I. Data Set Descriptors A. Title: Deepak Mishra. 2020. Leaf area index and above ground biomass for Juncus roemerianus in the Grand Bay National Estuarine Research Reserve from 2015 to 2019. Georgia Coastal Ecosystems LTER Data Catalog (data set PLT-NASA-2006b; http://gce-lter.marsci.uga.edu/data/PLT-NASA-2006b) B. Accession Number: PLT-NASA-2006b C. Description 1. Originator(s): Name: Deepak Mishra Address: Department of Geography University of Georgia Athens, Georgia 30602 Country: USA Email: dmishra@uga.edu 2. Abstract: Leaf area index (LAI) and above ground biomass were measured in 16 permanent Juncus roemerianus vegetation plots located in the Grand Bay National Estuarine Research Reserve in Mississippi. Data were collected from 2015 to 2019. LAI were measured using a handheld ceptometer. Above ground biomass for each plot were collected from core samples. 3. Study Type: Research Study 4. Study Themes: Plant Ecology 5. LTER Core Areas: Other Site Research 6. Georeferences: none 7. Submission Date: Jun 12, 2020 D. Keywords: aboveground biomass, canopy cover, Embryophyta, GCE, Georgia, Georgia Coastal Ecosystems, Juncaceae, Juncus roemerianus, leaf area index, Lilianae, LTER, Magnoliopsida, morphology, plant biomass, Plant Monitoring, Plantae, Poales, salt marshes, Sapelo Island, Spermatophytina, Streptophyta, Tracheophyta, USA, Viridiplantae II. Research Origin Descriptors A. Overall Project Description 1. Project Title: Tidal and species-based MODIS GPP product for estimating marsh blue carbon across the Southeastern United States 2. Principal Investigators: Name: Deepak Mishra Address: Department of Geography University of Georgia Athens, Georgia 30602 Country: USA Email: dmishra@uga.edu 3. Funding Period: Jan 01, 2017 to Dec 31, 2020 4. Objectives: The overarching goal of this project is a comprehensive assessment of tidal marsh carbon storage capacity in the southeastern U.S. by combining simultaneous MODIS observations and plant Gross Primary Productivity (GPP) from four marshes in GA, MS, and LA. The specific objectives are: 1) To accurately measure marsh surface flooding and fine tune a MODIS marsh inundation index that flags flooded marsh pixels. 2) To map marsh cover types, including wrack, soil, species composition and vegetation fraction (VF) via Unmanned Aerial Vehicle (UAV) and/or plot-based sampling. 3) To develop spectral reflectance models of vegetation biophysical traits that assist with building a canopy photosynthesis model (CPM) of marsh plant GPP. 4) To estimate continuous measures of plant photosynthetic capacity under dry and flooded conditions. 5. Abstract: Coastal marshes are atmospheric carbon sinks, depositing as much as 1713 g C m-2 yr-1 in soils, which has been termed "blue carbon". However, marshes are threatened by multiple causes, especially sea-level rise. Remote monitoring of landscape Gross Primary Productivity (GPP), a proxy for carbon sequestration potential, helps assess C sinks and facilitate prioritization of restoration and conservation. We propose to create a novel MODIS GPP algorithm for coastal marshes that accounts for the species-specific influence of tidal inundation on plant production. We will calibrate our models across four salt and brackish marsh sites across three states (Louisiana, Mississippi, Georgia) and covering three species (Spartina alterniflora, Spartina patens, Juncus roemerianus). Models will be based on a combination of eddy covariance carbon flux data, monitoring of plant level photosynthesis, field and spectral estimates of species composition and plant biophysical variables, as well as accurate measures of marsh surface inundation. The ultimate product will be regional maps of GPP that assists with monitoring coastal marsh blue carbon across the southeastern United States. This work relies on MODIS based remote sensing to scale from carbon flux and field data to regional GPP assessments. These remote sensing GPP models will be based on two approaches: production efficiency models (PEM) which compute GPP from absorbed solar radiation and canopy photosynthesis models (CPM), based on biophysical variables including leaf area index. We will adapt PEM and CPM GPP models to tidal marshes from 500 m tide-indexed MODIS daily surface reflectance data. As part of this study, we will solve problems that complicate tidal marsh GPP estimates. For example, we will improve marsh surface flooding estimates, generate a plant-centered GPP model that reduces the influence of tidal exchange on carbon accounting, adapt new spectral biophysical indices that account for the influence of wetland moist soils on reflectance, and use chlorophyll flouremetry to measure plant level productivity during high tides, a time when eddy covariance towers cannot estimate carbon flux. We also will map species composition and generate light use efficiency estimates for common coastal marsh species. Consequently, we will be able to generate species invariant and tide robust CPM and PEM plant centered GPP. The crux of this proposal is to combine multiple sources of information to generate our ultimate product, regional maps of MODIS derived plant GPP spanning 2000-2020 and perform a comprehensive phenological analysis. End-users engagement is also important. We will make end-users, such as coastal managers, aware of the tools we will develop, provide access to tools and instructional documents, and train staff to use the tools to inform decision-making. To ease this task, we will develop a Python plug-in for QGIS, an open source geospatial software and host our source code on GitHub to facilitate future and community model development. Applications of our work include estimating CO2 exchange after natural and anthropogenic disasters, modeling the influence of sea level rise on marsh health, understanding coastal C sources and sinks, and use by government agencies to assess restoration trajectories for conservation and management of critical coastal ecosystems. 6. Funding Source: NASA NNX17AI76G B. Sub-project Description 1. Site Description a. Geographic Location: GrandBay -- Grand Bay NERR, Moss Point, Mississippi GrandBay_FluxTower -- Grand Bay NERR Flux Tower Coordinates: GrandBay -- NW: 088 28 49.40 W, 30 25 43.80 N NE: 088 23 46.11 W, 30 25 43.80 N SE: 088 23 46.11 W, 30 19 00.91 N SW: 088 28 49.40 W, 30 19 00.91 N GrandBay_FluxTower -- 88 25 01.2 W, 30 22 08.4 N b. Physiographic Region: GrandBay -- unspecified GrandBay_FluxTower -- unspecified c. Landform Components: GrandBay -- unspecified GrandBay_FluxTower -- unspecified d. Hydrographic Characteristics: GrandBay -- unspecified GrandBay_FluxTower -- unspecified e. Topographic Attributes: GrandBay -- unspecified GrandBay_FluxTower -- unspecified f. Geology, Lithology and Soils: GrandBay -- unspecified GrandBay_FluxTower -- unspecified g. Vegetation Communities: GrandBay -- unspecified GrandBay_FluxTower -- unspecified h. History of Land Use and Disturbance: none recorded i. Climate: Climate summary for Sapelo Island, Georgia, based on NWS data from 1980-2010: Daily-aggregated Values: Mean (sample standard deviation) mean air temperature: 20.09°C (7.28°C) minimum air temperature: 15.02°C (7.96°C) maximum air temperature: 24.82°C (6.98°C) total precipitation: 3.26mm (10.3mm) Yearly-aggregated Daily Values: Mean (sample standard deviation) total precipitation (1980-2010): 1124mm (266mm) 2. Experimental or Sampling Design a. Design Characteristics: Leaf area index was collected at established Juncus roemerianus vegetation plots b. Permanent Plots: not specified c. Data Collection Duration and Frequency: Measurements were taken by hand at multiple locations within the canopy Beginning of Observations: Jan 21, 2015 End of Observations: Nov 07, 2019 3. Research Methods a. Field and Laboratory Methods: Method 1: Lear Area Index -- Leaf area index was collected using a handheld ceptometer. Measurements were taken by standing on the southwest corner of each one square meter plot. Six measurements of downwelling radiation were collected at three different azimuths. Three measurements were taken above the plot canopy and three were taken below the canopy directly above the soil surface. Measurements were collected in the following sequence: above canopy azimuth 1, above canopy azimuth 2, below canopy azimuth 1, below canopy azimuth 2, below canopy azimuth 3, above canopy azimuth 3. Method 2: Above Ground Biomass -- Aboveground biomass (AGB) was collected from core samples with an area of 62.1 cm2. Live and dead AGB were separated and dried at 60°C for 5 days and then weighed. Biomass measurements were scaled to the meter-square level. b. Protocols: Method 1: none Method 2: none c. Instrumentation: Method 1: Decagon Devices, Inc. model AccuPAR LP-80 handheld ceptometer Method 2: none d. Taxonomy and Systematics: Method 1: not applicable Method 2: not applicable e. Speclies List: f. Permit History: Method 1: not applicable Method 2: not applicable 4. Project Personnel a. Personnel: 1: Deepak Mishra 2: Jessica O'Connell 3: David L. Cotten 4: Peter Hawman 5: Lishen Mao 6: Caroline Narron b. Affiliations: 1: University of Georgia, Athens, Georgia 2: University of Georgia, Athens, Georgia 3: University of Georgia, Athens, Georgia 4: University of Georgia, Athens, Georgia 5: University of Georgia, Athens, Georgia 6: University of Georgia, Athens, Georgia III. Data Set Status and Accessibility A. Status 1. Latest Update: 18-Jun-2020 2. Latest Archive Date: 18-Jun-2020 3. Latest Metadata Update: 18-Jun-2020 4. Data Verification Status: B. Accessibility 1. Storage Location and Medium: Stored at on media: 2. Contact Person: Name: Position: Organization: Address: City: State: Postal Code: Phone: Email: UserID: 3. Copyright Restrictions: 4. Restrictions: a. Release Date: Affiliates: , Public: b. Citation: c. Disclaimer: 5. Costs: IV. Data Structural Descriptors A. Data Set File 1. File Name: PLT-NASA-2006b_1_0.CSV 2. Size: 266 records 3. File Format: ASCII text (comma-separated value format) 3a. Delimiters: single comma 4. Header Information: 5 lines of ASCII text 5. Alphanumeric Attributes: 6. Quality Control Flag Codes: Q = questionable value, I = invalid value, E = estimated value 7. Authentication Procedures: 8. Calculations: 9. Processing History: Software version: GCE Data Toolbox Version 3.9.9b (06-Mar-2019) Data structure version: GCE Data Structure 1.1 (29-Mar-2001) Original data file processed: PLT-NASA-2006b.TXT (266 records) Data processing history: 18-Jun-2020: new GCE Data Structure 1.1 created ('newstruct') 18-Jun-2020: 266 rows imported from ASCII data file 'PLT-NASA-2006b.TXT' ('imp_ascii') 18-Jun-2020: 13 metadata fields in file header parsed ('parse_header') 18-Jun-2020: data structure validated ('gce_valid') 18-Jun-2020: Q/C flagging criteria applied, 'flags' field updated ('dataflag') 18-Jun-2020: automatically assigned study date metadata descriptors based on the range of date values in date/time columns (add_studydates) 18-Jun-2020: updated 1 metadata fields in the Dataset sections ('addmeta') 18-Jun-2020: imported Dataset, Project, Site, Study, Status, Supplement metadata descriptors from the GCE Metabase ('imp_gcemetadata') 18-Jun-2020: updated 46 metadata fields in the Dataset, Project, Site, Status, Study, Supplement sections ('addmeta') 18-Jun-2020: updated 6 metadata fields in the Data sections ('addmeta') 18-Jun-2020: updated 15 metadata fields in the Status, Data sections to reflect attribute metadata ('updatecols') 18-Jun-2020: parsed and formatted metadata ('listmeta') B. Variable Information 1. Variable Name: column 1. Date column 2. Plot column 3. Live_Stem_Weight column 4. Dead_Stem_Weight column 5. Core_Area column 6. LAI 2. Variable Definition: column 1. Calendar date of observation column 2. Unique vegetation plot identifier column 3. Above ground live biomass column 4. Above ground dead biomass column 5. Area of core sample column 6. Leaf area index 3. Units of Measurement: column 1. yyyy-mm-dd column 2. none column 3. grams column 4. grams column 5. cm^2 column 6. unitless 4. Data Type a. Storage Type: column 1. string column 2. string column 3. floating-point column 4. floating-point column 5. floating-point column 6. floating-point b. Variable Codes: c. Numeric Range: column 1. (none) column 2. (none) column 3. 0 to 22.97 column 4. 0 to 26.18 column 5. 62.1 to 62.1 column 6. 0.3 to 6.7 d. Missing Value Code: 5. Data Format a. Column Type: column 1. text column 2. text column 3. numerical column 4. numerical column 5. numerical column 6. numerical b. Number of Columns: 6 c. Decimal Places: column 1. 0 column 2. 0 column 3. 2 column 4. 2 column 5. 1 column 6. 1 6. Logical Variable Type: column 1. datetime (none) column 2. nominal (none) column 3. data (continuous) column 4. data (continuous) column 5. data (continuous) column 6. data (continuous) 7. Flagging Criteria: column 1. none column 2. none column 3. x<0="I";x>50="Q" column 4. x<0="I";x>50="Q" column 5. x<0="I" column 6. x<0="I";x>7="Q" C. Data Anomalies: V. Supplemental Descriptors A. Data Acquisition 1. Data Forms: 2. Form Location: 3. Data Entry Validation: B. Quality Assurance/Quality Control Procedures: C. Supplemental Materials: D. Computer Programs: E. Archival Practices: F. Publications: not specified G. History of Data Set Usage 1. Data Request History: not specified 2. Data Set Update History: none 3. Review History: none 4. Questions and Comments from Users: none