文献类型：期刊文献

英文题名：Prediction and Spatiotemporal Dynamics of Vegetation Index Based on Deep Learning and Environmental Factors in the Yangtze River Basin

作者：Wang, Yin[1] Zhang, Nan[1] Chen, Mingjie[1] Zhao, Yabing[1] Guo, Famiao[1] Huang, Jingxian[1] Peng, Daoli[1] Wang, Xiaohui[2]

第一作者：Wang, Yin

通信作者：Peng, DL[1];Wang, XH[2]

机构：[1]Beijing Forestry Univ, State Key Lab Efficient Prod Forest Resources, Beijing 100083, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2025

卷号：16

期号：3

外文期刊名：FORESTS

收录：;EI(收录号：20251318136485);Scopus(收录号：2-s2.0-105001152169);WOS:【SCI-EXPANDED(收录号:WOS:001452346500001)】；

基金：This research was funded by the National Key R&D Program of China (Grant No. 2022YFF0711602) and National Key R&D Program of China (Grant No. 2023YFD2200403).

语种：英文

外文关键词：CNN-BiLSTM-AM; vegetation index prediction; Yangtze River basin; Shared Socioeconomic Pathways (SSPs) Scenario

摘要：Accurately predicting the vegetation index (VI) of the Yangtze River Basin and analyzing its spatiotemporal trends are essential for assessing vegetation dynamics and providing recommendations for environmental resource management in the region. This study selected the key climate factors most strongly correlated with three vegetation indexes (VI): the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel Normalized Difference Vegetation Index (kNDVI). Historical VI and climate data (2001-2020) were used to train, validate, and test a CNN-BiLSTM-AM deep learning model, which integrates the strengths of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM). The performance of this model was compared with CNN-BiLSTM, LSTM, and BiLSTM-AM models to validate its superiority in predicting the VI. Finally, climate simulation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP2-4.5, and SSP5-8.5) were used as inputs to the CNN-BiLSTM-AM model to predict the VI for the next 20 years (2021-2040), aiming to analyze spatiotemporal trends. The results showed the following: (1) Temperature, precipitation, and evapotranspiration had the highest correlation with VI data and were used as inputs to the time series VI model. (2) The CNN-BiLSTM-AM model combined with the EVI achieved the best performance (R2 = 0.981, RMSE = 0.022, MAE = 0.019). (3) Under all three scenarios, the EVI over the next 20 years showed an upward trend compared to the previous 20 years, with the most significant growth observed under SSP5-8.5. Vegetation in the source region and the western part of the upper reaches increased slowly, while significant increases were observed in the eastern part of the upper reaches, middle reaches, lower reaches, and estuary. The analysis of the predicted EVI time series indicates that the vegetation growth conditions in the Yangtze River Basin will continue to improve over the next 20 years.