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HyperCoast

A Python package for visualizing and analyzing hyperspetral data in coastal regions.
https://github.com/opengeos/hypercoast

Category: Hydrosphere
Sub Category: Coastal and Reefs

Keywords

aviris coastal emit geospatial hyperspectral ipyleaflet ipywidgets leafmap nasa neon pace python

Keywords from Contributors

archiving transforms measure observability climate-model composer optimize profiles conversation simulator

Last synced: about 22 hours ago
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Repository metadata

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

README.md

HyperCoast

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JOSS

A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments

Introduction

HyperCoast is a Python package designed to provide an accessible and comprehensive set of tools for visualizing and analyzing hyperspectral data in coastal environments. Hyperspectral data refers to the information collected by sensors that capture light across a wide range of wavelengths, beyond what the human eye can see. This data allows scientists to detect and analyze various materials and conditions on the Earth's surface with great detail. Unlike multispectral data, which captures light in a limited number of broad wavelength bands (typically 3 to 10), hyperspectral data captures light in many narrow, contiguous wavelength bands, often numbering in the hundreds. This provides much more detailed spectral information. Leveraging the capabilities of popular packages like Leafmap and PyVista, HyperCoast streamlines the exploration and interpretation of complex hyperspectral remote sensing data from existing spaceborne and airborne missions. It is also poised to support future hyperspectral missions, such as NASA's SBG and GLIMR. It enables researchers and environmental managers to gain deeper insights into the dynamic processes occurring in aquatic environments.

HyperCoast supports the reading and visualization of hyperspectral data from various missions, including AVIRIS, NEON, PACE, EMIT, DESIS, PRISMA and ENMAP along with other datasets like ECOSTRESS. Users can interactively explore hyperspectral data, extract spectral signatures, change band combinations and colormaps, visualize data in 3D, and perform interactive slicing and thresholding operations (see Figure 1). Additionally, by leveraging the earthaccess package, HyperCoast provides tools for interactively searching NASA's hyperspectral data. This makes HyperCoast a versatile and powerful tool for working with hyperspectral data globally, with a particular focus on coastal regions.

EMIT
Figure 1. An example of visualizing NASA EMIT hyperspectral data using HyperCoast.

Citations

If you find HyperCoast useful in your research, please consider citing the following papers to support us. Thank you!

  • Liu, B., & Wu, Q. (2024). HyperCoast: A Python Package for Visualizing and Analyzing Hyperspectral Data in Coastal Environments. Journal of Open Source Software, 9(100), 7025. https://doi.org/10.21105/joss.07025.

Features

  • Searching for NASA hyperspectral data interactively
  • Performing atmospheric correction using Acolite
  • Interactive visualization and analysis of hyperspectral data, such as AVIRIS, DESIS, EMIT, PACE, NEON AOP, Tanager, PRISMA and ENMAP
  • Interactive visualization of NASA ECOSTRESS data
  • Interactive visualization of PACE chlorophyll-a data
  • Interactive extraction and visualization of spectral signatures
  • Changing band combinations and colormaps interactively
  • Visualizing hyperspectral data in 3D
  • Visualizing ERA5 temperature data in 3D
  • Interactive slicing and thresholding of hyperspectral data in 3D
  • Saving spectral signatures as CSV files

QGIS Plugin

HyperCoast also provides a QGIS plugin for visualizing and analyzing hyperspectral data, including EMIT, PACE, DESIS, NEON, AVIRIS, PRISMA, EnMAP, Tanager, and Wyvern datasets.

To install the QGIS plugin, please follow the instructions in the QGIS Plugin README.

Check out this short video demo and full video tutorial on how to use the HyperCoast plugin in QGIS.

youtube-video

Demos

  • Visualizing hyperspectral data in 3D (notebook)

Cube

  • Interactive slicing of hyperspectral data in 3D (notebook)

Slicing

  • Interactive thresholding of hyperspectral data in 3D (notebook)

Slicing

  • Visualizing ERA5 temperature data in 3D (notebook)

ERA5

  • Changing band combinations and colormaps interactively (notebook)

colormap

AVIRIS

DESIS

  • Visualizing NASA EMIT hyperspectral data interactively (notebook)

EMIT

  • Visualizing NASA PACE hyperspectral data interactively (notebook)

PACE

NEON

  • Interactive visualization of PACE chlorophyll-a data (notebook)

Chla

Acknowledgement

The HyperCoast project draws inspiration from the nasa/EMIT-Data-Resources repository. Credits to the original authors. We also acknowledge the NASA EMIT program support through grant no. 80NSSC24K0865.

