen, A., and Bonnet, P., Springer Switzerland, 169–199,, 2018. 
Box, E. O.: Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography, Tasks for Vegetation Science, 1, Springer Netherlands,, 1981. 
Breshears, D. D., Cobb, N. S., Rich, P. M., Price, K. P., Allen, C. D., Balice, R. G., Romme, W. H., Kastens, J. H., Floyd, M. L., Belnap, J., Anderson, J. J., Myers, O. B., and Meyer, C. W.: Regional vegetation die-off in response to global-change-type drought, P. Natl. Acad. Sci. USA, 102, 15144–15148,, 2005. 
Short summary
Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.