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Title: Predicting global terrestrial biomes with the LeNet convolutional neural network
Authors: Sato, Hisashi
Ise, Takeshi
Author's alias: 佐藤, 永
伊勢, 武史
Issue Date: Apr-2022
Publisher: European Geosciences Union
Copernicus GmbH
Journal title: Geoscientific Model Development
Volume: 15
Issue: 7
Start page: 3121
End page: 3132
Abstract: A biome is a major regional ecological community characterized by distinctive life forms and principal plants. Many empirical schemes such as the Holdridge life zone (HLZ) system have been proposed and implemented to predict the global distribution of terrestrial biomes. Knowledge of physiological climatic limits has been employed to predict biomes, resulting in more precise simulation; however, this requires different sets of physiological limits for different vegetation classification schemes. Here, we demonstrate an accurate and practical method to construct empirical models for biome mapping: a convolutional neural network (CNN) was trained by an observation-based biome map, as well as images depicting air temperature and precipitation. Unlike previous approaches, which require assumption(s) of environmental constrain for each biome, this method automatically extracts non-linear seasonal patterns of climatic variables that are relevant in biome classification. The trained model accurately simulated a global map of current terrestrial biome distribution. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution with rapid warming trends. Our results demonstrate that the proposed CNN approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution. Moreover, we anticipate that our approach could provide a basis for more general implementations to build empirical models of other climate-driven categorical phenomena.
Description: 画像分類AIを用いた潜在植生を予測する新手法を開発. 京都大学プレスリリース. 2022-04-19.
Rights: © Author(s) 2022.
This work is distributed under the Creative Commons Attribution 4.0 License.
DOI(Published Version): 10.5194/gmd-15-3121-2022
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