Exercise5: Validation

Visual validation

  • Use the Sentinel-2 data to predict the mean vegetation height for the entire Lahntal.
  • Save the prediction as a tif file and have a look at it with your favorite GIS application.
  • What can you say about the quality of the prediction?

Statistical validation

  • Predict the mean vegetation height on your testing set.
  • Create a scatterplot with the actual mean vegetation height on the x-axis and the predicted mean vegetation height on the y-axis.

One of the most used error metrics to assess the quality of a model is the Root Mean Squared Error or short the RMSE.

  • How is the RMSE calculated?
  • Implement the formula for the RMSE in R and calculate it for your predictions of the test data. How can you interpret this value?
  • Divide the RMSE by the mean of the observed vegetation height in the test data. How can you interpret this value?

Uploading assignment to Ilias

Finalize the three Exercises (Exercise 3, 4, and 5), read the example script provided in the html, and create a nice map of the vegetation height that you predicted. Then upload both files (i.e., Rmd and html) to Ilias. See here for where exactly to place your files as well as for the file naming conventions.

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