====== W04-2: Land cover classification ======
Once training sites are available, we can start with classifying the Landsat scene.
In this worksheet, we will use Random Forest as a classifier.

===== Things you need for this worksheet =====
  * {{section>en:resources:templates:tools#R environment&inline}}
  * {{section>en:resources:templates:tools#R studio&inline}}
  * [[en:courses:training:element-01:worksheets:lc-ws-03-1|W03-1: Read remote sensing data]]
  * [[en:courses:training:element-01:worksheets:lc-ws-04-1|W04-1: Training sites]]



===== Learning log assignments =====


:-\ Load the cropped Landsat bands 1-7 and the training site shapefile into R. Make sure that both datasets are in the same projection.

:-\ Extract the information of the landsat bands for the location of the training sites. Return the results as a data.frame

:-\ Add a new column to the data.frame which contains the land cover information from the training site shapefile

:-\ Split your dataset into training and testing data (e.g. 30% Testing, 70% Training). 

:-\ Use the training dataset to train a random forest model that is able to predict the land cover class from the spectral Landsat bands.

:-\ Explore which bands were important in the model

:-\ Use the model to predict on the entire Landsat scene. Plot your results with spplot.