The 2020-2022 catalyst project Bridging the gap between quadrats and satellites: assessing utility of drone-based imagery to enhance emergent vegetation biomonitoring conducted a regionally coordinated effort, working in salt marshes an
Resources
Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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The 2020-2021 catalyst project Refining Techniques for High-Frequency Monitoring of Chlorophyll in the NERRS brought together twelve biogeochemically diverse reserves to compare results from new YSI in situ sensor technology with ex
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Monitoring plays a central role in detecting climate and anthropogenic stressors and associated changes in wetlands. There is a need for wetland monitoring programs to bridge the gap between ground-based surveys, which can miss important spatial heterogeneity and cause wetland disturbance, a
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With funding from the NERRS Science Collaborative, scientists from 12 biogeographically diverse Reserves compared fluorescence measurements taken by the YSI EXO TAL sensor to extracted chlorophyll concentrations processed in the lab.
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Standardized protocols for sensor-based chlorophyll monitoring are now available for use by staff around the system to implement high frequency chlorophyll monitoring at their reserves.
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This dataset comprises the data collected and produced as part of the 2016 research project Investigating the Interconnectedness of Climate Change, Nuisance Mosquitoes, and Resilience of Coastal Salt Marsh Systems.
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These coastal hazard risk communication training process agendas can be used to as a model help facilitators develop trainings for coastal decision makers in other communities.
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These facilitation guides and job aids, part of a Resilience Metrics toolkit, provide tools and activities for each step of the process to develop and track metrics of adaptation success.
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These coastal hazard risk communication workshop materials can be used to help facilitate trainings for coastal decision makers.
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This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.