资源环境科技发展态势分析平台

1. Key Points

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A new method using image classification AI to predict potential natural vegetation corresponding to climate (monthly precipitation and monthly mean air temperature of a year) was developed. This method can estimate potential natural vegetation with ease and precision compared with pre-existing methods.

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The trained model accurately simulated a global map of current distributions of potential natural vegetation. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution under rapid warming trends.

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This method could also be applied to building empirical models of other climate-driven phenomena such as cropping systems and the spread of vector-borne diseases.

2. Overview

Scientific Researcher Hisashi SATO of the Institute of Arctic Climate and Environment Research (IACE), Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Assoc. Prof. Takeshi ISE of Kyoto University’s Field Science Education and Research Center jointly developed a new method using an image classification AI (*1) to estimate potential natural vegetation corresponding to given climates (*2).

This is the first application of image classification AI to reconstruct potential natural vegetation, and it offers a convenient and practical means to predict changes in vegetation distribution accompanying climate change. In this method, the image classification AI is trained to extract features of various types of potential natural vegetation based on seasonal change patterns represented in images. The trained AI was able to reproduce the current distribution of potential natural vegetation with better precision than pre-existing methods. Then, the trained AI was applied to climatic scenarios at the end of the 21^st century, predicting a significant shift in global vegetation distributions such as (1) decreased forest density in the southern part of the Amazon basin, (2) expansion of temperate forests on the North American and Eurasian continents, and (3) a shift in the distribution of boreal forests towards higher latitudes and altitudes. Although these trends are qualitatively consistent with previous research works, improvements in quantitative reliability are expected.

Since this method is simply an application of image classification AI, it demands much less technical skill and computer resources. Therefore, this method could also be applied to building empirical models of other climate-driven phenomena such as cropping systems and the spread of vector-borne diseases and hence has the potential to be a de facto standard for building empirical models across a range of research and application fields.

This study was published in Geoscientific Model Development on April 18 (Japan time).

Title:

Predicting Global Terrestrial Biomes with Convolutional Neural Network

Authors:

Hisashi Sato¹, Takeshi Ise²

Affiliation:

1. Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
2. Field Science Education and Research Center (FSERC), Kyoto University

doi:

https://doi.org/10.5194/gmd-15-3121-2022

This study was funded by the Ministry of Education, Culture, Sports, Science and Technology’s Arctic Challenge for Sustainability II (ArCS II) (JPMXD1420318865) and the Japan Society for the Promotion of Science’s Grants-in-Aid for Scientific Research (18H03357).