spectral-recovery
Spectral recovery analysis for forested ecosystems in Python. Part of the PEOPLE-ER project.
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
1 of 9 committers (11.1%) from academic institutions -
○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (13.1%) to scientific vocabulary
Keywords
ecological-restoration
ecosystem-recovery
eo
esa
package
people-er
python3
remote-sensing
Last synced: 4 months ago
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JSON representation
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Repository
Spectral recovery analysis for forested ecosystems in Python. Part of the PEOPLE-ER project.
Basic Info
- Host: GitHub
- Owner: PEOPLE-ER
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://www.people-er.info/
- Size: 189 MB
Statistics
- Stars: 10
- Watchers: 3
- Forks: 2
- Open Issues: 4
- Releases: 9
Topics
ecological-restoration
ecosystem-recovery
eo
esa
package
people-er
python3
remote-sensing
Created almost 3 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Changelog
Contributing
License
Code of conduct
Citation
README.md
spectral-recovery
:artificial_satellite::evergreen_tree::chart_with_upwards_trend: supporting ecosystem restoration through spectral recovery analysis :chart_with_upwards_trend::evergreen_tree::artificial_satellite:
[](https://badge.fury.io/py/spectral-recovery)
[](https://mybinder.org/v2/gh/PEOPLE-ER/spectral-recovery/HEAD?labpath=docs%2Fnotebooks%2F)
[](https://github.com/psf/black)
Github: https://github.com/PEOPLE-ER/spectral-recovery/
Documentation: https://people-er.github.io/spectral-recovery/
PyPi: https://pypi.org/project/spectral-recovery/
Overview
spectral-recovery is an open-source project and Python package that provides simple, centralized, and reproducible methods for performing spectral recovery analysis to support Ecosystem Restoration (ER) efforts in forested ecosystems.
The package provides straight-forward interfaces and supplementary documentation to encourage the use of well-founded remote sensing techniques in ER research and projects. To get started, users provide restoration site locations, the years of disturbance and restoration, and annual composites of spectral data. spectral-recovery handles the rest!
See Quick Start or our interactive notebooks to dive right in, (in-progress) tutorials for detailed instructions, or the theoretical basis for in-depth information.
Installation
bash
pip install spectral-recovery
Quick Start
```python import spectralrecovery as sr from spectralrecovery import data
Read in timeseries data
spectralts = sr.readtimeseries( pathtotifs=data.bc06wildfirelandsatbaptimeseries(), band_names={1: "blue", 2: "green", 3: "red", 4: "nir", 5: "swir16", 6: "swir22"}, )
Compute indices
indexts = sr.computeindices( timeseriesdata=spectralts, indices=["NBR", "NDVI"], )
Read in restoration site(s)
restsite = sr.readrestorationsites( path=data.bc06wildfirerestorationsite(), distrestyears={0: [2005, 2006]}, )
Compute recovery target for restoration site
medianhist = sr.recoverytargets.historic.median( timeseriesdata=indexts, restorationsites=restsite, referencestart=2003, referenceend=2005, scale="pixel", )
Compute recovery metrics for restoration site!
metrics = sr.computemetrics( metrics=["Y2R", "R80P", "YrYr", "deltaIR", "RRI"], timeseriesdata=indexts, restorationsites=restsite, recoverytargets=median_hist, )
Inspect recovery metrics for the restoration site (site 0)
e.g what is the site's mean R80P (porportion of 80% of the recovery target)?:
metrics[0].sel(metric="R80P").mean().compute()
Or, write results out to a TIF:
metrics[0].sel(metric="Y2R").rio.toraster("site0y2r.tif")
```
Documentation
- View background information, static tutorials, and API references in our project documentation.
- Try out an interactive notebook:
Contributing
- Report bugs, suggest features, and see what others are saying on our GitHub Issues page.
- Start discussions about the tool on our discussion page.
- Want to contribute code? See our CONTRIBUTING document for more information.
How to Cite
Publication in progress. For now, when using this tool in your work we ask that you acknowledge as follows:
"spectral-recovery method developed in the PEOPLE-ER Project, managed by Hatfield Consultants, and financed by the European Space Agency."
License
Copyright 2023 Hatfield Consultants LLP
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Owner
- Name: Pioneer Earth Observation apPlications for the Environment - Ecosystem Restoration (PEOPLE-ER)
- Login: PEOPLE-ER
- Kind: organization
- Location: Canada
- Website: https://www.people-er.info
- Repositories: 1
- Profile: https://github.com/PEOPLE-ER
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
title: spectral-recovery
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Sarah Vaughan
family-names: Zwiep
email: sara.zwiep@ubc.ca
affiliation: University of British Columbia
orcid: 'https://orcid.org/0000-0002-0812-9509'
- given-names: Marcos
family-names: Kavlin-Castaneda
orcid: 'https://orcid.org/0009-0009-2239-2360'
email: mkavlin@hatfieldgroup.com
affiliation: Hatfield Consultants
- given-names: Melissa
family-names: Birch
affiliation: University of British Columbia
email: melissa.birch@ubc.ca
repository-code: 'https://github.com/PEOPLE-ER/Spectral-Recovery'
url: 'https://people-er.github.io/Spectral-Recovery/'
abstract: >-
Aimed at supporting Ecosystem Restoration (ER) efforts
in forested ecosystems, spectral-recovery provides simple
and repeatable methods for using spectral recovery analysis
to derive recovery metrics for individual restoration sites.
keywords:
- Remote Sensing
- Ecosystem Restoration
- Earth Observation
- Ecology
- Conservation
- Biodiversity
license: Apache-2.0
commit: edd540c0a4527c135963011f202feead2882b92f
version: 0.4.1
date-released: '2024-04-16'
GitHub Events
Total
- Watch event: 2
- Fork event: 1
Last Year
- Watch event: 2
- Fork event: 1
Committers
Last synced: almost 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| szwiep | s****p@g****m | 276 |
| szwiep | s****p@s****a | 95 |
| Sarah Zwiep | 5****p | 28 |
| marcos | m****s@m****m | 25 |
| szwiep | $****R@g****a | 11 |
| Marcos | 5****n | 8 |
| Zwiep | s****p@u****a | 6 |
| melissabirch | 1****h | 6 |
| Ben | B****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 54
- Total pull requests: 105
- Average time to close issues: about 1 month
- Average time to close pull requests: 3 days
- Total issue authors: 2
- Total pull request authors: 4
- Average comments per issue: 0.41
- Average comments per pull request: 0.14
- Merged pull requests: 100
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 8
- Pull requests: 26
- Average time to close issues: about 1 month
- Average time to close pull requests: about 3 hours
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 0.13
- Average comments per pull request: 0.0
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- szwiep (50)
- KavlinClein (4)
Pull Request Authors
- szwiep (160)
- KavlinClein (4)
- melissabirch (1)
Top Labels
Issue Labels
enhancement (9)
bug (4)
documentation (3)
high priority (2)
question (1)
good first issue (1)
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 41 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 16
- Total maintainers: 1
pypi.org: spectral-recovery
- Documentation: https://spectral-recovery.readthedocs.io/
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-
Latest release: 1.0.1
published about 1 year ago
Rankings
Dependent packages count: 7.3%
Average: 37.9%
Dependent repos count: 68.4%
Maintainers (1)
Last synced:
4 months ago
Dependencies
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requirements.txt
pypi
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