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
06:30 PM
Remote sensing phytoplankton size classes in the Eastern Mediterranean Sea (6153)
Primary Presenter: John Gittings, National & Kapodistrian University of Athens (jagittings@biol.uoa.gr)
The surface and deeper layers of the Mediterranean have exhibited unprecedented rates of warming since the mid-1980’s, primarily due to climate change. Recent projections have predicted that the Mediterranean Sea will be one of the regions most impacted by increasing global temperatures, and thus may serve as a “laboratory” for what can be expected in other areas of the global oceans under warmer climate scenarios. Metrics that characterise phytoplankton abundance, phenology (bloom timing) and size structure can be utilised as ecological indicators that enable a quantitative assessment of the status of the Mediterranean Sea, in response to warmer climate scenarios. Here, we use an abundance-based size phytoplankton size class model and apply it to remotely sensed Chl-a observations to infer Chl-a in three phytoplankton size classes (PSCs), pico- (< 2μm), nano- (2-20μm) and micro-phytoplankton (> 20μm). Model re-parameterisation is achieved using in situ High Performance Liquid Chromatography (HPLC) algal pigment datasets obtained during several research cruises conducted in the Eastern Mediterranean. We subsequently investigate the interannual variability of phenology metrics associated with each independent phytoplankton size class. Our results may ultimately be relevant for under-standing trophic linkages between phytoplankton size structure and fisheries, as well as the development of marine management strategies.
06:30 PM
TIAMAT: a marine observatory for climate change in the Spanish National Parks (5414)
Primary Presenter: Melanie Juza, SOCIB (mjuza@socib.es)
The TIAMAT Observatory facilitates the continuous monitoring of the marine environment in the Spanish National Park network with natural marine systems (Cabrera Archipelago in the Mediterranean Sea, Atlantic Islands and Doñana along the Atlantic coast) in the context of climate change. This marine observatory provides continuous open access to information on the ocean state and variability, through the monitoring and visualization of essential ocean variables and indicators (e.g. sea surface temperature, chlorophyll-a concentration, ocean currents or sea level, ocean surface stress), from daily to interannual and decadal scales. Such an observatory is enabled by the Copernicus Marine Service which provides free and open access to quality-controlled historical and near real-time ocean data, in particular from non-intrusive and regular satellite remote sensing data available since 1982. This web-based application enables the detection of extreme events (e.g. marine heat waves) in real-time, as well as the monitoring of long-term ocean variations, in response to climate change. The TIAMAT observatory will be presented for the Cabrera National Park in the Balearic Islands. This observatory aims to be an open science-based management tool to support decision-making in the National Parks and marine protected areas from regional to international levels. Finally, through its dissemination strategy, the TIAMAT project will raise awareness about the climate challenges that the National Parks are facing to foster their conservation.
06:30 PM
Correct selection of remote sensing product is crucial in freshwater science (5522)
Primary Presenter: Igor Ogashawara, Leibnitz Institute of Freshwater Ecology and Inland Fisheries (igoroga@gmail.com)
There is a growing number of lake studies using remote sensing technology for inland aquatic systems. In particular, satellite imagery from newly developed sensors allows the retrieval of several water quality parameters across the surfaces of an increasing number of smaller lakes, providing not only the surface area but additional biogeochemical and biological data. Correctly used, remote sensing technologies enable spatio-temporal analysis of dynamic developments in a large number of water bodies to detect long-term trends and identify sudden changes within aquatic environments as well as their catchments. Notably, however, in most limnological studies, only limited attention has been paid to the differences between and peculiarities of the various remote sensing methods and products available. Indeed, a profound understanding of remote sensing methods for inland waters is necessary for their correct application and data interpretation. Due to the optical complexity, biogeochemical and biological variability of freshwater ecosystems, common remote sensing products made for terrestrial or oceanic applications are not accurately applicable in inland waters. Fortunately, given the growing interest in remote sensing of inland waters, capacity-building strategies have been discussed to overcome these knowledge gaps. In this context, it became clear that synergies between remote sensing and limnology communities need to be developed, with the aim to overcome methodological limitations and improve our ability to accurately monitor our rapidly changing inland waters.
