Step by Step: Recommended Practice: Drought monitoring using the Standard Vegetation Index (SVI)

Data Access: 

The step-by-step procedure for drought monitoring using the Standard Vegetation Index (SVI) is available to follow using R-Studio and Python. These two methods were developed to give the user flexibility to choose which of the open source tools is more practical and convenient.

Please click on the icons below to see the corresponding step-by-step procedure. 

           
Image: The R Foundation/CC-BY-SA 4.0 Logo: Python Software Foundation/GPL

 

Note about the Step-by-Step procedures available:

The method originally developed by UFSM in Brazil was applied to the programming languages R and Python and gradually updated:

  • The original recommended practice in R retrieves the EVI input data from USGS Earth Explorer and has to make use of the Modis Reprojection Tool (MRT) to convert the HDF dataformat (downloaded from USGS Earth Explorer) to geotiff. Furthermore, it does not include a cloud mask. We encourage our users to apply the up-to-date versions available with instructions by clicking on the buttons above. For those that would like to access the option with the Modis Reprojection tool the script is available here
  • Following the launch of AppEEARS (a more efficient way to access geospatial data provided by USGS), the updated R script does not need to include the Modis Reprojection Tool (MRT) as MODIS data now can be downloaded directly in geotiff format. The updated R script further applies a cloud mask on the analyzed scene using the Pixel Reliability Quality Assurance (QA) layer also downloaded via AppEEARS.
  • As the updated R script has shown to not be able to compute large areas (above 200 000 km2), an additional R script has been provided.
  • The procedure has also been made available in Python version to compute the SVI which has been shown to be very efficient and capable of handling both small and large data sets. The Python script, accessible as a Jupiter Notebook, also includes a cloud mask.
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