Abstract:

The data file consists of Normalized Difference Vegetation Index (NDVI) datasets of the southeastern Louisiana marshes generated from Landsat Surface Reflectance Climate Data Record (Landsat CDR) (Path 22 and Rows 39 and 40) in 1994. The projection is the Universal Transverse Mercator (UTM Zone 15 North). The datum is WGS-1984. Spatial resolution is 30 meters by 30 meters. The dataset consists of 9 stacked files. They are NDVI datasets from sampling dates throughout 1994 (in terms of Julian Day of Year): 76, 92, 140, 172, 252, 268, 300, 316, 348. Processing was done by Yu Mo, J.C. Alexis Riter, and Michael S. Kearney of the Department of Environmental Science and Technology, University of Maryland, College Park, MD 20742. This dataset supports the publication: Mo, Y., Kearney, M., & Momen, B. (2017). Drought-associated phenological changes of coastal marshes in Louisiana. Ecosphere, 8(5), e01811. doi:10.1002/ecs2.1811

Suggested Citation:

Kearney, Michael Sean, Mo, Yu. 2018. 1994 southeastern Louisiana marsh Normalized Difference Vegetation Index (NDVI) phenology record from Landsat 5 TM of Path 22 and Rows 39 and 40. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7WH2N25

Publications:

Mo, Y., Kearney, M., & Momen, B. (2017). Drought-associated phenological changes of coastal marshes in Louisiana. Ecosphere, 8(5), e01811. doi:10.1002/ecs2.1811

Mo, Y., Kearney, M. S., & Turner, R. E. (2019). Feedback of coastal marshes to climate change: Long-term phenological shifts. Ecology and Evolution, 9(12), 6785–6797. doi:10.1002/ece3.5215

Mo, Y., Kearney, M. S., & Turner, R. E. (2020). The resilience of coastal marshes to hurricanes: The potential impact of excess nutrients. Environment International, 138, 105409. doi:10.1016/j.envint.2019.105409

Purpose:

To evaluate the effect of the April 20 - July 15, 2010 Macondo oil-spill on Louisiana marsh vegetation and marsh substrate stability with Landsat data and to examine the variation in the phenology of marsh vegetation based on NDVI data before and after the Macondo oil-spill.

Data Parameters and Units:

The Normalized Difference Vegetation Index (NDVI) is a unitless proxy in optical remote sensing for the amount of vegetation. NDVI ranges from minus one (-1.0) to plus one (+1.0). Usually, a NDVI close to zero or slightly positive means little or no vegetation and a NDVI close to +1 (0.8 - 0.9) indicates a very high density of green leaves.

Methods:

The Landsat Surface Reflectance Climate Data Record (Landsat CDR) was downloaded from the USGS Earth Explorer website (http://earthexplorer.usgs.gov/). More information about the Landsat CDR is available on its USGS official website: https://landsat.usgs.gov/landsat-surface-reflectance-high-level-data-products. Further processing of the data was performed using ENVI 4.8 (ITT Exelis, USA). All relatively clear-sky Landsat Path 22 and Row 39 and 40 images were mosaicked together and resized. All surface reflectance values equal to or less than 0.00 were masked and replaced by the value 0.0001. All surface reflectance values greater than 1.00 were masked and replaced by the value 1.0000. NDVI was calculated with the standard equation NDVI = (rho NIR - rho red)/(rho NIR - rho red ) where rho NIR and rho red are the surface reflectance of the near-infrared and visible red Landsat bands. Marsh areas were determined using the Coastal Louisiana Vegetative Type Characterization Data, created by the US Geological Survey, National Wetlands Research Center, Lafayette, LA (https://pubs.usgs.gov/sim/3290/). Clouds, cloud shadows, and water bodies were masked out using the full masks provided in the Landsat CDR.

Instruments:

Landsat 5 Thematic Mapper (TM) operated from 1984 to 2011. The Landsat 5 TM has seven spectral bands: Band 1 Blue (0.45 - 0.52 µm), Band 2 Green (0.52 - 0.60 µm), Band 3 Red (0.63 - 0.69 µm), Band 4 Near-Infrared (0.76 - 0.90 µm), Band 5 Shortwave Infrared (1.55 - 1.75 µm), Band 6 Thermal (10.40 - 12.50 µm), and Band 7 Shortwave Infrared (2.08 - 2.35 µm).

Error Analysis:

Comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance to the MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value).

Provenance and Historical References:

The following information and more can be found at: https://landsat.usgs.gov/landsat-surface-reflectance-high-level-data-products For Landsat 5 Thematic Mapper (TM) data, surface Reflectance data are generated from the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), a specialized software originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2012). The software applies Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric correction routines to Level-1 data products. Water vapor, ozone, geopotential height, aerosol optical thickness, and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA) reflectance, surface reflectance, brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. References Masek, J.G., E.F. Vermote, N. Saleous, R. Wolfe, F.G. Hall, F. Huemmrich, F. Gao, J. Kutler, and T.K. Lim. 2012. LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1080

Individual Files: