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NASA NTRS2022

Grand Valley Ecological Forecasting II: Forecasting Trends in Pinyon-Juniper and Sagebrush Habitat Relative to Wildfire, Drought, Beetle Disturbance, and Treatment Impact for Management Planning

Disturbances and landcover change in pinyon-juniper and sagebrush ecosystems are exacerbated by environmental conditions such as variability in climate characteristics. Our DEVELOP team partnered with the National Park Service (NPS) in Colorado National Monument and the Bureau of Land Management (BLM)in McInnis Canyons and Dominguez-Escalante National Conservation Areas to investigate disturbances to land cover. NPS partners were interested in identifying areas at risk of pinyon-juniper die-off or encroachment by invasive species. The BLM partners prioritized identifying areas suitable for fire reduction/prevention treatment. To address these concerns, we forecasted landcover change in the Grand Valley region of Colorado using NASA Earth observation data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua. We collected and analyzed these data in conjunction with Term I of this project. We found that the primary drivers of forecasted landcover change in the study area were aspect and elevation. Our forecasted landcover change maps, created using the Idrisi TerrSet Land Change Modeler, addressed the needs of our partners by showing potential habitat suitability trends, which will inform management planning. Forecasted landcover maps indicated that by 2040, ecosystems within partner management areas will likely see tree encroachment on shrublands.

William Curtiss↗
NASA NTRSDate not supplied

Chesapeake Bay Water Resources: Examining Turbidity and Sediment Dynamics in the Chesapeake Bay

An increase in total suspended sediment (TSS) concentrations and turbidity have contributed to poor water quality in the Chesapeake Bay since the 1970s. Although turbidity and TSS have been moderately improving over the past few decades, poor water quality is detrimental to the Chesapeake Bay’s ecosystems and the surrounding watersheds. The Summer 2022 Chesapeake Bay Water Resources project observed sediment dynamics and turbidity in the York River watershed using remote sensing tools, models, and Earth observations including NASA and United States Geological Survey’s (USGS) Landsat satellite series, NASA DEVELOP’s Optical Reef Coastal Area Assessment Tool 2.0 (ORCAA), and Soil and Water Assessment Tool (SWAT). The collaborators on this project were the Chesapeake Bay National Estuarine Research Reserve (CBNERR), Group on Earth Observations (GEO) AquaWatch, the Committee on Earth Observation Satellites Coastal Observations, Applications, Services and Tools (CEOS COAST), and the Virginia Department of Environmental Quality (VA DEQ). The team concluded that the upstream sections of the York River watershed increased in TSS from 2009–2019. However, the seasonal TSS patterns often correlated with higher precipitation levels, although not necessarily major storm events. This may be due to factors like wind and waves contributing to the sedimentation trends observed. Although projects have been conducted to improve water quality, additional efforts like planting riparian buffers along areas with high TSS are needed to reduce the intensity of runoff. The team’s results will allow the end user, the VA DEQ, to inform their policymaking regarding future Bay conservation efforts.

Katherine Hahn↗
NASA NTRS2022

Chesapeake Bay Water Resources: Characterization of Sediment Dynamics for Enhanced Water Quality Monitoring in the Chesapeake Bay

An increase in total suspended sediment (TSS) concentrations and turbidity have contributed to poor water quality in the Chesapeake Bay since the 1970s. Although turbidity and TSS have been moderately improving over the past few decades, poor water quality is detrimental to the Chesapeake Bay’s ecosystems and the surrounding watersheds. The Summer 2022 Chesapeake Bay Water Resources project observed sediment dynamics and turbidity in the York River watershed using remote sensing tools, models, and Earth observations including NASA and United States Geological Survey’s (USGS) Landsat satellite series, NASA DEVELOP’s Optical Reef Coastal Area Assessment Tool 2.0 (ORCAA), and Soil and Water Assessment Tool (SWAT). The collaborators on this project were the Chesapeake Bay National Estuarine Research Reserve (CBNERR), Group on Earth Observations (GEO) AquaWatch, the Committee on Earth Observation Satellites Coastal Observations, Applications, Services and Tools (CEOS COAST), and the Virginia Department of Environmental Quality (VA DEQ). The team concluded that the upstream sections of the York River watershed increased in TSS from 2009–2019. However, the seasonal TSS patterns often correlated with higher precipitation levels, although not necessarily major storm events. This may be due to factors like wind and waves contributing to the sedimentation trends observed. Although projects have been conducted to improve water quality, additional efforts like planting riparian buffers along areas with high TSS are needed to reduce the intensity of runoff. The team’s results will allow the end user, the VA DEQ, to inform their policymaking regarding future Bay conservation efforts.

