Neural Classification of Terrestrial Biomes

Vyacheslav Shen, Dong-Kyum Kim, Elke Zeller, Meeyoung Cha

Abstract: Predicting vegetation changes under climate change is crucial because it will alter the distribution of different plants and have repercussions for ecosystems. To detect changes in vegetation, we employ biome classification that assigns vegetation distributions to specific biomes. Conventional methods have used empirical formulas or simple vegetation models. Based on previous research that showed the use of convolutional neural networks (CNN), this work employs multiple deep models to classify biomes with the goal of predicting future changes. Experiments over multiple datasets demonstrate that Transformer models can be a suitable alternative to the CNN model. In addition, we observe that the use of additional climate variables helps improve the prediction accuracy without overfitting the data, which previous studies have not considered. We discuss the future directions of machine learning for biome classification as a complement to traditional biome classification methods.