Wetland ecosystems provide essential services for the subsistence of life on Earth; however, these ecosystems face constant external threats that affect and change their natural processes and dynamics.
Significant knowledge gaps exist on multiple aspects, components, and interactions of wetlands worldwide. Multitemporal earth observations (using passive and active sensors) offer an excellent opportunity to address these knowledge gaps and are sometimes the only source of information in remote and non-instrumented areas.
This session focuses on studies that use multitemporal earth observation data to understand different processes and components (e.g., water dynamics, vegetation changes, disturbances, soil moisture, biodiversity) of wetland ecosystems (e.g., marshes, swamps, fens, bogs, peatlands, lakes, ponds) with different regimes (e.g., permanent, temporary), and support the development of new applications.
This session also encourages but is not limited to studies using multi-sensors and machine learning technologies that provide solutions for wetland monitoring, conservation, and restoration.
Significant knowledge gaps exist on multiple aspects, components, and interactions of wetlands worldwide. Multitemporal earth observations (using passive and active sensors) offer an excellent opportunity to address these knowledge gaps and are sometimes the only source of information in remote and non-instrumented areas.
This session focuses on studies that use multitemporal earth observation data to understand different processes and components (e.g., water dynamics, vegetation changes, disturbances, soil moisture, biodiversity) of wetland ecosystems (e.g., marshes, swamps, fens, bogs, peatlands, lakes, ponds) with different regimes (e.g., permanent, temporary), and support the development of new applications.
This session also encourages but is not limited to studies using multi-sensors and machine learning technologies that provide solutions for wetland monitoring, conservation, and restoration.