Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century DOI Creative Commons
Hao Zhang, Zengyun Hu, Xi Chen

et al.

Global Change Biology, Journal Year: 2025, Volume and Issue: 31(3)

Published: March 1, 2025

ABSTRACT Future variations of global vegetation are paramount importance for the socio‐ecological systems. However, up to now, it is still difficult develop an approach project considering spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid‐by‐grid; multi‐algorithms; optimal combination) construct using six algorithms (i.e., LR: linear regression; SVR: support vector RF: random forest; CNN: convolutional neural network; LSTM: long short‐term memory; transformer) based on five climatic factors Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, CO 2 ). The employed future changes in leaf area index (LAI) four sub‐regions: high‐latitude northern hemisphere (NH), mid‐latitude NH, tropics, southern hemisphere. Our results indicate that LAI will continue increase, with greening rate expanding 2.25 times NH by 2100 against 1982–2014 period. Moreover, RF shows strong applicability In study, we introduce innovative GGMAOC, which provides new scheme environmental geoscientific research.

Language: Английский

Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century DOI Creative Commons
Hao Zhang, Zengyun Hu, Xi Chen

et al.

Global Change Biology, Journal Year: 2025, Volume and Issue: 31(3)

Published: March 1, 2025

ABSTRACT Future variations of global vegetation are paramount importance for the socio‐ecological systems. However, up to now, it is still difficult develop an approach project considering spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid‐by‐grid; multi‐algorithms; optimal combination) construct using six algorithms (i.e., LR: linear regression; SVR: support vector RF: random forest; CNN: convolutional neural network; LSTM: long short‐term memory; transformer) based on five climatic factors Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, CO 2 ). The employed future changes in leaf area index (LAI) four sub‐regions: high‐latitude northern hemisphere (NH), mid‐latitude NH, tropics, southern hemisphere. Our results indicate that LAI will continue increase, with greening rate expanding 2.25 times NH by 2100 against 1982–2014 period. Moreover, RF shows strong applicability In study, we introduce innovative GGMAOC, which provides new scheme environmental geoscientific research.

Language: Английский

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