Dataset Available

2015 Bay Batiste marsh Aboveground Green Biomass (AGB) from the Compact Airborne Spectrographic Imager (CASI)

Authors: Mo, Yu, Kearney, Michael Sean

Published On: Apr 10 2018 19:25 UTC

DOI: https://doi.org/10.7266/N7C24TX2

UDI: R4.x264.000:0077

Files

Individual Files

No. of Downloads: 2

No. of Files: 1

File Size: 175.97 MB

File Format(s):
tif

Project Information

Funded By:
Gulf of Mexico Research Initiative

Funding Cycle:
RFP-IV

Research Group:
Coastal Waters Consortium II (CWC II)

Point of Contact

Joyce Christine Alexis Riter
University of Maryland / Department of Environmental Science and Technology
ariter99@umd.edu

Data Collection Period

2015-08-16

Theme keywords

AGB, Aboveground Green Biomass, Macondo oil spill

ISO 19115-2 Metadata

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Abstract:

The map shows the Aboveground Green Biomass (AGB) of coastal marshes near Bay Batiste, Louisiana in 2015. The data is generated from the Compact Airborne Spectrographic Imager (CASI) data collected by the National Center for Airborne Laser Mapping (NCALM), University of Houston (GRIIDC dataset R4.x264.196:0002). The CASI data were collected on August 16, 2015, with flight altitude of 2400 m. The data were collected in spatial resolution of 1.2 m by 1.2 m and with 48 bands (bandwidth 14.4 nm) at 380–1050 nm. 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. This dataset supports the publication: Mo, Y., Kearney, M. S., Riter, J. C. A., Zhao, F., & Tilley, D. R. (2018). Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models. International Journal of Applied Earth Observation and Geoinformation, 68, 189–201. doi:10.1016/j.jag.2017.12.003

Suggested Citation:

Mo, Yu, Kearney, Michael Sean. 2018. 2015 Bay Batiste marsh Aboveground Green Biomass (AGB) from the Compact Airborne Spectrographic Imager (CASI). Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7C24TX2

Publications:

Mo, Y., Kearney, M. S., Riter, J. C. A., Zhao, F., & Tilley, D. R. (2018). Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models. International Journal of Applied Earth Observation and Geoinformation, 68, 189–201. doi:10.1016/j.jag.2017.12.003

Purpose:

To evaluate the use of Landsat data to examine Louisiana marsh conditions before and after the April 20 – July 15, 2010, Macondo oil spill, using the Normalized Difference Vegetation Index (NDVI). Specifically, this dataset is generated as part of the effort to establish the correlations between optical remote sensing measurements and the marsh biomass.

Data Parameters and Units:

Dry weight of biomass per unit area (g/m2)