Spatiotemporal Prediction of Alpine Vegetation Dynamic Change Based on a ConvGRU Neural Network Model: A Case Study of the Upper Heihe River Basin in Northwest China

Spatiotemporal Prediction of Alpine Vegetation Dynamic Change Based on a ConvGRU Neural Network Model: A Case Study of the Upper Heihe River Basin in Northwest China

Accurate and comprehensive vegetation prediction methods are essential for effective agricultural planning and budgeting. Most existing vegetation prediction methods rely on sampling points rather than on overall spatiotemporal characteristics, making it difficult to accurately forecast vegetation c...

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Bibliographic Details
Main Authors: Lifeng Zhang, Haowen Yan, Yi He, Sheng Yao, Shengpeng Cao, Qiang Sun
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning
Qilian mountains
spatiotemporal prediction
vegetation dynamic change
Online Access:https://ieeexplore.ieee.org/document/9864041/

Internet

https://ieeexplore.ieee.org/document/9864041/

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