Linear Regression performs analysis (Y = a1X1+a2X2+...+anXn) to predict forest parameters, such as tree height, biomass, etc., from field measurements and LiDAR-derived metrics.
Navigate to and click on ALS Forest > Regression Analysis > Linear Regression.
- Import Training Data: Refer to Sample Data and Independent Variables.
- Independent Variables: Refer to Sample Data and Independent Variables.
- Linear Regression Method: Users can define the method of linear regression here.
- Enter (default): All selected independent variables will be included in the linear regression equation.
- Stepwise: The choice of independent variables from the user input will be carried out by an automatic procedure. In each step, one independent variable is considered as an addition to the linear regression model, and each variable will be retained or removed from the model based on statistical t-test.
- Accuracy Assessment: Based on the K-Fold cross validation model, a sample would be partitioned into k subsets according to input K-Fold value (no less than 2). Take one of subsets as a validation dataset and rest of subsets as training datasets to form a model, then run this model and test the fitting of validation set to training sets. Repeat this process until every subset is treated as a validation set at least once and select out the model with the least MSE (mean square error) as the optimal model.
- Save Regression Model: Tick the checkbox to save the regression model (Linear Regression.model) under the output path.
- Save Regression Dataset: Tick the checkbox to save the training dataset (Linear Regression.csv) in .csv format under the output path.
- Output Path: Choose an output directory. A linear regression model report (Linear Regression.html), recording the model's parameters and accuracy (R-square, RMSE), would be generated under this directory. A prediction result file (Linear Regression.tif), based on the linear regression model and input variables from a .tif or .csv file, would also be generated under this output directory.
Note: The dimension of imported sample/training data must be within the scope of independent variables, which may be adjusted accordingly. The model/result is based on the passed-in variables.