Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review

A Ferchichi, AB Abbes, V Barra, IR Farah - Ecological Informatics, 2022‏ - Elsevier
Over the last few years, Deep learning (DL) approaches have been shown to outperform
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …

Monthly NDVI prediction using spatial autocorrelation and nonlocal attention networks

L Xu, R Cai, H Yu, W Du, Z Chen… - IEEE Journal of Selected …, 2024‏ - ieeexplore.ieee.org
Accurate prediction of vegetation indices is useful for hel** maintain vegetation stability,
sustaining food production, and reducing socioeconomic losses. The traditional …

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

H Zheng, H Hou, R Li, C Tong - Agronomy, 2024‏ - mdpi.com
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to
continuously monitor phenology, vegetation health indices, and vegetation drought indices …

Mechanisms of climate change impacts on vegetation and prediction of changes on the Loess Plateau, China

Y Gou, Z **, P Kou, Y Tao, Q Xu, W Zhu… - Environmental Earth …, 2024‏ - Springer
Monitoring and forecasting the spatiotemporal dynamics of vegetation across the Loess
Plateau emerge as critical endeavors for environmental conservation, resource …

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

S Kartal, MC Iban, A Sekertekin - Environmental Science and Pollution …, 2024‏ - Springer
Abstract The Vegetation Health Index (VHI) is a metric used to assess the health and
condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator …

Prediction of NDVI dynamics under different ecological water supplementation scenarios based on a long short-term memory network in the Zhalong Wetland, China

W Wang, P Hu, Z Yang, J Wang, J Zhao, Q Zeng… - Journal of …, 2022‏ - Elsevier
Wetland plants are a key factor in ecosystems but are threatened by water extraction and
water resource exploitation. Ecological water supplementation is a common solution to the …

Lightweight neural network for spatiotemporal filling of data gaps in sea surface temperature images

S Baker, Z Huang, B Philippa - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Optical remotely sensed data often have data gaps due to cloud coverage, which hinders
their full potential in many environmental applications. The question of how to accurately …

[HTML][HTML] How well can we predict vegetation growth through the coming growing season?

Q Peng, X Li, R Shen, B He, X Chen, Y Peng… - Science of Remote …, 2022‏ - Elsevier
The prediction of vegetation growth under climate change has become much more important
in recent years, but is still a challenge. This study developed a machine learning method to …

Forecasting vegetation behavior based on planetscope time series data using RNN-based models

A Marsetič, U Kanjir - IEEE Journal of Selected Topics in …, 2024‏ - ieeexplore.ieee.org
Accurate vegetation behavior forecasting is essential for understanding the dynamics of
plant life in the context of climate change and other natural or human-induced disturbances …

[PDF][PDF] Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction

V Benson, C Requena Mesa, C Robin, L Alonso… - 2023‏ - pure.mpg.de
We present a novel approach for modeling vegetation response to weather in Europe as
measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus …