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GCE-LTER Data Set Summary

Accession: PLT-GCET-1801 Research Theme: Plant Ecology (Directed Study)
Contributors: John O'Donnell, John Schalles
Title: Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
Abstract: We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
DOI: 10.6073/pasta/68320d728b883bca949ca41ee3675500
Key Words: biomass, climate drivers, discharge, landsat, plant ecology, plant growth, productivity, sea level, Spartina, stress response
LTER Core Area: Disturbance Patterns, Primary Production
Research Themes: Plant Ecology, Geospatial Analysis, Multi-Disciplinary Study, Population Ecology
Study Period: 12-Mar-1984 to 31-Dec-2011
Study Sites:
GCE_Domain -- GCE Domain, Sapelo Island, Georgia
» Download Geographic Coverage: Google Earth, ESRI Shapefile (polygons)
Species References: Spartina alterniflora
Data References: MLT-GCED-0811 (Ground truth data for matching hyperspectral imagery), GIS-GCES-1401f (2006 AISA hyperspectral imagery of the GCE domain for vegetation)
Research Protocols:

Landsat Ecosystem Disturbance Adaptive Processing System (LDEPAS) Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2 (NASA/USGS)

Publications:

O'Donnell, J. and Schalles, J.F. 2016. Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast. Special Issue: Remote Sensing in Coastal Environments. Remote Sensing. 8(6):22. (DOI: 10.3390/rs8060477)

Downloads: Information

Data Table: PLT-GCET-1801 (Main data table for data set PLT-GCET-1801, 294 records)

Access: Public (released 29-Jan-2018)

Metadata: Text (ESA FLED), XML (Ecological Metadata Language)

Data Formats: Spreadsheet (CSV) [136.97kb],  Text File [133.17kb],  Text Report [176.81kb],  MATLAB (GCE Toolbox) [515.72kb],  MATLAB (Variables) [493.67kb]

Column List:(display)


Supporting Document: PLT-GCET-1801_landsat (Additional Landsat 5 TM sensor metadata.)

Access: Public (released 29-Jan-2018)

Metadata: XML (Ecological Metadata Language)

Data Formats: Spreadsheet CSV [207kb]


Ancillary Metadata: PLT-GCET-1801_landsatmeta (Data dictionary for Landsat 5 TM sensor metadata.)

Access: Public (released 29-Jan-2018)

Metadata: XML (Ecological Metadata Language)

Data Formats: ASCII text [17kb]


GIS Keyhole Markup Language File: PLT-GCET-1801_polygons (Google earth file highlighting Spartina polygons used for data extraction.)

Access: Public (released 29-Jan-2018)

Metadata: XML (Ecological Metadata Language)

Data Formats: Google Earth Keyhole Markup Language (KMZ) file [20kb]


Script or program code: PLT-GCET-1801_ROI (Zip file containing ENVI ROI file used to extract pixel data from landsat 5 imagery.)

Access: Public (released 29-Jan-2018)

Metadata: XML (Ecological Metadata Language)

Data Formats: Zip archive [4kb]

Statistics: Generate script code to retrieve data tables for analysis in: MATLAB, R, SAS, SPSS
Citation: O'Donnell, John. 2018. Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis. Georgia Coastal Ecosystems LTER Project, University of Georgia, Long Term Ecological Research Network. http://dx.doi.org/10.6073/pasta/68320d728b883bca949ca41ee3675500

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This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959 and OCE-1237140. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.