06:30 PM
A game of drones: Advancing discovery and innovation in intertidal research (6177)
Primary Presenter: Corey Garza, California State University, Monterey Bay (cogarza@csumb.edu)
The rocky intertidal is one of the most heavily studied marine habitats in the world owing to its economic and ecological importance. Traditional monitoring methods, such as quadrats and meter tapes, have historically been employed to collect data on trends and status in this important habitat. However, these approaches come with limitations centred about the spatial and temporal scales across which they can be used. Aerial drones have seen increased use in coastal studies due to their ability to rapidly capture broad-scale data on the distribution and abundance of coastal resources at a relatively low cost. Drones now provide a tool for capturing broad-scale data on intertidal habitat distribution and community composition at scales of a few centimeters up to hundreds of meters. Beyond their capacity to capture high resolution image data, drones come with other advantages that provide scientists with new ways to study coastal habitats across multiple spatial and temporal scales. These include but are not limited to (1) carrying various imaging (e.g., multispectral cameras) payloads; (2) increased frequency in survey intervals; (3) low altitude, autonomous flight that allows sensors to collect fine spatial resolution data; and (4) low operating costs. In this talk I describe approaches for using drones to rapidly collect and then analyze intertidal data via automated image classification. Drones can ultimately help support the emerging needs of 21st century rocky intertidal research.
06:30 PM
Reconstruction of Sub-Surface Ocean State Variables using Convolutional Neural Network (7110)
Primary Presenter: Philip Smith, Technical University of Denmark (pahsm@aqua.dtu.dk)
Accurate monitoring of regional and global ocean processes is crucial for a full understanding of changes in the ocean circulations and for an assessment of critical impacts of anthropogenic pressures on climate and marine ecosystems. Therefore, it is important to determine the distributions of essential ocean variables (EOVs), like temperature and salinity, as well as their interactions over a wide range of spatial and temporal scales both in horizontal and vertical dimensions. To achieve reliable ocean observations, in situ and remote sensing platforms have been developed collecting EOVs data at great resolution, and to enable process-based modelling efforts of ocean dynamics. However, the distribution of subsurface ocean measurements is extremely sparse and irregular, which presents a challenge for gaining a comprehensive understanding of the ocean and marine ecosystems. To overcome the limits of sparse temporal and spatial observations, neural networks combining remotely-sensed surface measurements and in situ vertical profiles are increasingly being used to obtain high-quality three-dimensional estimates of subsurface ocean state variables. This study proposes a convolutional neural network (CNN) for reconstruction of vertical profiles using satellite surface measurements as input collected between 2005 and 2020 in the Atlantic Ocean, and its performance is shown to be superior to current state-of-the-art methods. Different combinations of surface variables are analyzed and compared to determine the key surface variables for ocean structure reconstruction. Furthermore, the relative importance of each of these variables is estimated over the full vertical profiles.
06:30 PM
Distribution and impacts of long-lasting marine heat waves on phytoplankton biomass (7127)
Primary Presenter: Anshul Chauhan, Technical University of Denmark (DTU) (anscha@aqua.dtu.dk)
Irregular, abrupt but persistent changes in sea surface temperature (SST) have been noticed in recent years, with cascading effects on different components of marine ecosystems. Warm temperature anomalies are increasing in frequency in the global ocean with potential consequences on the goods and services provided by marine ecosystems. Recent studies have analysed the distribution and dynamics of marine heat waves (MHWs) and evaluated their impacts on marine habitats. Different drivers can generate those anomalies and the emerging attributes can vary significantly both in space and time, with potentially different effects on marine biology. In this paper we classify MHWs based on their attributes and using different baselines, to account for different adaptive responses in phytoplankton dynamics. Specifically, we evaluate the impacts of the most extreme, long-lasting and high-intensity MHWs on phytoplankton communities using remote sensing data. We demonstrate marginal impacts on total chlorophyll concentrations which can be different across different ocean regions. These contrasting effects on phytoplankton dynamics are most likely the results of the different mechanisms generating the MHWs in the first place, including changes in front dynamics, shallower mixed layers, and eddy dynamics. We conclude that those drivers can also induce different phytoplankton responses across the global ocean.
