mapping-flowering-dynamics - Mapping_flowering_workflow

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).

What's Changed

• Create LICENSE by @yoselineangel in https://github.com/yoselineangel/Mapping-flowering-dynamics/pull/1

New Contributors

• @yoselineangel made their first contribution in https://github.com/yoselineangel/Mapping-flowering-dynamics/pull/1
• @EvanDLang

Full Changelog: https://github.com/yoselineangel/Mapping-flowering-dynamics/commits/v1.0.0

- Jupyter Notebook
Published by yoselineangel almost 3 years ago