Quick start

You can very easily make your first steps with Forest Foresight by running the following lines in Rstudio, after installing the Forest Foresight package. Please read the comments carefully since they contain instructions on what to do during running, for instance when instantiating ff_environment. Make sure you have Rstudio, R and the ForestForesight package installed.

To learn more about what every function does make sure that you check the required material in the chapter on Training material.

library(ForestForesight)
ff_environment() #after running this line, fill in the details in the interactive window below in Rstudio
#fastest is to select a small country with fewer tiles, e.g. 
#Suriname (SUR) has one, 
#Laos (LAO) two, 
#while Brazil (BRA) has over fifteen tiles.

#first download the preprocessed data
ff_sync(features = "high", #we only want the most important features
        date_start = "2023-01-01", date_end = "2023-12-01",# this is an example year
        download_model = TRUE,  #we will download the already pregenerated model
        download_predictions = TRUE, #we will download the predictions as well
        download_groundtruth = TRUE #we will also download the groundtruth data since we have no own groundtruth
        )

# then use ff_run to load the input data, train and predict. 
# This will automatically plot the accuracy over the four prediction months per 1x1 degree tile
# and per month and the importance of individual features for the model. If not, increase the window
# size in the bottom right and run again.
ff_output <- ff_run(train_dates = ForestForesight::daterange("2023-01-01","2023-03-01"), #train on the first 3 months, normally we recommend at least a year of training data
                    prediction_dates = ForestForesight::daterange("2023-09-01","2023-12-01")) # predict on the last four months to always have a 6 month gap between trianing and predicting

# the plot below will show the four rasters for the four months of prediction
plot(ff_output$predictions)
# the plot below shows the medium, high and very high risk level polygons for december 2023
plot(ff_output$shape)
plot(ff_output$risk_zones$`2023-12-01`$medium, col="yellow", border = "yellow", add=TRUE)
plot(ff_output$risk_zones$`2023-12-01`$high, col="orange", border = "orange", add=TRUE)
plot(ff_output$risk_zones$`2023-12-01`$very_high, col="red", border = "red", add=TRUE)

#this shows the first lines of the accuracy dataframe, with precision, recall and F0.5 for every month
# and every 1x1 degree
head(ff_output$accuracy_dataframe)

#this shows the relative importance of the features for the built model
head(ff_output$importance_dataframe,10)

#in this variable the model is stored. You can save it by setting the model_save_path in ff_run before running.
ff_output$model

# by setting the parameters for output in ff_run you can export all this information to disk as well

Forest Foresight

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