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mapping-flowering-dynamics

This repository comprises notebooks and files to perform a two-step framework for mapping and tracking flowering dynamics from hyperspectral datasets.

https://github.com/yoselineangel/mapping-flowering-dynamics

Science Score: 57.0%

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Repository

This repository comprises notebooks and files to perform a two-step framework for mapping and tracking flowering dynamics from hyperspectral datasets.

Basic Info
  • Host: GitHub
  • Owner: yoselineangel
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 50.2 MB
Statistics
  • Stars: 11
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 1
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Mapping-flowering-dynamics

This repository comprises notebooks and files to perform a two-step framework for mapping and tracking flowering dynamics from hyperspectral datasets.

Summary

A single pixel may contain several individual plants of different species, flowers, soil, and shadows with highly variable fractional coverage of the canopy area. You can find a Jupyter notebook comprising a two-step framework for mapping and tracking flowering dynamics from hyperspectral datasets:

  • Spectral mixture residual analysis
  • Unsupervised clustering based on the Gaussian mixture model (GMM)

We implemented the workflow on an open cloud computing environment (e.g., Amazon Web Service -AWS) that couples various Python libraries for imagery storage (e.g., Zarr), access (e.g., Intake), and managing multi-dimension arrays (e.g., Xarray), as described in (Lang et al., 2023). In addition, to perform the data analysis, we adapted the publicly available code posted by Sousa et al., 2022 to retrieve the mixture residual and the Gaussian Mixture module from the Scikit-learn library (Pedregosa et al., 2011) integrated with some visualization tools (e.g., hvPlot, Bokeh, matplotlib).

References

Lang, E., Angel, Y., & Shiklomanov, A. N. (2023). SHIFT SMCE User Guide (v2.0.2). Zenodo. https://doi.org/10.5281/zenodo.7864544

Sousa, D., Brodrick, P., Cawse-Nicholson, K., Fisher, J. B., Pavlick, R., Small, C., & Thompson, D. R. (2022). The Spectral Mixture Residual: A Source of Low-Variance Information to Enhance the Explainability and Accuracy of Surface Biology and Geology Retrievals. Journal of Geophysical Research: Biogeosciences, 127(2), e2021JG006672. https://doi.org/10.1029/2021JG006672

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825, https://doi.org/10.48550/arXiv.1201.0490

Owner

  • Name: Yoseline Angel
  • Login: yoselineangel
  • Kind: user
  • Location: Maryland, USA

Associate Postdoc at NASA Goddard Space Flight Center / University of Maryland - College Park

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Yoseline
    family-names: Angel
    email: yoseline.b.angellopez@nasa.gov
    orcid: 'https://orcid.org/0000-0002-8377-8736'
    affiliation: NASA Goddard Space Flight Center / University of Maryland College Park - ESSIC
  - given-names: Evan
    family-names: Lang
    email: evan.d.lang@nasa.gov
    orcid: 'https://orcid.org/0009-0000-6683-7231'
    affiliation: NASA Goddard Space Flight Center / Science Systems and Applications Incorporated
  - given-names: Alexey N
    family-names: Shiklomanov
    email: alexey.shiklomanov@nasa.gov
    affiliation: NASA Goddard Space Flight Center
    orcid: 'https://orcid.org/0000-0003-4022-5979'
identifiers:
  - type: doi
    value: 10.5281/zenodo.7901619
repository-code: >-
  https://github.com/yoselineangel/Mapping-flowering-dynamics
abstract: >-
  This repository comprises some ancillary data and a
  Jupyter notebook including a two-step framework for
  mapping and tracking flowering dynamics from hyperspectral
  datasets.
  -Spectral mixture residual analysis
  -Unsupervised clustering based on the Gaussian mixture
  model (GMM)
  We implemented the workflow on an open cloud computing
  environment (e.g., Amazon Web Service -AWS) that couples
  various Python libraries for imagery storage (e.g., Zarr),
  access (e.g., Intake), and managing multi-dimension arrays
  (e.g., Xarray), as described in (Lang et al., 2023). In
  addition, to perform the data analysis, we adapted the
  publicly available code posted by Sousa et al., 2022 to
  retrieve the mixture residual and the Gaussian Mixture
  module from the Scikit-learn library (Pedregosa et al.,
  2011) integrated with some visualization tools (e.g.,
  hvPlot, Bokeh, matplotlib).
keywords:
  - Hyperspectral
  - Imaging spectroscopy
  - Unmixing
  - Flowering
  - AVIRIS
  - Gaussian mixture
title: "Mapping flowering workflow"
license: GPL-3.0-or-later
version: v1.0.0
doi: 10.5281/zenodo.7901619
date-released: '2023-05-05'

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