https://github.com/alan-turing-institute/monitoring-ecosystem-resilience

Repository for mini-projects in the Data science for Sustainable development project
https://github.com/alan-turing-institute/monitoring-ecosystem-resilience

hut23 hut23-240

Last synced: about 2 months ago
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Repository for mini-projects in the Data science for Sustainable development project

• Host: GitHub
• URL: https://github.com/alan-turing-institute/monitoring-ecosystem-resilience
• Owner: alan-turing-institute
• License: mit
• Created: 2019-07-18T09:23:22.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-05-23T17:16:11.000Z (8 months ago)
• Last Synced: 2024-10-29T22:32:03.439Z (2 months ago)
• Topics: hut23, hut23-240
• Language: Python
• Size: 65.4 MB
• Stars: 22
• Watchers: 8
• Forks: 5
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

Awesome Lists containing this project

• open-sustainable-technology - monitoring-ecosystem-resilience - The focus is understanding vegetation patterns in semi-arid environments. (Biosphere / Plants and Vegetation)

README

        
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# monitoring-ecosystem-resilience

Repository for mini-projects in the Data science for Sustainable development project.

Currently the focus of code in this repository is understanding vegetation patterns in semi-arid environments.

The code in this repository is intended to perform three inter-related tasks:

* Download and process satellite imagery from Google Earth Engine.

* Generate simulated vegetation patterns.

* Calculate graph metrics to quantify the interconnectedness of vegetation in real and simulated images.

### Python

The tasks above are all implemented in Python in the *pyveg* package. See the [README.md](pyveg/README.md) in the `pyveg` subdirectory for details on installation and usage.

### R

The pattern-generation and graph-modelling are implemented in R in the *rveg* package.  See the [README.md](rveg/README.md) in the `rveg` directory for further details.