Land use Land Cover (LULC) Prediction Map (2025) Using Random Forest Classifier ||Google Earth Engin

Land cover parameter: Define values, palette, and names for land cover classes.

Show legend: Display a legend on the map for the land cover classes.

Land cover dictionary for visualization: Create a dictionary containing land cover class information for visualization.

Land cover data for 2019 and 2022: Load land cover data for 2019 and 2022.

Region of interest (ROI): Define the region of interest as the geometry of the land cover data for 2019.

Put the land cover in a list: Create a list containing the land cover images for 2019 and 2022.

Show the 2019 and 2022 land cover: Add layers for 2019 and 2022 land cover to the map.

Create land cover change map: Generate a map indicating changes between land cover classes.

Show the change map: Add the change map layer to the map.

Print the change dictionary: Print a dictionary containing land cover change values.

Create images with variables to predict land cover change: Create images containing variables for predicting land cover change.

Sample image: Generate a stratified sample of points for training the prediction model.

Split train and test: Split the sample into training and testing datasets.

Build random forest model for prediction: Train a random forest model using the training dataset.

Test model accuracy: Evaluate the accuracy of the trained model using the testing dataset.

Variables for predict for year 2025: Create image variables for predicting land cover for the year 2025.

Apply the model for the variables for 2025: Use the trained model to predict land cover for the year 2025.

Add lulc 2025 to LULC list: Add the predicted land cover for 2025 to the list of land cover images.

Calculate land cover area per year: Calculate the area of each land cover class for each year.

Make chart for land cover area change: Create a chart showing land cover area changes over time.

Function to add legend: Define a function to add a legend to the map.