BFAST GPU

This document provides usage instructions for the GPU implementation of BFAST, here implemented as a Jupyter dashboard on SEPAL.

Introduction

Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances such as deforestation events across the earth surface. Typical scenarios require the analysis of billions of image patches/pixels, which can become very expensive from a computational point of view. The bfast package provides an efficient massively-parallel implementation for one of the state-of-the-art change detection methods called Breaks For Additive Season and Trend (BFASTmonito) <http://bfast.r-forge.r-project.org> proposed by Verbesselt et al.

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/bfastmonitor_1.png

the analysis perfomed pixel wise

The implementation is based on OpenCL and allows to process large-scale change detection scenarios given satellite time series data. The optimizations made are tailored to the specific requirements of modern massively-parallel devices such as graphics processing units (GPUs). The full documentation of the bfast package can be found here.

Usage

Warning

prerequist: before launching the bfast module, you need to have at least 1 time series in SEPAL folders

Set up

To launch the app please follow the SEPAL registration steps. Then open a GPU instance in your terminal (g4`or :code:`g8). Then move to the SEPAL Apps dashboard (purple wrench icon on the left side panel), search for and click on bfast GPU.

The application should launch itself in the BFAST process section. On the left side the navdrawer will help you navigate between the different pages of the app. If you click on wiki, bug report or code source, you will be redirected to the corresponding webpage.

Note

The launching process can take several minutes

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/full_app.png

The landing page of bfast GPU

Select folder

Select a folder in using the first widget. Navigate in your folders to find the time series foder you want to analyse. click outside the popup to exit the selection. Your folder should only contain folders with numbered name (corresponding to each tile of the TS).

By selecting an appropriate folder some widget will be automatically filled and unable:

  • output directory name: filled with the basename of your TS folder

  • select tiles to use: preselect all the available tiles

  • monitoring dates: the slider is unabled and filled with the date list included in the TS folder. The values are defaulted to the full range of dates.

  • historical period: The slider is unabled and filled with the date list included in the TS folder. The value is defaulted to the first date of the TS.

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/correct_folder.png

Select a folder and preload all the informations

Warning

If your selected folder does not meet the requirements of a SEPAL TS folder, the prefilled inputs will be emptied and disabled and you will be notified in 2 spots (in the input and in the warning banner) that the folder is not set. Change to an appropriate foldeer to see these messages disapear.

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/wrong_folder.png

The error messages if a wrong folder is provided

Parameters

Now you can change the parameters to fit the requirements of your analysis:

  • output directory name: this name will be used to store all your analysis. It is automatically filled with the basename of your TS folder but you can still change it.

    Note

    The name of your folder can only contain alphanumeric characters and no special characters (like space). If you try to use them the name will be sanitized automatically.

  • select tiles to use: This is the tiles that you want to use in your analysis. they default to all but you can deselect the one that you don’t need.

  • number of harmonic: Specifies the order of the harmonic term, defaulting to 3.

  • frequency of seasonal model: The frequency for the seasonal model set here in month

  • add trend: Whether a trend offset term shall be used or not

  • Monitoring dates: The year that marks the end of the historical period and the start of the monitoring period. The default value does not let enough images in the historical stable perdiod

    Danger

    If you let less than 40 images between the start of the historical stable period and the start of the monitoring, the program will send you a warning. You will still be able to launch the process but the result will be very uncertain has not enough image were provided to build an accurate model.

    https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/too_short.png
  • History start date: Specifies the start of the stable history period

Advanced parameter

Tip

this parameters are for advanced user only, the default value our team set for you already give accurate results in many situation

Click on advanced parameter and a new panel of options will be available:

  • bandwith relative to sample size: Float in the interval (0,1) specifying the bandwidth relative to the sample size in the MOSUM/ME monitoring processes.

  • Significance level of the monitoring: Significance level of the monitoring (and ROC, if selected) procedure, i.e., probability of type I error.

  • backend: Specifies the implementation that shall be used: Python resorts to the non-optimized Python version; OpenCL resorts to the optimized massively-parallel OpenCL implementation.

    Note

    If before starting the application you didn’t start a GPU instance, the OpenCL backend will be disabled as no GPU are available on your machine. Please close the app and your previous instance and start a g4 or g8. If you run the application on a GPU machine the default backend is OpenCL.

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/advance_params.png

advanced parameters list

Run process

You can now click on LAUNCH BFAST ANALYSIS to start the process.

The process wil start shortly notifying you of it’s advancment tile by tyle in the info banner as shown on the following image.

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/advancement.png

process currently runnning

Danger

Closing the app will shut down the Python kernel that runs underneath and thus stop your process. In it’s current implementation the app should stay open to run

Tip

If your connection to SEPAL is lost and the application stop, use the exact same parameters as in your previous analysis, the application will find back the already computed tiles and images and will start from where it stops instead of restarting from scratch

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/computation_end.png

End of computation screen

The module provide the following .vrt outputs: - ~/module_results/bfast/[name_of_input]/[bfast_params]/bfast_outputs.vrt

It is a 3 band raster with :

  • band 1 being the breakpoints in decimal year format

  • band 2, the magnitude of change

  • band 3, the validity of the pixel model

This raster has the exact same dimension as the input raste`

Example

Here you’ll find an example of this 2 bands over the Juaboso Region in Ghana whit a monitoring period between 2017 and 2019:

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/utils/magnitude.png

Magnitude display with the magma colormap, values in [-624, 417]

https://raw.githubusercontent.com/12rambau/bfast_gpu/master/utils/breaks.png

Breaks masked in the center of the region. displayed with a viridis colormap, values in [2017.26, 2019.98]