In times of an increasingly uncertain climate and growing demand for water resources, there is a need for assessing how aquatic systems worldwide are responding to co-occurring human and climatic disturbances. Over the past half century, remote sensing technology, high performance and cloud computing infrastructure, machine learning techniques, open data practices, and widened training in computer programming have created extraordinary opportunities to expand aquatic science research across spatial and temporal scales. Remote sensing of chlorophyll concentrations has enabled near-global monitoring of algal blooms. Data science and machine learning methods have empowered the prediction of water temperatures and water quality dynamics, while also informing management actions. Process-based models have improved our understanding of lake and reservoir mixing dynamics, which can be consequential for ecosystem regime shifts. Open data practices have increased the amount of publicly available data that many calibration and validation techniques ultimately demand. The nexus of data science, remote sensing, modeling, and open science in the aquatic sciences is a dynamic, rapidly progressing space, where novel aquatic science questions can be posed at unprecedented scales. Given these developments, there can be a lingering question of “What’s next?” To date, many aquatic remote sensing, modeling, and data science efforts have focused on measuring or predicting individual parameters or variables. While these individual efforts are herculean initiatives, a ripe frontier for this nexus is linking limnological, hydrological, oceanographic, and ecological processes and principles with remote sensing, data science, and modeling techniques to understand fundamental emergent properties of aquatic systems and inform monitoring at local-to-global and daily-to-decadal scales. Additionally, the increase of open science and data democratization brings the potential for a more diverse, inclusive, and accessible scientific community and more holistic research.; To build a conversation around multifaceted developments in remote sensing, data science, modeling, and the aquatic sciences, this session invites contributors to share how they use one or a combination of remote sensing, data science, modeling, or open science practices to expand the fields of limnology, oceanography, hydrology, or ecology. We envision this session will host a range of presentation topics, including but not limited to novel methods for atmospheric corrections, scaling of cloud and other high-volume computing environments, assessing water quantity and quality across spatial and temporal scales, quantifying long-term changes in stratification dynamics, and applying data-intensive techniques for basic and applied research questions. While submissions may stem from methodological and technical hurdles encountered in remote sensing and data science fields, we challenge submissions to focus on how their efforts de-silo the aquatic sciences from the remote sensing and data science arenas. We enthusiastically encourage submissions by early career researchers as well as by researchers from BIPOC, LGBTQIA+, and other marginalized identities. An intentional focus on research that breaks down barriers to entry for underrepresented scientists in the fields of remote sensing and aquatic data science, through open science, will yield insight into the power and potential of the next frontier in emergent properties of aquatic systems.; We welcome full-length oral presentations, 5-minute “lightning” presentations, and posters.
Lead Organizer: Michael F Meyer, U.S. Geological Survey (mfmeyer@usgs.gov)
Co-organizers:
Kate C Fickas, U.S. Geological Survey (kfickas-naleway@usgs.gov)
Robert Ladwig, University of Wisconsin - Madison (rladwig2@wisc.edu)
Rachel M Pilla, Oak Ridge National Laboratory (pillarm@ornl.gov)
Simon N Topp, U.S. Geological Survey (stopp@usgs.gov)
Presentations
10:30 AM
Surface water temperature observations and ice phenology estimations for 1.4 million global lakes (4804)
Primary Presenter: Maartje Korver, McGill University (maartjekorver@gmail.com)
Water temperature and ice cover are critical variables for the ecological, biogeochemical, and physical functioning of a lake. However, site-specific observations of water temperature and ice cover are not available for most lakes in the world. Yet this information is crucial to understanding the global role of lakes in the functioning of the bio- and hydrosphere and understanding the (projected) changes induced by global environmental change. Here, we present two datasets which include: 1) lake surface water temperature (LSWT) observations between 2013 and 2021 for ~1.4 million lakes (larger than 0.1 km2) globally, and 2) seasonal LSWT summary statistics as well as predictions of average yearly ice cover derived from dataset 1. The observations were extracted from Landsat-8 thermal radiance imageries, processed and calculated to LSWT from a center point of each lake. We used in-situ LSWT data for validation and compared our data to other satellite-derived LSWT and ice phenology datasets. All data underwent extensive quality control, based on outlier detection, overlapping imagery removal, and the removal of observations taken from dry lake beds. We used an ensemble of computational tools (e.g., Google Earth Engine, ArcGIS, R) to create these datasets, and although we believe that large-scale data retrieval methods are becoming increasingly more accessible, we also acknowledge that there is still a time and resource barrier for many interested in analyzing large-scale data. We therefore make these two datasets openly available and provide them in an analytical-friendly format. We believe that this dataset fills a crucial spatial data gap, especially for the incorporation of small(er) lakes and understudied geographies in large-scale limnological research.