License

HyperCoast is released under the MIT License. However, some of the modules in HyperCoast adapt code from other open-source projects, which may have different licenses. Please refer to the license notice in each module for more information. Credits to the original authors.

Contributors

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
    - family-names: Liu
      given-names: Bingqing
      orcid: "https://orcid.org/0000-0003-4651-6996"
    - family-names: Wu
      given-names: Qiusheng
      orcid: "https://orcid.org/0000-0001-5437-4073"
doi: 10.5281/zenodo.13368024
message: If you use this software, please cite our article in the
    Journal of Open Source Software.
preferred-citation:
    authors:
        - family-names: Liu
          given-names: Bingqing
          orcid: "https://orcid.org/0000-0003-4651-6996"
        - family-names: Wu
          given-names: Qiusheng
          orcid: "https://orcid.org/0000-0001-5437-4073"
    date-published: 2024-08-26
    doi: 10.21105/joss.07025
    issn: 2475-9066
    issue: 100
    journal: Journal of Open Source Software
    publisher:
        name: Open Journals
    start: 7025
    title: "HyperCoast: A Python Package for Visualizing and Analyzing
        Hyperspectral Data in Coastal Environments"
    type: article
    url: "https://joss.theoj.org/papers/10.21105/joss.07025"
    volume: 9
title: "HyperCoast: A Python Package for Visualizing and Analyzing
    Hyperspectral Data in Coastal Environments"

Owner metadata

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Committers metadata

Last synced: 8 days ago

Total Commits: 221
Total Committers: 11
Avg Commits per committer: 20.091
Development Distribution Score (DDS): 0.208

Commits in past year: 62
Committers in past year: 7
Avg Commits per committer in past year: 8.857
Development Distribution Score (DDS) in past year: 0.371

Name Email Commits
Qiusheng Wu g****s@g****m 175
pre-commit-ci[bot] 6****] 16
dependabot[bot] 4****] 10
allcontributors[bot] 4****] 7
Bingqing Liu 1****u 4
Advait Dhamorikar 9****0 3
arfy slowy s****y@p****e 2
Ian Black b****a@o****u 1
Guillermo E. Ponce-Campos g****e@a****t 1
Bingqing Liu 1****7 1
Alex Leith a****h@g****m 1

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Issue and Pull Request metadata

Last synced: 9 days ago

Total issues: 29
Total pull requests: 256
Average time to close issues: 6 days
Average time to close pull requests: 1 day
Total issue authors: 11
Total pull request authors: 11
Average comments per issue: 2.03
Average comments per pull request: 1.0
Merged pull request: 232
Bot issues: 0
Bot pull requests: 51

Past year issues: 4
Past year pull requests: 41
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 3 days
Past year issue authors: 4
Past year pull request authors: 6
Past year average comments per issue: 2.5
Past year average comments per pull request: 0.83
Past year merged pull request: 32
Past year bot issues: 0
Past year bot pull requests: 17

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Package metadata

proxy.golang.org: github.com/opengeos/HyperCoast

proxy.golang.org: github.com/opengeos/hypercoast

pypi.org: hypercoast

A Python package for processing hyperspectral data in coastal regions

  • Homepage: https://github.com/opengeos/HyperCoast
  • Documentation: https://hypercoast.readthedocs.io/
  • Licenses: MIT License
  • Latest release: 0.18.0 (published 13 days ago)
  • Last Synced: 2025-12-18T04:05:40.435Z (8 days ago)
  • Versions: 51
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 3,488 Last month
  • Rankings:
    • Dependent packages count: 9.58%
    • Average: 36.391%
    • Dependent repos count: 63.202%
  • Maintainers (2)

Dependencies

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Score: 16.044490014151528