Tools for phenology
There are different tools you can use to collect, derive and analyze phenological information. Nature-loving amateurs find a list of apps and webpages that help in contributing to phenological research. Experts get an overview of tools used for EO data. And if that’s not enough and you want more, learn about furtheR R-tools for phenology.
Phenology needs you! How you can help collecting phenological information
When you contribute to science a an amateur, you are a so-called Citizen Scientist. It means you observe your surroundings and make an invaluable asset to research. In times, when almost everyone is equipped with a smartphone, it’s quite easy to use apps in order to help the scientific community. For phenological monitoring, all you need to do is go outside (on your way to school, university, work or on your Sunday stroll with the beloved), look at the plants, enjoy their colors and submit your observation with one of the following tools:
Nature's Notebook App
The use of smartphone apps makes plant phenological observations especially easy:
- MySeasons (Germany, app on Google Play)
- Naturkalender ZAMG (Austria, app on Google Play and App Store)
- Nature’s Notebook (United States, app on Google Play and App Store)
Other programs engage citizens in collecting scientific information and submitting them online on webpages:
This list was compiled using table 1 “Examples of citizen science programs and their associated Web sites table” in Dickinson et al. 2010 and a search on Google Play and App Store as well as screening Twitter accounts related to #phenology. This list is not comprehensive and if you like to add a tool, let us know via email (contact(at)phenosens.org) or Twitter.
Expert tools for Earth Observation data
If you made your hobbies a profession and you are working with phenology and Earth Observation data, there are a lot of freely available software packages that make your daily tasks easier. Hey tools, Where Art Thou? Here’s a list with short descriptions:
MODIS R package
Where to get it: on CRAN
What it helps you with: Download and processing of Moderate Resolution Imaging Spectroradiometer (MODIS). The package provides automated access to the global online data archives LP DAAC and LAADS and processing capabilities such as file conversion, mosaicking, subsetting and time series filtering. (shortened from package description)
Why it’s worth using: Makes life so easy when you need to download MODIS data. No need to worry about original HDF format. Includes functions to deal with Science Data Sets dates and dates. Comes with filters.
BFAST R package
Where to get it: on CRAN
What it helps you with: BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. (shortened from package description)
Why it’s worth using: Can decompose time series: According to the classical component model, long-term time series can be broken down into the following components: (1) trend (also long-term component), (2) seasonality (also seasonal component) and (3) residual component (short-term component / noise).
greenbrown R package
Where to get it: on R-Forge
What it helps you with: The package provides access to different methods for 1) trend and breakpoint analysis, 2) time series smoothing and interpolation, and 3) analysis of land surface phenology. (shortened from package description)
Why it’s worth using: Includes methods to derive LSP. Smoothing and interpolation can be done in R.
Where to get it: from the TIMESAT page
What it helps you with: TIMESAT is a software package for analysing time-series of satellite sensor data. It was developed to investigate the seasonality of satellite time-series data and their relationship with dynamic properties of vegetation, such as phenology and temporal development. (shortened from TIMESAT webpages)
Why it’s worth using: No R fundamentals needed (although they help to automise and preprocess the data). Includes several approaches for outlier detection and filters for smoothing. Derives a set of 9 seasonality parameters.
Earth Observation Monitor (EOM)
What it helps you with: The web-based Earth Observation Monitor (webEOM) provides easy access and visualization for spatial time-series data. (shortened from the webpages)
Why it’s worth using: Integrates the functionalities of BFAST, greenbrown and TIMESAT in a web service. Comes with a tutorial on YouTube. Easy access, no installation needed. A great tool for teaching purposes!
Going furtheR: More useful R-tools
tools in the context of animal movement / animal phenology
moveVis R package: Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations. Developed by our EAGLE master student Jakob Schwalb-Willmann.
tools in the context of webcams and phenology
phenocamr R package (on GitHub): Phenocamr facilitates the retrieval and processing of PhenoCam time series. Post-processing of PhenoCam data includes outlier removal and the generation of data products such as phenological transition dates. Developed by Koen Hufkens.