Satellite & Climate ecosystems
Chair: Rodrigo Abarca del Rio
Co-chairs:
Dr. Chaitanya B. Pande: Indian Institute of Tropical Meteorology, Pune, India
Dr. Kanak N. Moharir. Assistant Professor, Department of Remote Sensing, Banasthali Vidyapeeth University, Rajasthan, India
This track aims at gathering scientists and practitioners interested in Climate ecosystems from the point of view of Complex Systems.
New satellite missions, instrumentation, and global/regional models have generated an unprecedented data source (BigData). The combination of increased computational power, the development of cloud computing (in platforms such as the Google Earth engine), and recent advances in statistical modeling and artificial intelligence (AI, particularly in machine learning (ML)) offer promising new opportunities to expand our knowledge of the Earth system from data. There are many tools available in the fields of ML and AI which have been consistently developed and adapted to geoscientific analysis. These have improved understanding and forecasting and promoted new ways of understanding geoscientific processes and the various components of the Earth’s ecosystem (atmosphere, land surface, and ocean), interactivity, and nonlinearity. AI and ML have improved weather warnings, especially extreme events, by identifying teleconnections and climate networks, while Google Earth Engine has transformed satellite data processing.
Earth system science offers new opportunities, challenges, and methodological demands, particularly for recent lines of research focused on the spatio-temporal context. Thus, many anthropogenic dangers’ trends and variability cannot be fully explained. Numerous natural or anthropogenic climate fluctuations affect climate ecosystems, including hydrological basins and coastal areas. For example, the El Niño-Southern Oscillation (ENSO), one of the main drivers of climate variability, affects air temperature, precipitation, wave, and sea level extremes, as well as the availability of water on which the food of hundreds of millions of people depends, remains challenging to predict. Even more direct anthropogenic consequences, such as air pollution in cities, water contamination in rivers, lakes, and coastal areas, or direct human intervention linked with sudden changes in land use or the indiscriminate exploitation of groundwater over the past several decades, are also significant. Understanding the ecological dynamics of these climate impacts, hotspots of sensitivity and resilience, and management measures that can help the biosphere adapt and reduce climate change are needed.
Closed research fields rarely overlap. This is not the case when applying new ML methods, allowing field-to-field transitions. Thus, we want to bring together researchers in climate ecosystems, particularly satellite remote sensing, and even more in the observation and modeling of hydrology and climate variability, which have employed creative ways of understanding or predicting the complexity of these systems. The convergence of specialists from many fields provides a unique opportunity to understand better how natural and anthropogenic climate change affects climate ecosystems and their networks and how to best respond to them using new ML techniques or other statistical methodologies. Thus, this session brings together scientists from various fields to demonstrate their practical numerical and statistical methods, ML, and other techniques to understand the current state of the many compartments of the earth system.
Abstract submission of oral presentations or posters are encouraged on topics including (but not limited to) : Artificial intelligence, Machine learning, Applications of Machine Learning, Deep Learning, Interpretability, Genetic Programming, Clustering; Time Series Analysis, Random forest, forecasting, prediction, AI/ML applications in Earth Science, Networks, climate networks, complex network, complex systems, network models, Big data, Methodology, Validation, Data-driven modeling, Ecosystem services, ecosystem ecology, ecosystem monitoring, environmental science, Nature-based solutions, Resilience, Climate change impacts, Contamination, Extremes, extreme weather, drought, forecasting, modeling, software, limnology, ecology, environmental science, water resources, waves, satellite remote sensing, land cover change.
This session will be primarily online and will not be immediately tied to proceedings; therefore, the submission deadline for this session is March 5, 2023. This session requires only a business-as-usual abstract of no more than 500 words, which should be specified for an oral or poster presentation.
As this is an online session, the poster session will be a typical interaction session in which the various posters will be presented sequentially by the conveners. Then anyone will be able to interact alone or interactively with each speaker. The concept is simply that we may all benefit from the provided techniques and methodologies.
However, if someone would like to take advantage of the ability to publish in the proceedings, he/she can follow the procedure and deadline outlined below.
English