06:30 PM
Application of deep learning to the macro beach litter quantification and the training dataset publication (7278)
Primary Presenter: Mitsuko Hidaka, Japan Agency for Marine-Earth Science and Technology (JAMSTEC) (mitsukou@jamstec.go.jp)
Beaches are places where human living areas, and a variety of litter are washed ashore from the river and offshore. Plastic litter comprises 70-90% of beached litter, and they are thought to be fractured into microplastics by the effect of ultraviolet, winds, and waves. Automated analysis methods for the beach images taken by multi different types of cameras (e.g. aerial drones, webcams, and smartphones) have the possibilities to provide alternative monitoring methods to relatively laborious approaches such as a manual collection and counting of the litter and help us to accelerate to understand the real state of the pollution. As the first step to establishing the method to estimate the total amount of macro plastic litter on the beach, we developed a deep learning model which gives pixel-level image classification to the beach images into eight classes, including artificial litter and natural litter classes. The accuracy of the model was evaluated qualitatively and quantitatively. The detection accuracy of the model for artificial litter on the test data was around 80%, and the usefulness of the method was demonstrated by comparing the results by projection transform from the ground image inference, and drone image ground truth results. Moreover, now we are attempting to develop a technology to classify macro plastic objects in detailed classes. The training dataset used in this research is now public from SEANOE - Sea Open Scientific Data Publication. We also produced a beach plastic litter training dataset for machine learning use.
06:30 PM
Storm Gloria in the Ebro Delta (Western Mediterranean): Landsat-8 and Sentinel-2 satellites to monitor its impact (5472)
Primary Presenter: Isabel Caballero, Spanish National Research Council (CSIC) (isabel.caballero@icman.csic.es)
Coastal hazards and extreme events are increasing in frequency due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The Ebro Delta ecosystem, located in the NW Mediterranean, was subject to storm Gloria in the winter of 2020, the most severe coastal storm registered in the area in decades. In this study, Landsat-8 and Sentinel-2 satellites are used to monitor flooding impact and water quality status, including chlorophyll-a, suspended particulate matter, and turbidity to evaluate pre-, syn-, and post-storm scenarios. Image processing is carried out using the ACOLITE software and the on-the-cloud Google Earth Engine platform for water quality and flood mapping, respectively, showing a consistent performance for both satellites. This cost-effective methodology allows us to characterize the main water quality variations in the coastal environment during the storm. Moreover, the time series reveals how the detrimental impact on turbidity conditions was restored two weeks after the storm. These results, obtained within the H2020 EuroSea project, demonstrate that the used workflow using open satellite imagery at 10-30 m spatial resolution is suitable for monitoring the delta, thus providing valuable information for early warning to facilitate timely assistance and hazard impact evaluation. The integration of high-resolution remote sensing tools into ecological disaster management can significantly improve current monitoring strategies, supporting decision-makers from the local to the national level in prevention, adaptation measures, and damage compensation.
06:30 PM
PATTERNS AND TRENDS IN CHLOROPHYLL-A CONCENTRATION AND PHYTOPLANKTON PHENOLOGY IN THE BIOGEOGRAPHICAL REGIONS OF SOUTHWESTERN ATLANTIC OCEAN (5911)
Primary Presenter: Ana Delgado, CONSEJO NACIONAL DE INVESTIGACIONES CIENTÍFICAS Y TÉCNICAS (CONICET) (delgadoanalau@gmail.com)
The Southwestern Atlantic Ocean (SAO), is considered as one of the most productive areas of the world, with high abundance of ecologically and economical important fish species. Yet, the biological responses of this complex region to climate variability are still uncertain. Here, using 24 years of satellite derived Chl-a datasets, we classified the SAO into coherent regions based on homogeneous temporal variability of Chl-a concentration, as revealed by the Self-Organizing Maps analysis. These coherent biogeographical regions were the basis of our regional trend analysis in phytoplankton biomass, regional phenological indices, and environmental forcing variations. A generalized positive trend in phytoplankton concentration is observed, especially in the highly productive areas of the northern shelf-break, where phytoplankton biomass is increasing at an outstanding rate up to 0.42 ± 0.04 mg m-3 per decade associated with the SST warming (0.11 ± 0.02 °C decade-1) and the MLD shoaling (-3.36 ± 0.13 m decade-1). In addition to the generalized increase in Chl-a, in the northern inner shelf waters and the Patagonian temperate fronts, a significant positive trend in the start of the autumn bloom of 15 ± 3 and 24 ± 6 days decade-1, respectively, was observed, which might be explained by the significant warming trend of SST, which would sustain the water stratification for a longer period, thus delaying the secondary bloom initialization. Consistent with previous studies, our results provided further evidences of the impact of climate change in these highly productive waters.