Katherine Hahn↗
NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗
NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗
NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗
NASA NTRS2021

Delaware Ecological Forecasting: Assessing Land Cover and Soil to Identify Suitable Sites for Tidal Marsh Migration in Delaware

Tidal wetlands provide vital resources for the state of Delaware, crucial not only for maintaining important ecosystem functions, but also for providing human populations with substantial services. Healthy wetland networks offer protection from severe weather, reduce flooding, improve water quality, and provide opportunities for education and recreation. However, human activities in combination with natural events, continue to cause substantial loss of wetland cover and damage wetland health. Over the last thirty years, the state of Delaware has experienced a net loss of roughly 5,000 acres of wetland. In collaboration with the Delaware Department of Natural Resources and Environmental Control (DNREC), the team used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to develop a methodology to monitor recent changes in wetland cover and forecast landward marsh migration due to sea-level rise, changes to climate, and human development. Trend analysis of current and past climate conditions in precipitation and temperature revealed an overall increase in both metrics. Using Land Change Modeler in TerrSet and Suitability Modeler in ArcGIS Pro, the team visualized landcover shifts over the last 20 years, indicating a general pattern of net wetland loss and identified locations where marsh migration could potentially occur in the future. These observations will enable better planning for restoration activities and in form decision-making to preserve wetland health and ecosystem functions.

McKenna Brahler↗
NASA NTRS2019

2018 NISAR Applications Workshop: Wetlands; Workshop Report

Wetland ecosystems are a critical part of our natural environment, providing socioeconomic benefits to human communities and habitats to a rich diversity of plant and animal life. Socioeconomic benefits include improved water quality, flood control, foods, shoreline stabilization, groundwater recharge, and recreational opportunities. Wetlands also have a major role as carbon sinks and sources through processes that are influenced by the duration and timing of soil saturation and inundation. Thus, carbon and water cycle models must take into account wetland extent and seasonal patterns of wetland inundation. The joint NASA, US Geological Survey (USGS) and Fish and Wildlife Service (FWS) workshop focused on advancing wetland applications of the spaceborne NASA-ISRO Synthetic Aperture Radar (SAR) mission (NISAR), a jointly developed satellite between NASA and the Indian Space Research Organisation (ISRO) expected for launch early 2022. Participants from 15 national and international organizations --including US Federal Agencies, nonprofits, academics, and the private sector-- had been identified as key-players in facilitating integration of Earth Observations into decision support workflows. Discussions were held over two and a half days to convey the knowledge and measurement needs of the wetlands community and discuss the delivery of relevant geospatial products that could be derived from NISAR data. While the community typically characterizes wetlands by their hydrological process, vegetation and soil types, a central defining characteristic is that a wetland is a land area inundated or saturated in the rootzone for at least 2 weeks of the average vegetation growing season.

FWS↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Technical Paper↗
NASA NTRS2023

Southern Idaho Ecological Conservation: Investigating the Impact of Targeted Grazing to Improve Wetland Habitat in the Sterling Wildlife Management Area

Wetland ecosystems are vital for biodiversity conservation and ecosystem services. The Sterling Wildlife Management Area in Bingham County, Idaho, has management concerns about decadent and accumulated vegetation growth encroaching on wetland habitat, which presents challenges for wildlife, decreases biodiversity, and limits public access. Targeted grazing has been proposed as a sustainable alternative to chemical herbicides or burning. Land managers introduced targeted cattle grazing in January 2021 to reduce biomass. NASA DEVELOP partnered with the Idaho Department of Fish and Game to determine the impact of grazing using NASA Earth observations from Landsat 8 Operational Land Imager (OLI) in Google Earth Engine (GEE). Images were processed with TerrSet’s Land Change Modeler and ArcGIS Pro’s Change Detection Wizard to understand land changes following grazing. A Normalized Difference Vegetation Index (NDVI) analysis was performed to assess impacts on vegetation productivity and compare variance in biomass before and after grazing. A Normalized Difference Water Index (NDWI) was used to compare changes in the wetland and its vegetation content to evaluate the suitability of the area for migratory birds post-grazing. Results showed a decrease in the vegetation index and an increase in the water index postgrazing. The DEVELOP team’s analysis suggests that grazing helps break down thick, senesced vegetation and increase soil moisture. Providing a workflow model will aid partners in continuing to monitor this management area and other management areas across the state.

change detection↗
NASA NTRS2024

Platte River Basin Water Resources II: Predicting Land Cover Change in the Platte River Basin to Select Wetland Protection Sites Vulnerable to Urban Encroachment

The Platte River Basin (PRB) represents a critical dynamic ecosystem where wetlands play a pivotal role as essential habitats for local and migratory birds, as well as various flora and fauna. It also provides many crucial ecosystem services that directly and indirectly benefit human welfare. However, it is threatened by anthropogenic activity, climate change, and urbanization. Our team partnered with Audubon Great Plains, the regional office of a national non-profit organization (NGO), to analyze the future potential development of the region and its potential impact on the wetlands. We utilized remotely sensed data, such as Landsat 8 Operational Land Imager (OLI) and Suomi National Polar Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), as well as NASA Socioeconomic Data and Application Center (SEDAC) data, to simulate urban growth potential up to 2050 using the open-source model FUTure Urban-Regional Environment Simulation (FUTURES) model. Our results for two proposed scenarios (all wetlands are protected, and no wetlands are protected) revealed that at least 56 counties out of 81 in the PRB would experience growth by the year 2050. The results for the first scenario show that there will be no loss of wetlands in the future. However, the results for the second scenario indicate a basin-wide reduction of 43.22 square kilometers (0.58%) in wetland areas, leading to a significant loss of bird habitat, critical for conservation. The results will help Audubon Great Plains' Urban Woods and Prairies Initiative to lead awareness workshops for communities about wetland protection and to form impactful conservation strategies.

FUTURES↗
NASA NTRS2018

Mapping Forest Disturbance Intensity in North and South Carolina Using Annual Landsat Observations and Field Inventory Data

Disturbance and regrowth are vital processes in determining the roles of forest ecosystem in the carbon and biogeochemical cycles. Using time series observations, the vegetation change tracker (VCT) algorithm was designed to map the location, timing, and spectral magnitudes of forest disturbance events. While these spectral disturbance magnitudes are indicative of physical changes in tree cover or biomass, their quantitative relationships have yet to be established. This study focuses on estimating disturbance intensity as measured by percent basal area removal using spectral indices from the VCT algorithm over North and South Carolina. Repeat measurements on Forest Service Forest Inventory Analysis (FIA) ground plots, which provide changes in basal area between multiple dates at precise locations, are used for training and validation of the model. The overall R2 between predicted disturbance intensity and reference data is 0.66, and cross-validation prediction uncertainty is 14% in North Carolina. Possible causes of this uncertainty could be site heterogeneity and the temporal offset between ground measurements and satellite observations. Results show the area of stand clearing disturbances remains relatively stable around 1143 km2 yr−1 in North and South Carolina throughout the period of observations (1985–2015). The average amount of forest area affected by partial disturbance is much higher at 3287 km2 yr−1. The area of partial disturbances has strong inter-annual variability with a high value of 6000 km2 in 2007 and a low value of 1919 km2 in 2013.