10:45 AM
THE INFLUENCE OF CLIMATE CHANGE ON THE PHISICOCHEMICAL PROPERTIES OF SADO ESTUARY (PORTUGAL): LINKING IN SITU, MODEL AND SATELLITE DATA (5726)
Primary Presenter: Beatriz Biguino, Marine and Environmental Sciences Centre (MARE) - Lisbon University (bibiguino@fc.ul.pt)
It is increasingly clear that each estuarine system responds to climate change differently. In this context, the present study aims to understand how the physicochemical and biological properties of Sado Estuary, in Portugal, have changed in recent decades due to climate change and other factors. This assessment takes advantage of a rare and high-quality long-term (40 years) dataset of in situ observations that was corrected for seasonal and tidal variations with results of a water quality numerical simulation using the Delft3D suite. In addition, this analysis was complemented with remote sensing datasets to investigate the long-term patterns observed along the different types of boundaries within the system. Preliminary results show a decrease in the river flow, that could in part reflect the influence of climate change in the region. In fact, the area is expected to be increasingly affected by drought periods. Contrastingly, the river flow reduction seems to be moving the system away from scenarios of increased warming, eutrophication or increased nutrient retention, despite the warming patterns typically associated with climate change. These results demonstrate the importance of long-term regional studies for developing climate change adaptation and mitigation plans. Moreover, the results highlight the need to link data science with remote sensing and modeling to surpass the speculative process that is often associated with predicting estuary response to climate change.
11:00 AM
ASSESSING THE MONITORING OF AQUATIC HABITAT COMPLEXITY USING SIMULATED LANDSCAPES AND HIGH-RESOLUTION REMOTE SENSING (5548)
Primary Presenter: de Grandpré Arthur, Université du Québec à Trois-Rivières (arthur.de.grandpre@uqtr.ca)
During the last decade, the rapid growth of remote sensing applications has been a strong catalyst to research about the relationships between landscapes, biodiversity, and ecosystem functioning. Despite some important advances regarding the role of spatial feedbacks in structuring terrestrial ecosystems, such observations are lagging in aquatic systems because of the inherent difficulty of assessing their landscape composition and complexity. To investigate the technical capacity of high-resolution remote sensing to fill this gap, we used simulated landscapes to emulate the main sources of remote sensing noise affecting aquatic scenes at multiple sensor resolution and vegetation organisation levels. Multiple informational complexity metrics were then computed over these landscapes to study their behavior across gradients of organisation, resolution, and noise, assessing the limits of their usage in a monitoring context. To ensure the simulated landscapes were representative of natural gradients, simulated noise was also added to real high-resolution remote sensed landscapes to compare the complexity response curves. Preliminary results suggests that some metrics evaluated on very high-resolution images (<5m resolution) are less robust to environmental noise than on high or medium resolution images, while some complexity metrics such as those related to shape tend to perform well across all noise treatments. These findings represent an important step towards studying landscapes complexity changes across time and space and how it affects functioning and resilience.