06:30 PM
SYNERGY BETWEEN HIGH-FREQUENCY SENSOR NETWORKS AND HARMFUL ALGAL BLOOM MANAGEMENT TACTICS (6104)
Primary Presenter: Olivia Trombley, Dickinson College (livtrombley@gmail.com)
Harmful algal blooms (HABs) are an ongoing challenge for those who manage freshwater reservoirs. The toxins produced by HABs impact a wide range of activities, including recreation, drinking water treatment, and overall ecosystem health. We’ve established a data-rich network across Pennsylvania, USA that involves state agency officials, lake managers, dam operators, drinking water utilities, and an academic institution. This public/private collaboration uses high-frequency sensors, detailed monitoring efforts across agencies, and modeling to strengthen the ability to detect HABs and toxins, establish an understanding of HAB drivers, and prepare guidance for managing this threat. Findings from this network suggest that high-frequency sensor networks are important components for supporting real-time management decision making, especially for reservoirs capable of altering hydrodynamic conditions. A combination of hypolimnetic withdrawals and surface flushing were able to temporarily relieve high temperature stress and low dissolved oxygen conditions. However, chlorophyll and phycocyanin sensors had weak correlations with algal biomass and toxin concentrations measured in bi-weekly monitoring. Largely reactionary monitoring programs based on HAB visual cues missed a substantial number of high toxin events that exceeded recreational and drinking water recommendations. Insights from this network have been key to developing water management strategies for HABs across the state, and would not be possible without close collaborations among a broad array of stakeholders.
06:30 PM
A low-cost, open-source, wireless multi-sensor system for monitoring hydrological and biogeochemical dynamics across land-stream interfaces (6794)
Primary Presenter: Antoine Wiedmer, CREAF | Ecological and Forestry Applications Research Centre (a.wiedmer@creaf.uab.cat)
The recognition of global change impacts on catchments and the waters they drain emphasizes the need to better understand and predict hydrological and biogeochemical dynamics across terrestrial-aquatic interfaces. To achieve this great endeavor, a key priority is to substantially increase the number of multi-annual time series, covering a broad range of river ecosystems and filling existing geographical gaps (e.g., low-income regions in/and the Global South). However, commercial sensors solutions are not affordable to everyone. Aiming to overcome this financial challenge we have designed and optimized a low cost autonomous multi-sensor system for monitoring hydrological and biogeochemical dynamics in soil and fluvial ecosystems. The system consists of a STM32 micro-controller board combined with a data logger module, and a set of sensors to measure hydro-chemical properties both at different depths in soil and within streams: temperature, water level, moisture, electrical conductivity, dissolved O2 and CO2. The monitoring system also integrates a wireless communication capability using WIFI or LoRa network technologies. To make our project as accessible as possible, we have designed, build and program the multi-sensor adopting the Open Source Hardware and Software philosophy. Through the complete processes of pre-calibration and in situ measurement, the preliminary results illustrate that the proposed multi-sensor system can provide long-term, high-frequency hydrological and biogeochemical data across land-stream interfaces, while keeping the balance of costs and accuracy.
06:30 PM
Characterizing Low-Lying Coastal Upland Forests to Predict Future Landward Marsh Expansion using Terrestrial Laser Scanning (6797)
Primary Presenter: Elisabeth Powell, University of Maryland (epowell1@terpmail.umd.edu)
Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that in many coastal regions, SLR scenarios may exceed the threshold for elevation gain primarily through vertical accretion. Therefore, research has focused on mapping the landward expansion of marshes, as it is a vital process for estimating future wetland resilience to accelerated SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in-situ data and effective mapping strategies for delineating the boundaries, or ‘ecotones’, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from Light detection and ranging (Lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we characterized 3 coastal upland forests using terrestrial laser scanning (TLS) along a narrow elevation gradient in the Delaware Bay estuary that is particularly vulnerable to SLR. We analyzed the structural dynamics across forest edge-to-interior transects and utilized the comprehensive 3D data obtained from TLS to determine how elevation (i.e., inundation proxy) influenced the vertical stratification and other forest structural characteristics that may be consistently impacted by inundation at low lying elevations. Our findings revealed a consistent pattern between elevation and the Plant Area Index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration.
SS012P The Next Frontier: Linking Remote Sensing, Data Science, Modeling, Open Science, and the Aquatic Sciences To Understand Emergent Properties of Aquatic Systems
Description
Time: 6:30 PM
Date: 8/6/2023
Room: Mezzanine