Dataset: Habitat classification (mangrove, marsh, water) based on drone imagery taken in spring 2023 in Port Fourchon, LA

This dataset has not been validatedData not availableVersion 1 (2025-01-03)Dataset Type:Unknown

Principal Investigator: James Nelson (University of Louisiana at Lafayette)

Student, Contact: Herbert Leavitt (University of Louisiana at Lafayette)

Student: Alexander Thomas (University of Louisiana at Lafayette)

BCO-DMO Data Manager: Karen Soenen (Woods Hole Oceanographic Institution)

Project: CAREER: Integrating Seascapes and Energy Flow: learning and teaching about energy, biodiversity, and ecosystem function on the frontlines of climate change (Louisiana E-scapes)

Abstract

This dataset contains habitat classifications based on drone based imagery collected at the location of sites sampled during the Fall 2022 drop sampling season. The imagery includes geospatial coverage of estuarine and adjacent terrestrial habitats, providing detailed landscape features such as vegetation type, water bodies, and land use around each sampling site. The spatial resolution of the satellite imagery allows for precise analysis of habitat variables at multiple scales. The resolution o...

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Cite Dataset

Column Name,Column Description [Include meaning of any codes or flags used in data column as well as detection limits.],Units of measurement,missing data/no data value

  • FID ,Numerical identifier for each polygon,unitless ,
  • Shape ,Classification of feature type (all polygons in this case) ,unitless ,
  • Area_Pxl ,number of pixels in the polygon ,count,
  • Class_name,"Catagories of habitat types: Listed as Mangroves (mangrove vegetation), Spartina (spartina vegetation, equivolent to saltmarsh in the satelite data, Water (open water), Spillbank (Equivolent to man-made) in the satellite data) ",,
  • Max_diff,"A Normalized Difference Vegetation Index  created using the Red and NIR color bands. This was used as a parameter for species identification as it helps quantify plant health and habitat composition (Broussard et al., 2018).",,
  • Mean_Blue,Mean reflectance in blue color band (444 nm) for the polygon ,,
  • Mean_DSM ,Mean digital surface model value for the polygon. Note: these values are not corrected and only represent relative elevation. DSM values broadly represent vegetation heigh but should not be interpreted beyond distinguishing vegetation classes,,
  • Mean_Green,Mean reflectance in green  color band (560 nm) for the polygon ,,
  • Mean_NDVI,Mean NDVI value for the polygon,,
  • Mean_NIR,Mean reflectance in near-infrared color band (842 nm) for the polygon ,,
  • Mean_Red,Mean reflectance in red color band (668 nm) for the polygon ,,
  • Mean_Red_E,Mean reflectance in Rededge color band (717 nm) for the polygon ,,

Related Datasets

IsSourceOf
Dataset: Drone habitat variables, Port Fourchon 2023
Relationship Description: The dataset "Classified drone-based imagery of Port Fourchon, LA during Spring 2023" contains the shapefiles used to generate the tables in this dataset.
Leavitt, H., Thomas, A., Nelson, J. (2025) Habitat variables (mangrove, marsh, water) of Port Fourchon, LA dervied from drone imagery taken in spring 2023. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-01-08 doi:10.26008/1912/bco-dmo.948112.1

Related Publications

Methods
Broussard, W., Suir, G., & Visser, J. (2018). Unmanned Aircraft Systems (UAS) and satellite imagery collections in a coastal intermediate marsh to determine the land-water interface, vegetation types, and Normalized Difference Vegetation Index (NDVI) values. Engineer Research and Development Center (U.S.). https://doi.org/10.21079/11681/29517
Methods
Zhao, J., Fang, Y., Zhang, M., & Dong, Y. (2020). Identification of Remote Sensing-Based Land Cover Types Combining Nearest-Neighbor Classification and SEaTH Algorithm. Journal of the Indian Society of Remote Sensing, 48(7), 1007–1020. https://doi.org/10.1007/s12524-020-01131-6

Leavitt, H., Thomas, A., Nelson, J. (2025) Habitat classification (mangrove, marsh, water) based on drone imagery taken in spring 2023 in Port Fourchon, LA. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-01-03 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/947918 [access date]

Terms of Use

This dataset is licensed under Creative Commons Attribution 4.0.


If you wish to use this dataset, it is highly recommended that you contact the original principal investigators (PI). Should the relevant PI be unavailable, please contact BCO-DMO (info@bco-dmo.org) for additional guidance. For general guidance please see the BCO-DMO Terms of Use document.

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Funded by the U.S. National Science Foundation