Assessing Water Quality and Vegetation Changes under Changing Climate Using Machine Learning and High-Resolution UAV Imagery
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 2010
Special Issue Editors
Interests: UAV remote sensing; machine learning; deep learning; phenology extraction; yield prediction; data fusion
Special Issues, Collections and Topics in MDPI journals
Interests: GNSS data processing and application
Special Issues, Collections and Topics in MDPI journals
Interests: DSSAT model; wheat; genetic parameters; global sensitivity analysis; time series
Special Issue Information
Dear Colleagues,
Global warming has been an ongoing issue for decades, and it has profoundly influenced water quality and vegetation growth. Timely prediction of water quality and variation of vegetation is of great importance to society. With the quick development of computation speed, machine-learning-based data analysis has grown in popularity. The purpose of this Special Issue is to present new research advances on the applications of remote sensing techniques, such as multi-/hyperspectral satellites and UAVs, to monitor changes in water quality and vegetation growth under a changing climate. Contributions focusing on new methods and applications in assessing water quality, vegetation growth monitoring—in particular, new approaches and novel contributions using machine learning—and deep learning methods, specifically studies based on multispectral and hyperspectral from multiple platforms, are welcome. The scope of this Special Issue includes but is not limited to the following:
- Water quality monitoring using multispectral and hyperspectral images;
- Mapping vegetation phenology;
- Vegetation growth monitoring;
- Time-series analysis monitoring of agriculture and forest;
- High-throughput phenomics.
Dr. Yahui Guo
Dr. Shunqiang Hu
Dr. Haijiao Ma
Guest Editors
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Keywords
- water quality monitoring using remote sensing and machine learning
- climate change impacts on vegetation growth
- data analysis using machine learning and deep learning
- data fusion
- time-series analysis
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