期刊
ECOLOGY AND EVOLUTION
卷 11, 期 12, 页码 7335-7345出版社
WILEY
DOI: 10.1002/ece3.7564
关键词
climate change; climate sensitivity; deep learning; long short‐ term memory network; vegetation greenness; vegetation– climate relationship
资金
- National Key Research and Development Program of China [2016YFC0500701]
- National Natural Science Foundation of China [41790422, 41530747]
Utilizing global gridded climate-vegetation models based on deep learning algorithms allows for accurate simulation and prediction of vegetation dynamics, revealing the sensitivity of global vegetation to climate and providing a new approach to investigate climate sensitivity of vegetation.
Climate sensitivity of vegetation has long been explored using statistical or process-based models. However, great uncertainties still remain due to the methodologies' deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate-vegetation models based on long short-term memory (LSTM) network, which is a powerful deep-learning algorithm for long-time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep-learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据