About this Research Topic
Wetlands cover only 5-8% of the terrestrial land surface but provide valuable ecosystem services for human society and habitats for various plants and animals. They are a major natural source of methane (CH4) and contribute 30-40% to the total CH4 emissions, leading to a warmer climate. Wetlands have also been experiencing rapid loss due to many factors, e.g., agriculturalization, urbanization, and climate change from local to global scales. Due to the complex and varied environment of wetland ecosystems, there exist great challenges in accurately characterizing the wetland landscape. Therefore, efficient and timely approaches for wetland characterization are essential for quantifying changes due to climate change and human activity and are of great importance for sustainable management and resource assessment.
Remote Sensing provides efficient tools and advantages in characterizing and monitoring the status and functioning of wetlands, particular for many inaccessible wetland regions. There has been an exponential increase in using optical, synthetic aperture radar (SAR), and light detection and ranging (lidar) sensors to inform and advance knowledge about wetlands. The fine resolution remote sensing data (e.g., Landsat, Sentinel, and RADARSAT) have become widely available and have a considerably high revisit frequency, which provides a promising opportunity for wetland mapping and analysis at multiple spatiotemporal scales. Meanwhile, with the development of computer science, new approaches (e.g., machine learning and deep learning) combined with high-performance computers and computing platforms have advanced the techniques for wetland mapping and monitoring from remote sensing for a variety of applications. We wish to capture these state-of-the-art advances in mapping and monitoring of wetlands and hydrologic features using remote sensing data at fine to coarse scales.
This Research Topic would welcome recent approaches development and success in operational wetland mapping and monitoring using remote sensing technology. Topics may include, but are not limited to:
• Utilization of multi-source remote sensing data, including optical, lidar, and SAR, etc., for wetland mapping and monitoring
• Advanced machine learning/deep learning approaches for wetland characterization
• Wetland change detection (seasonal, interannual, etc.)
• Wetland type/species classification and mapping
• Wetland mapping and monitoring using big data, high-performance computers, and cloud-computing platforms
• Analysis of natural or anthropogenic drivers, such as climate change and human activities
Remote Sensing provides efficient tools and advantages in characterizing and monitoring the status and functioning of wetlands, particular for many inaccessible wetland regions. There has been an exponential increase in using optical, synthetic aperture radar (SAR), and light detection and ranging (lidar) sensors to inform and advance knowledge about wetlands. The fine resolution remote sensing data (e.g., Landsat, Sentinel, and RADARSAT) have become widely available and have a considerably high revisit frequency, which provides a promising opportunity for wetland mapping and analysis at multiple spatiotemporal scales. Meanwhile, with the development of computer science, new approaches (e.g., machine learning and deep learning) combined with high-performance computers and computing platforms have advanced the techniques for wetland mapping and monitoring from remote sensing for a variety of applications. We wish to capture these state-of-the-art advances in mapping and monitoring of wetlands and hydrologic features using remote sensing data at fine to coarse scales.
This Research Topic would welcome recent approaches development and success in operational wetland mapping and monitoring using remote sensing technology. Topics may include, but are not limited to:
• Utilization of multi-source remote sensing data, including optical, lidar, and SAR, etc., for wetland mapping and monitoring
• Advanced machine learning/deep learning approaches for wetland characterization
• Wetland change detection (seasonal, interannual, etc.)
• Wetland type/species classification and mapping
• Wetland mapping and monitoring using big data, high-performance computers, and cloud-computing platforms
• Analysis of natural or anthropogenic drivers, such as climate change and human activities
Keywords: Remote sensing, wetland mapping, wetland change analysis, machine learning, deep learning, big data, climate change
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