Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change II

Dear Colleagues,

Climate change, especially extreme climate events such as drought and heat waves, have profoundly influenced the terrestrial water cycle and vegetation growth and subsequently also affected fluvial geomorphology patterns and carbon and energy balance, as well as water safety and food security. Understanding the extent of the hydrology and vegetation response to the ongoing climate change and investigating the mechanisms behind these changes will not only help to fight the negative effects of climate change but also to provide effective adaptive measures. Therefore, it is essential to explore the changes in hydrology and vegetation under climate change at a basin and regional scale—even at a global scale. With the development of high-resolution satellites and unmanned aerial vehicles (UAVs), the capacity of remote sensing to monitor the changes of hydrology and vegetation have been significantly improved.

The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques, such as multi/hyper-spectral light detection and ranging (LiDAR) from satellites and UAVs, for monitoring the changes of hydrology and vegetation under climate change. The contributions focusing on applications in hydrology and vegetation, both algorithmic and methodological. In particular, new approaches and novel contributions, such as the fusion method, knowledge extraction and machine learning and deep learning methods, are preferred. Studies based on multi-spectral and hyper-spectral LiDAR data from UAV platforms will be especially welcome.

This Special Issue of Remote Sensing calls for papers related to new technological advancements in the application of remote sensing techniques in the domains of hydrology and vegetation. The following topics are suggested:

• Hydrology and vegetation mapping and change detection (multi/hyper-spectral LiDAR);
• Vegetation response to extreme drought;
• Water quality monitoring (multi/hyper-spectra, RS);
• Vegetation health monitoring;
• Phenotyping estimation and disease detection of forest;
• Time-series analysis monitoring for agriculture and forest;
• Machine learning and deep learning;
• Novel methods for phenotyping from UAV imagery (e.g., leaf nitrogen, leaf area index or biomass);
• Reconstruction of forest structures using LiDAR;

Fluvial network topology and its climatic dependence.

Prof. Dr. Yongshuo Fu
Dr. Xuan Zhang
Dr. Senthilnath Jayavelu
Dr. Shengli Tao
Dr. Xuesong Zhang
Guest Editors

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• hydrology and ecohydrology
• water cycle
• UAV remote sensing
• forest ecology
• phenology extraction
• yield prediction
• climate dynamics
• vegetation dynamic
• modeling climate change
• machine learning and deep learning
• river basin geometry and topology