Dataset: Satellite habitat variables, Port Fourchon, 2022

Name: Satellite habitat variables, Port Fourchon, 2022
Published: 2025-01-06
Keywords: oceans

View Data: Data not available yet

Cite This Dataset

Spatial Extent: N:29.164671 E:-90.149744 S:29.092646 W:-90.269831

Marshes surrounding Port Fourchon, Louisiana.

Temporal Extent: 2022-09-23 - 2022-09-29

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)

Principal Investigator:

James Nelson (University of Louisiana at Lafayette)

Student:

Herbert Leavitt (University of Louisiana at Lafayette)

Alexander Thomas (University of Louisiana at Lafayette)

Contact:

Herbert Leavitt (University of Louisiana at Lafayette)

BCO-DMO Data Manager:

Karen Soenen (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2025-01-06

Restricted:

Current State:

Data not available

Satellite habitat variables, Port Fourchon, 2022

Abstract:

This dataset consists of satellite-derived habitat data tables used to quantify fine-scale landscape metrics in an estuarine environment undergoing rapid climate-driven habitat change. The data were generated as part of a study evaluating the effects of mangrove encroachment and marsh loss on species-landscape relationships in coastal Louisiana. Habitat variables were derived for buffer zones ranging from 150 to 600 meters around 52 field sampling sites and edge zones 1, 3, and 5 meters from the water's edge, providing detailed metrics such as percent land cover, edge area, and proportional mangrove cover. The greater spatial coverage of the satellite imagery allowed for larger habitat scales to be encompassed in the analysis. Satellite images used in this analysis were all taken during the year 2022, within a few months of our sampling season, in the region surrounding Port Fourchon, LA. This dataset enables testing of species-specific responses to habitat features at ecologically relevant fine scales, particularly for nekton species interacting with marsh edges and immediate surrounding areas. The primary purpose of this dataset is to inform ecological research focused on habitat suitability, landscape ecology, and the impacts of fine-scale habitat changes on estuarine species distributions. Researchers and resource managers can use these data to improve habitat suitability models, identify critical habitat features, and guide conservation strategies. The data were collected and interpreted by Herbert Leavitt, Dr. James Nelson, and Alex Thomas, with institutional affiliation at the time of collection being the University of Louisiana at Lafayette.

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Methods & Sampling

No raw data is included in this dataset. For information pertaining to the collection methods for the data used to generate this dataset, refer to methods sections of linked datasets

Problems/Issues

Computation time and memory was a barrier in this analysis. For this reason, although we originally intended to perform calculations out to 1000m we limited our analysis to 600m. We do not believe this altered our final results in any way.

Data Processing Description

The satellite-scale habitat data processing workflow uses a combination of Bash and Python scripts to calculate habitat metrics and spatial relationships across multiple scales. Designed for use on a Slurm-based high-performance computing cluster, the workflow efficiently handles large spatial datasets through parallelized processing. It is divided into two main phases: preprocessing and scale-specific calculations.

The preprocessing phase, managed by the preprocess_satscale.sh Bash script and executed through the satscale_preprocessing_241022.py Python script, prepares habitat shapefiles and site data for subsequent analyses. Habitat polygons from google2022.shp are reprojected to EPSG:32615 and assigned numerical identifiers for efficient processing. Water and mudflat polygons are consolidated into a single "Water" multipolygon for streamlined analyses. The processed habitat polygons are saved as GeoPackage files (habitat_poly.gpkg, water_poly.gpkg), while site data from drop_field.csv is georeferenced, reprojected, and saved as pffw_sites.gpkg. Wind data from CO-OPS_8761724_met.csv is used to calculate fetch distances for each site based on the prevailing wind direction, determined from average wind vectors. These fetch distances are then integrated into the site dataset.

The scale-specific calculations phase, managed by the permutations_satscale.sh Bash script and the satscale_permutations_241022.py Python script, calculates habitat metrics for various combinations of buffer distances (100–1000 meters) and edge distances (1, 3, 5 meters). Buffers are generated around each site, and habitat polygons are clipped to these buffers to calculate land-to-water ratios, edge proportions (e.g., mangrove and marsh edge lengths), and classifications of sites into "mangrove," "marsh," or "mixed" categories based on habitat thresholds. Fetch distance is also included as a metric for wind and wave exposure. The outputs for each buffer and edge combination are saved as CSV files in the output/satscale directory, with filenames structured as google2022_edge[distance]_buf[distance].csv.

To improve efficiency, the workflow employs a Slurm job array, enabling parallel processing of multiple scale permutations. The Python scripts utilize geopandas for spatial processing and implement memory management to handle large datasets effectively. This workflow plays a critical role in calculating fine-scale habitat metrics required for analyzing species-habitat relationships in estuarine environments. The calculated metrics, including edge proportions, land-to-water ratios, and fetch distances, serve as key predictors for ecological models, supporting analyses of habitat suitability and the impacts of habitat changes such as mangrove encroachment. By optimizing calculations across scales, the workflow ensures accurate and efficient processing, making it integral to the project’s success.