Xin Tao↗
NASA NTRS2011

The Invasive Species Forecasting System

The Invasive Species Forecasting System (ISFS) provides computational support for the generic work processes found in many regional-scale ecosystem modeling applications. Decision support tools built using ISFS allow a user to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of management concern, such as a national park, monument, forest, or refuge. This type of decision product helps resource managers plan invasive species protection, monitoring, and control strategies for the lands they manage. Until now, scientists and resource managers have lacked the data-assembly and computing capabilities to produce these maps quickly and cost efficiently. ISFS focuses on regional-scale habitat suitability modeling for invasive terrestrial plants. ISFS s component architecture emphasizes simplicity and adaptability. Its core services can be easily adapted to produce model-based decision support tools tailored to particular parks, monuments, forests, refuges, and related management units. ISFS can be used to build standalone run-time tools that require no connection to the Internet, as well as fully Internet-based decision support applications. ISFS provides the core data structures, operating system interfaces, network interfaces, and inter-component constraints comprising the canonical workflow for habitat suitability modeling. The predictors, analysis methods, and geographic extents involved in any particular model run are elements of the user space and arbitrarily configurable by the user. ISFS provides small, lightweight, readily hardened core components of general utility. These components can be adapted to unanticipated uses, are tailorable, and require at most a loosely coupled, nonproprietary connection to the Web. Users can invoke capabilities from a command line; programmers can integrate ISFS's core components into more complex systems and services. Taken together, these features enable a degree of decentralization and distributed ownership that have helped other types of scientific information services succeed in recent years.

Schnase, John↗
NASA NTRS2022

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems The National Academies have prioritized the pursuit of new scientific discoveries using diverse and temporally coordinated measurements from multiple ground and space-based observatories. Networked communications can enable such measurements by connecting individual observatories and allowing them to operate as a cohesive and purposefully designed system. Timely data flows across terrestrial and space communications networks are required to observe transient scientific events and processes. Currently, communications to space-based observatories experience large latencies due to manual service reservation and scheduling procedures, intermittent signal coverage, and network capacity constraints. If space communications network latencies could be reduced, new discoveries about dynamic scientific processes could be realized. However, science mission and network planners lack a systematic framework for defining, quantifying and evaluating timely space data flow implementation options for transient scientific observation scenarios involving multiple ground and space-based observatories. This dissertation presents a model-based systems engineering approach to investigate and develop network solutions to meet the needs of transient science space systems. First, a systematic investigation of the current transient science operations of the National Aeronautics and Space Administration’s (NASA) Tracking and Data Relay Satellite (TDRS) space data network and the Neil Gehrels Swift Observatory resulted in a formal architectural model for transient science space systems. Two methods individual missions may use to achieve timely network services were defined, quantitatively modeled, and experimentally compared. Next, the architectural model was extended to describe two alternative ways to achieve timely and autonomous space data flows to multiple space-based observatories within the context of a purposefully designed transient science observation scenario. A quantitative multipoint space data flow modeling method based in queueing theory was defined. General system suitability metrics for timeliness, throughput, and capacity were specified to support the evaluation of alternative network data flow implementations. A hypothetical design study was performed to demonstrate the multipoint data flow modeling method and to evaluate alternative data flow implementations using TDRS. The merits of a proposed future TDRS broadcast service to implement multipoint data flows were quantified and compared to expected outcomes using the as-built TDRS network. Then, the architectural model was extended to incorporate commercial network service providers. Quantitative models for Globalstar and Iridium short messaging data services were developed based on publicly available sources. Financial cost was added to the set of system suitability metrics. The hypothetical design study was extended to compare the relative suitability of the as-built TDRS network with the commercial Globalstar and Iridium networks. Finally, results from this research are being applied by NASA missions and network planners. In 2020, Swift implemented the first automated command pipeline, increasing its expected gravitational wave follow-up detection rate by greater than 400%. Current NASA technology initiatives informed by this research will enable future space-based observatories to become interoperable sensing devices connected by a diverse ecosystem of network service providers.

Christopher J. Roberts↗
NASA NTRS2020

Hawai'i Water Resources: Utilizing NASA Earth Observations to Assess Ocean Conditions Leading to the Spread of the Nuisance Red Algae (Chondria tumulosa) in Papahānaumokuākea Marine National Monument, Hawai’i

Chondria tumulosa, a newly discovered red alga, was observed in low abundance in 2016 but has since proliferated and is now smothering and decimating vast expanse of coral reefs in Manawai, located in Papahānaumokuākea Marine National Monument (PMNM). If the spread persists, the outbreak of this cryptogenic species could potentially cause region-wide ecosystem degradation. In coordination with the U.S. Fish and Wildlife Service, Marine National Monuments of the Pacific and the National Oceanographic and Atmospheric Administration (NOAA) Office of National Marine Sanctuaries’ Papahānaumokuākea Marine National Monument, this project created a tool to analyze oceanographic conditions (sea surface temperature (SST), chlorophyll-a, water velocity, salinity, turbidity) across the Monument that could potentially be driving the algal spread. The Google Earth Engine tool enabled the partners to visualize oceanographic conditions and gather time-series graphs utilizing Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Colour Instrument (OLCI), Hybrid Coordinate Ocean Model (HYCOM) and NOAA's Climate Data Record in a user-friendly interface. The team used in situ SST data from subsurface temperature recorders provided by the partners to validate the tool's accuracy. Preliminary statistical analysis of MODIS data found warming trends in SST in Manawai as well as increased chlorophyll-a levels during the summer months in contrast to the control (non-infected) Lalo atoll. The tool did not aim to classify algal presence due to limited availability of higher resolution satellite imagery but instead enabled PMNM managers to monitor conditions that may be conducive to algal growth around the monument to make informed decisions and mitigation practices.

DEVELOP Project Summary↗
NASA NTRS2017

Cross-Disciplinarity in the Advance of Antarctic Ecosystem Research

The biodiversity, ecosystem services and climate variability of the Antarctic continent, and the Southern Ocean are major components of the whole Earth system. Antarctic ecosystems are driven more strongly by the physical environment than many other marine and terrestrial ecosystems. As a consequence, to understand ecological functioning, cross-disciplinary studies are especially important in Antarctic research. The conceptual study presented here is based on a workshop initiated by the Research Programme Antarctic Thresholds - Ecosystem Resilience and Adaption of the Scientific Committee on Antarctic Research, which focused on challenges in identifying and applying cross-disciplinary approaches in the Antarctic. Novel ideas, and first steps in their implementation, were clustered into eight themes, ranging from scale problems, risk maps, organism and ecosystem responses to multiple environmental changes, to evolutionary processes. Scaling models and data across different spatial and temporal scales were identified as an overarching challenge. Approaches to bridge gaps in the research programmes included multi-disciplinary monitoring, linking biomolecular findings and simulated physical environments, as well as integrative ecological modelling. New strategies in academic education are proposed. The results of advanced cross-disciplinary approaches can contribute significantly to our knowledge of ecosystem functioning, the consequences of climate change, and to global assessments that ultimately benefit humankind.

Scaling↗
NASA NTRS2024

Southern California Water Resources: Using NASA Earth Observations to Monitor Seagrass Extent and Water Quality Parameters in Southern California

Eelgrass (Zostera marina) is a species of submerged aquatic vegetation found in shallow bays and estuaries with soft seafloors. Eelgrass is recognized for providing ecosystem benefits, such as carbon sequestration, sediment stabilization, water clarification, and fish and wildlife habitats. However, eelgrass is impacted by both marine and terrestrial threats associated with climate change. In this project, we worked with the Southern California Coastal Water Research Project, the National Oceanic and Atmospheric Administration’s National Marine Fisheries Service, and the State of California San Diego Regional Water Quality Control Board to investigate water quality parameters (i.e., chlorophyll-a concentration, sea surface temperature, and turbidity) associated with eelgrass in Newport Bay and Mission Bay, California. The team used Landsat 8 Operational Land Imager and Thermal Infrared Sensor, Landsat 9 Operational Land Imager-2 and Thermal Infrared Sensor-2, Sentinel-2 MultiSpectral Instrument, and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station land surface temperature and cloud mask to create a time series of these water quality parameters from 2019–2023. We found that the parameters followed cyclical, seasonal patterns with turbidity and sea surface temperature peaking in the summer. We did not find that the parameters had changed significantly over longer time periods. These results will be used in a model developed by the Southern California Coastal Water Research Project to assess eelgrass ecosystem health and predict ecosystem occupancy in the future.

Katya Beener↗