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

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

Last synced: about 6 hours ago
JSON representation

Repository metadata

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

Host: GitHub
URL: https://github.com/opengeos/hypercoast
Owner: opengeos
License: mit
Created: 2024-04-08T16:01:06.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2025-02-15T00:24:07.000Z (2 months ago)
Last Synced: 2025-04-22T08:02:52.706Z (5 days ago)
Topics: aviris, coastal, emit, geospatial, hyperspectral, ipyleaflet, ipywidgets, leafmap, nasa, neon, pace, python
Language: Python
Homepage: https://hypercoast.org
Size: 61.1 MB
Stars: 172
Watchers: 5
Forks: 29
Open Issues: 3
Releases: 38
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.md
- License: LICENSE
- Citation: CITATION.cff

HyperCoast

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

Free software: MIT License
Documentation: https://hypercoast.org

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, and DESIS, 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
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

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

Visualizing NASA AVIRIS hyperspectral data interactively (notebook)

AVIRIS

Visualizing DESIS hyperspectral data interactively (notebook)

DESIS

Visualizing NASA EMIT hyperspectral data interactively (notebook)

EMIT

Visualizing NASA PACE hyperspectral data interactively (notebook)

PACE

Visualizing NEON AOP hyperspectral data interactively (notebook)

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.

emit.py: Part of the code is adapted from the nasa/EMIT-Data-Resources repository, which is released under the Apache License 2.0.
aviris.py: Part of the code is adapted from the jjmcnelis/aviris-ng-notebooks, which is released under the MIT License.

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

Name: Open Geospatial Solutions
Login: opengeos
Email: [email protected]
Kind: organization
Description: A collection of open-source software packages for the geospatial community
Website: https://opengeos.github.io
Location:
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/129896036?v=4
Repositories: 1
Last ynced at: 2023-04-06T18:19:50.442Z
Profile URL: https://github.com/opengeos

GitHub Events

Total

Create event: 7
Issues event: 3
Release event: 1
Watch event: 24
Delete event: 6
Issue comment event: 11
Push event: 26
Pull request event: 14
Fork event: 4

Last Year

Create event: 7
Issues event: 3
Release event: 1
Watch event: 24
Delete event: 6
Issue comment event: 11
Push event: 26
Pull request event: 14
Fork event: 4

Committers metadata

Last synced: 7 days ago

Total Commits: 163
Total Committers: 9
Avg Commits per committer: 18.111
Development Distribution Score (DDS): 0.153

Commits in past year: 151
Committers in past year: 8
Avg Commits per committer in past year: 18.875
Development Distribution Score (DDS) in past year: 0.146

Name	Email	Commits
Qiusheng Wu	g**s@g**m	138
pre-commit-ci[bot]	6****]	7
allcontributors[bot]	4****]	6
dependabot[bot]	4****]	4
Bingqing Liu	1****u	3
arfy slowy	s**y@p**e	2
Guillermo E. Ponce-Campos	g**e@a**t	1
Bingqing Liu	1****7	1
Alex Leith	a**h@g**m	1

Committer domains:

arstucson.net: 1
proton.me: 1

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 35
Total pull requests: 160
Average time to close issues: 3 days
Average time to close pull requests: about 21 hours
Total issue authors: 9
Total pull request authors: 8
Average comments per issue: 3.0
Average comments per pull request: 1.22
Merged pull request: 154
Bot issues: 0
Bot pull requests: 27

Past year issues: 33
Past year pull requests: 139
Past year average time to close issues: about 22 hours
Past year average time to close pull requests: 1 day
Past year issue authors: 9
Past year pull request authors: 8
Past year average comments per issue: 3.15
Past year average comments per pull request: 1.27
Past year merged pull request: 133
Past year bot issues: 0
Past year bot pull requests: 25

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/opengeos/hypercoast

Top Issue Authors

giswqs (16)
platipodium (8)
ohadshapira (4)
iacallejas (2)
smcclatchie (1)
gponce-ars (1)
sonicviz (1)
jessjaco (1)
Tartomas (1)

Top Pull Request Authors

giswqs (119)
allcontributors[bot] (12)
dependabot[bot] (8)
pre-commit-ci[bot] (7)
bingqing-liu (5)
slowy07 (5)
gponce-ars (2)
alexgleith (2)

Top Issue Labels

bug (15)

Top Pull Request Labels

dependencies (8)
ready-to-merge (5)
github_actions (4)
python (4)
already reviewed (3)

Package metadata

Total packages: 1
Total downloads:
- pypi: 2,157 last-month
Total dependent packages: 0
Total dependent repositories: 0
Total versions: 38
Total maintainers: 2

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: 0.10.0 (published 5 months ago)
Last Synced: 2025-04-25T20:02:18.688Z (1 day ago)
Versions: 38
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 2,157 Last month
Rankings:
- Dependent packages count: 9.58%
- Average: 36.391%
- Dependent repos count: 63.202%
Maintainers (2)
- giswqs
- bingqingliu

Dependencies

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pyproject.toml pypi

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numpy *

requirements_dev.txt pypi

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wheel * development

Score: 15.039410981774541

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HyperCoast

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