[A-CG50] 地球環境科学と人工知能

[ACG50-02] Predicting global potential natural vegetation with an image recognition AI

*佐藤 永¹、伊勢 武史² (1.海洋研究開発機構 地球表層物質循環研究分野、2.京都大学 フィールド科学教育研究センター)

キーワード：気候エンベロープ、潜在植生地図、画像分類AI

Potential natural vegetation (PNV) is the vegetation cover equilibrium with environmental condition, which would exist at a given location without human land-conversion. For operational mapping of PNV, we developed an empirical model using a deep neural network (DNN), which was trained by an observation based PNV map (Figure 1) and graphical images of global air temperature and precipitation at 0.5 degree resolution. The trained model well reconstructs an observation based global PNV map, demonstrating that this way of DNN application can capture empirical relationships between PNV and climate. Then, the trained model was applied to projected climate at the end of the 21^st century, predicting significant shift of global PNV distribution with rapid warming trends (Figure 2).