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https://github.com/eco-hydro/phenofit
R package: A state-of-the-art Vegetation Phenology extraction package, phenofit
https://github.com/eco-hydro/phenofit
phenology remote-sensing
Last synced: about 2 months ago
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R package: A state-of-the-art Vegetation Phenology extraction package, phenofit
- Host: GitHub
- URL: https://github.com/eco-hydro/phenofit
- Owner: eco-hydro
- License: gpl-2.0
- Created: 2018-04-23T08:40:14.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-23T06:46:03.000Z (6 months ago)
- Last Synced: 2024-04-26T21:31:45.399Z (2 months ago)
- Topics: phenology, remote-sensing
- Language: R
- Homepage: http://phenofit.top
- Size: 90.6 MB
- Stars: 67
- Watchers: 6
- Forks: 31
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Lists
- Awesome-Geospatial - phenofit - A state-of-the-art Vegetation Phenology extraction package. (R)
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
fig.width = 10, fig.height = 5,
fig.align = "center",
fig.path = "man/Figure/",
dev = 'svg'
)
```
# phenofit
[](https://github.com/eco-hydro/phenofit/actions)
[](https://app.codecov.io/gh/eco-hydro/phenofit)
[](http://www.gnu.org/licenses/gpl-2.0.html)
[](https://cran.r-project.org/package=phenofit)
[](https://cran.r-project.org/package=phenofit)
[](https://cran.r-project.org/package=phenofit)
[](https://doi.org/10.5281/zenodo.5150204)
A state-of-the-art **remote sensing vegetation phenology** extraction package: `phenofit`
- `phenofit` combine merits of TIMESAT and phenopix
- A simple and stable growing season dividing methods was proposed
- Provide a practical snow elimination method, based on Whittaker
- 7 curve fitting methods and 4 phenology extraction methods
- We add parameters boundary for every curve fitting methods according to their ecological meaning.
- `optimx` is used to select best optimization method for different curve fitting methods.
***Task lists***
- [x] Test the performance of `phenofit` in multiple growing season regions (e.g. the North China Plain);
- [ ] Uncertainty analysis of curve fitting and phenological metrics;
- [x] shiny app has been moved to [phenofit.shiny](https://github.com/eco-hydro/phenofit.shiny);
- [x] Complete script automatic generating module in shinyapp;
- [x] `Rcpp` improve double logistics optimization efficiency by 60%;
- [x] Support spatial analysis;
- [x] Support annual season in curve fitting;
- [x] flexible fine fitting input ( original time-series or smoothed time-series by rough fitting).
- [x] Asymmetric of Threshold method
# Installation
You can install phenofit from github with:
```{r gh-installation, eval = FALSE}
# install.packages("remotes")
remotes::install_github("eco-hydro/phenofit")
```
# Note
Users can through the following options to improve the performance of phenofit in multiple growing
season regions:
- Users can decrease those three parameters `nextend`, `minExtendMonth` and
`maxExtendMonth` to a relative low value, by setting option
`set_options(fitting = list(nextend = 1, minExtendMonth = 0, maxExtendMonth = 0.5))`.
- Use `wHANTS` as the rough fitting function. Due to nature of fourier functions,
`wHANTS` is more stable for multiple growing seasons, but it is less flexible
than `wWHIT.` `wHANTS` is suitable for regions with the static growing season
pattern accoss multiple years, `wWHIT` is more suitable for regions with the
dynamic growing season pattern.
Dynamic growing season pattern is the most challenging task, which also means
that large uncertainty might be exists.
- Use only one iteration in fine fitting procedure.
# **References**
> [1] Kong, D., McVicar, T. R., Xiao, M., Zhang, Y., Peña-Arancibia, J. L., Filippa, G., Xie, Y., Gu, X. (2022). phenofit: An R package for extracting vegetation phenology from time series remote sensing. *Methods in Ecology and Evolution*, 13, 1508-1527. https://doi.org/10.1111/2041-210X.13870
> [2] Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. *Journal of Geophysical Research: Biogeosciences*, 125(8), e2020JG005636. https://doi.org/10.1029/2020JG005636
>
> [3] Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. *ISPRS Journal of Photogrammetry and Remote Sensing*, 155, 13–24.
>
> [4] Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, `phenofit` version 0.3.1,
>
> [5] Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417.
# Acknowledgements
Keep in mind that this repository is released under a GPL2 license, which permits commercial use but requires that the source code (of derivatives) is always open even if hosted as a web service.