11:15 AM
COMBINING FISHERIES MONITORING WITH REMOTE SENSING FOR ACHIEVING SUSTAINABILITY: FROM DATA COLLECTION TO WEB VISUALIZATION (6359)
Primary Presenter: Jordi Ribera-Altimir, Institut de Ciències del Mar (ICM - CSIC) (jribera@icm.csic.es)
Developing data-driven fisheries management strategies is essential to overcome the global decline of fishing stocks, as stated by EU Data Collection Framework. In this work, we present the tools and techniques from ICATMAR, Catalan Institute for Ocean Governance Research, for data collection, processing, analysis, publication and web visualization for bottom trawling fisheries management in the North-Western Mediterranean. Within 4 years of collected data (2019-2022), the sampling program created a dataset of over 900000 sampled specimens accounting for almost 25 tons of catch. As the combination of remote sensing data with fisheries monitoring offers new approaches for assessing the ecosystem, the collected fisheries data, which are continuous trawling samplings, daily fishing landings, and Vessel Monitoring System (VMS), are visualized in combination with georeferenced sea habitats (EMODnet), climate and sea conditions (CMEMS) on the web browser. An open website offers these data visualizations: geolocalized bottom trawling samplings adding the mentioned data sources, distribution of biomass per port or season, and length-frequency charts per species (https://icatmar.github.io/VISAP). This information system aims to fulfill the gaps in the scientific community, administration and civil society to access high-quality open data for fisheries management, following the FAIR principles, and enable answering applied research questions.
11:30 AM
Fostering data interoperability to improve analysis of the Lake Lugano ecosystem (7438)
Primary Presenter: Daniele Strigaro, SUPSI (daniele.strigaro@supsi.ch)
The lack of interoperability is due to the inability to have communication standards that can facilitate the exchange of information. The digital revolution, and in particular the digitization process we have witnessed over the last decade, is increasingly raising awareness of the importance of data interoperability, which is essential for fully exploiting data and creating new series that can foster understanding of a monitored object. In this presentation, we would like to show an implemented solution on Lake Lugano, where we developed an integrated system that uses as much open-source licensed technology as possible to integrate different data sources, including historical time series, citizen science, satellite, and in-situ sensor data. We have implemented a complete pipeline to integrate different data sources, using processes that can organize and make the time series accessible, and serve them via standard services such as the Sensor Observation Service (SOS) of the Open Geospatial Consortium (OGC). Our proposition also includes the creation of an Automatic High-Frequency Monitoring (AHFM) system built using cost-effective principles and meeting open design requirements. We apply it for the development of an algorithm to calculate the Primary Production of Lake Lugano starting from the dissolved oxygen variations. Thanks to the data integration, we could easily compare time series and make validations. Additionally, we could automatically create some basic indicators by using the historical time series collected from the traditional campaigns performed on the lake.
11:45 AM
Tracking of the floating Sargassum in the Yellow and East China Sea from satellites and modeling (5678)
Primary Presenter: YOUNG GYU PARK, Korea Institute of Ocean Science and Technology (ypark@kiost.ac.kr)
Using particle tracking modeling and satellite data, the origin and pathway of the floating Sargassum reaching southwestern part of Korea were investigated. The origin of Sargassum was estimated not only to the Zhejiang coast of the East China Sea (ECS), but also to the northern coast of the Yellow Sea (YS), which was previously unknown. In particularly, the particles of the northern coast of the YS origin showed two paths that reached Jeju Island and the southwestern part of the Korean Peninsula due to the influence of monsoon and coastal currents in winter, or moved to the ECS and reached by southerly wind in spring. The time of reaching the coast of the Korean peninsula, which varies from year to year, was greatly influenced by anomalous westerly wind in last December. In addition, the anomalous wind field from January to March also determined Sargassum distribution in spring. In relation to the growth of Sargassum, the influence of temperature and the supply of nutrients of human origin released to the coast of China were important. During the period of 2010–2020, the trend of surface temperature on the coast of China and the ECS was about 10 times higher than that of the past 30 years, and 5–6 times the global surface average. This indicates that accelerated algae growth and warming of coastal and sea areas are related, and continuous monitoring is necessary.
SS012B The Next Frontier: Linking Remote Sensing, Data Science, Modeling, Open Science, and the Aquatic Sciences To Understand Emergent Properties of Aquatic Systems
Description
Time: 10:30 AM
Date: 8/6/2023
Room: Sala Menorca B