1.0 Explanation of the ANALYSE blocks in the Toolbox

Modified on Thu, 19 Jan 2023 at 03:08 PM

The "ANALYSE" part of the tool box is where you can modify or extract information from the data you selected in "INPUT".

Instructions for using the analytics

To use one of the analytics blocks, you have to drag it into a data block.  Note that not all analytics will work with every block. For example, there is no need to use a CLOUD FILTER on radar data, as there are no clouds in radar imagery. 

The following sections describe how to use each of the available blocks. 



  • This is one of the most used blocks.
  • It allows you to look at a sequence of map images over time as an animation.
  • You place it within a data block like this:

  • The drop-down allows you to select the interval of the time series: i.e. the time between each image in the animation.
  • The block will create an image for every time interval, starting from the first date until the last date selected.
  • The options are: yearly, monthly, weekly, or daily.  Note that some time intervals don't make sense to apply. For example, Sentinel data tends to be weekly, so that choosing a daily time interval will give you lots of blanks frames.
  • Be careful to consider how many scenes are being processed.  If you choose daily intervals for a 12 month period, that is 365 scenes, so it may take a long time to process. 


  • This block quantifies the occurrence of an event, such as fire, in terms of which landcover class they occur on.
  • At the moment, the COMPARISON block only works with the FIRE block.
  • Connect the yellow COMPARISON block into the blue FIRE block, like this:
  • Notice that here an "other data visualisation" block has been added, and a "table" block to generate dashboard output. 
  • To run the comparison, choose a landcover map from the drop-down menu, then...
  • ...select which landcover classes you want to use.
  • When you press RUN WORKFLOW the dashboard will show a table of the landcover classes that have experience fires during the selected period.


  • Classification finds regions within a data set that have the same spectral properties.
  • It can be used to map crop types, extract urban areas from data, or identify areas of deforestation, amongst other applications.
  • A complete description of how to use this block is given in this article on How to apply image classification (supervised or unsupervised).


  • There are 5 different index blocks:

  • NDVI, NDWI, NDSI and NBI only work in combination with the Landsat and Sentinel 2 data. For example:

  • SPI only works in combination with the precipitation block. For Example:

  • The four main indices are:
    • NDVI is the Normalised Difference Vegetation Index and is a measure of vegetation cover and health, useful for looking at vegetation changes over time. This uses the Red and Near Infra-Red bands.
    • NDWI is the Normalised Difference Water Index and indicator of water content in open water bodies. This uses the Green and Near Infra-Red bands.
    • NDSI is the Normalised Difference Snow Index and indicates where there is snow cover. This uses the Green and Shortwave Infra-Red bands.
    • NBI is the Normalised Burn Index and is used to identify areas of burnt vegetation. It is also known as the Normalised Burn Ratio (NBR). This uses the Near Infra-Red and Shortwave Infra-Red bands.
  • They all require the other visualisation block to view the output. 
  • SPI is the Standardised Precipitation Index
  • The drop-down menu offers a choice of minimum, mean, median, maximum, or 95th percentile. These choices select the statistical value extracted over the designated time period to show on the map.
  • Note that Mean returns a composite value, whereas the others are all a single value from a single scene. (The median is a single value from a single scene if there are an odd number of scenes. If there is an even number of scenes, then it is the average of two values.)


  • These blocks allow you to combine bands together in a way you define.
  • There are two frames to build the band math. They are either to build an RGB colour composite or a single band:

  • Once you have chosen one of these, add it to a satellite data block (optical or radar).
  • You can add an operation block, like this:

  • This gives you the opportunity to add, subtract, multiply or divide bands. 
  • A Band block allows you to choose the band, like this:

  • Note that when you select a band from the drop-down it will only give you the options available for the satellite data selected.
  • You can nest operator blocks to create more complex formulas.  The bracketing follows the order of the operator blocks (so each operator block is calculated in turn):

  • You will need an Other visualisation block to display the result. 
  • Here is an example:

  • In this case, the red band is VV, the green band is 2xVH, and the blue band is VH/VV.  


  • Change detection picks out the differences between two data sets. 
  • This block is useful for mapping changes in urban expansion, forest (and other land cover) change, post-storm damage assessment, or any other environmental change. 
  • A complete description of how to use this block is given in this article on How to do Change Detection. 



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