benchmarkrecovery
Benchmarking recovery indicators from remote sensing time series
Science Score: 67.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 5 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
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○Scientific vocabulary similarity
Low similarity (13.3%) to scientific vocabulary
Repository
Benchmarking recovery indicators from remote sensing time series
Basic Info
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 5
- Releases: 0
Metadata Files
README.md
Benchmarking recovery indicators derived from remote sensing time series
This project simulates Landsat data and evaluates the performance of recovery indicators with respect to data and disturbance characteristics.
Background
The context of this project is the study of the recovery of tropical forests after an abrupt disturbance (typically a forest fire) using satellite images as a data source.
The speed of recovery after a disturbance is known to be correlated with the concept of resilience. This is true not only for forests, but for many dynamical systems. To put it simply: forests that recover fast are more resilient. Forests that recover slowly may be in danger of permanent disappearance.
The specialized literature proposes different metrics for measuring the recovery speed. The performance of these metrics depends on many factors. Some of them are natural, such as the intensity of the perturbation or the seasonality. Others are technical, such as the sampling frequency or the spatial resolution.
Purpose
The purpose of this project is to efficiently compare the reliability of different post-disturbance recovery metrics.
Mechanics
- Infers time series' parameters and characteristics from optical satellite image data
- Uses those parameters to create a large collection of synthetic (but realistic) time series
- Calculates several state-of-the-art recovery metrics
Simplified workflow
Install
You can install the master version from R via:
r
library(devtools)
install_github("RETURN-project/BenchmarkRecovery")
Citation info
Please cite as:
Wanda De Keersmaecker, & Pablo Rodríguez-Sánchez. (2020, December 14). RETURN-project/BenchmarkRecovery: Benchmarking recovery metrics derived from remote sensing time series (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.432050
Citation can be exported to different formats (BibTeX, JSON, ...) with our Zenodo link.
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
License
Owner
- Name: RETURN
- Login: RETURN-project
- Kind: organization
Software repositories of the REcovery of Tropical forest Using Radar seNtinel time series project
Citation (CITATION.cff)
# YAML 1.2
---
authors:
-
affiliation: "Wageningen University"
family-names: "De Keersmaecker"
given-names: Wanda
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affiliation: "Netherlands eScience Center"
family-names: "Rodríguez-Sánchez"
given-names: Pablo
orcid: "https://orcid.org/0000-0002-2855-940X"
cff-version: "1.1.0"
date-released: 2021-05-03
doi: "10.5281/zenodo.4320502"
keywords:
- "time series analysis"
- "time series simulation"
license: "Apache-2.0"
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/RETURN-project/BenchmarkRecovery"
title: BenchmarkRecovery
version: "0.1.0"
...
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Dependencies
- bfast * imports
- colorspace * imports
- forecast * imports
- ggplot2 * imports
- reshape2 * imports
- sde * imports
- strucchange * imports
- zoo * imports
- GGally * suggests
- imputeTS * suggests
- knitr * suggests
- markdown * suggests
- parallel * suggests
- pbapply * suggests
- plyr * suggests
- profvis * suggests
- rmarkdown * suggests
- signal * suggests
- testthat * suggests