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 2005. The projection is the Universal Transverse Mercator (UTM Zone 15 North). The datum is WGS-1984. Spatial resolution is 28.5 meters by 28.5 meters resampled to 30 meters by 30 meters. The dataset consists of 24 stacked files. They are NDVI datasets from sampling dates throughout 2005 (in turns of Julian Day of Year):2, 42, 82, 98, 106, 114, 122, 130, 138, 154, 170, 178, 194, 202, 234, 250, 258, 282, 290, 298, 306, 314, 322, 354. More information can be found at: Yu Mo, Bahram Momen, Michael S. Kearney, Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes,Ecological Modelling 312 (2015) 191–199. Processing 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.

Suggested Citation:

Riter, Joyce Christine. 2016. Year 2005 southeastern Louisiana marsh Normalized Difference Vegetation Index (NDVI) phenology record from Landsat 5 TM and Landsat 7 ETM+ of Path 22 and Rows 39 and 40. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7F47M2T

Publications:

Mo, Y., Momen, B., & Kearney, M. S. (2015). Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes. Ecological Modelling, 312, 191–199. doi:10.1016/j.ecolmodel.2015.05.022

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:

Landsat 5 TM and Landsat ETM + pixels are 30 meters by 30 meters. NDVI values range from -1.00 to 1.00 and are unitless.

Methods:

The Landsat Surface Reflectance Climate Data Record (Landsat CDR) generated from the Landsat Ecosystem Disturbance Adaptive Processing System atmosphere correction tool was downloaded from the USGS Earth Explorer website (http://earthexplorer.usgs. gov/). Further processing of the data was performed using ENVI 4.8 (ITT Exelis, USA). Path 22 and Row 39 and 40 were mosaicked together and resized. Surface reflectance was calculated. Clouds, cloud shadows, water, as well as data gaps in Landsat 7 imagery were masked out using the full masks provided in the Landsat CDR. All surface values equal to or less than 0.00 were masked and replaced by the value 0.0001. All surface values greater than 1.00 were masked and replaced by the value 1.00. NDVI calculated with the standard equation = (rho NIR - rho red)/(rho NIR - rho red) where rho NIR and rho red are the surface reflectance of the near-infrared and red visible Landsat bands.

Instruments:

Landsat 5 TM and Landsat 7 ETM+ data processed by NASA with LEDAPS code to surface reflectance.

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 Landsat Surface Reflectance Climate Data Record (CDR) provides high quality surface reflectance data for land surface change studies (http://landsat.usgs.gov/CDR_LSR.php). The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) is a NASA project (Masek et al. 2006) that provides surface reflectance values from Landsat TM and ETM+ data by using ozone, water vapor, and aerosol optical thickness data developed for the MODIS sensor to correct for molecular scattering and absorption by the atmosphere based on the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models (Vermote et al. 1997). Eric Vermote, Nazmi Saleous, Jonathan Kutler, and Robert Wolfe of NASA GSFC developed the LEDAPS software with support from the NASA Terrestrial Ecology program (PI: Jeff Masek). Dr. Feng Gao made further software adaptions (Masek et al. 2013). Level-1T Landsat products are radiometrically calibrated and orthorectified using ground control points for precision correction and digital elevation models to correct for relief displacement. References http://landsat.usgs.gov/CDR_LSR.php last obtained 29 May 2014. Masek, J.G., E.F. Vermote, N. Saleous, R. Wolfe, F.G. Hall, F. Huemmrich, F. Gao, J. Kutler, and T.K. Lim, 2006. A Landsat surface reflectance data set for North America, 1990-2000, Geoscience and Remote Sensing Letters, 3: 68-72. Masek, J.G., E.F. Vermote, N. Saleous, R. Wolfe, F.G. Hall, F. Huemmrich, F. Gao, J. Kutler, and T.K. Lim. 2013. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code, Version 2. Model product. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1146. Vermote, E.F. , N. Saleous, C.O. Justice, Y.J. Kaufman, J.L. Privette, L. Remer, J.C. Roger, and D. Tanre, 1997. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm, and validation, Journal of Geophysical Research, 102: 17131-17141.

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