PolyTrend is a trend detection algorithm (Jamali et al, 2014). It works on pixel-level time-series and classifies trends in vegetation into linear, quadratic, cubic, concealed and no-trend types. Here it makes use of an external repository of satellite images through Google Earth Engine's API. The output is a csv file with pixel coordinates (WGS 1984, EPSG: 4326), trend type, statistical significance of the fit of the polynomial and slope of linear polynomial. Maps with a limited number of pixels can be created in the Jupyter Notebook.
List of scripts:
- Trend_analysis_GEE_API.ipynb imports all needed libraries, lets you set parameters for the image collection and the algorithm, it reduces large image collections to annual NDVI values, using either the maximum or the mean value of NDVI for each year, then splits the images into pixel time-series and applies PolyTrend. To use this script you will need to install Jupyter Notebook first (see SettingUpJupyterNotebook.pdf).
- PolyTrend file is the algorithm that fits polynomials into pixel-level data and does nothing more. You will need your own data in the form of a list of values, minimum 4 (to fit the cubic polynomial), eg. [0.789, 0.756, 0.755, 0.746]
- Colab_trend_analysis_with_GEE.ipynb is a version of PolyTrend adapted to work in Colaboratory with GEE API and without the need to go through Jupyter Notebook installation.
The algorithm is also available in:
- Matlab,
- as a package in RStudio: https://cran.r-project.org/web/packages/PolyTrend/index.html
- as a webtool: http://polytrend.gis.lu.se/
References: Jamali, S., Seaquist, J., Eklundh, L., Ardö, J. (2014). Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote Sensing of Environment, 141, 79–89. DOI: 10.1016/j.rse.2013.10.019.