Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine Learning

Research Topic: Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine LearningObjective: This study aims to compare parametric and non-parametric methods for estimating the percentage of forest canopy cover in a section of the Zagros ecosystem.Method: In order to achieve the research objective, field sampling was conducted to determine the percentage of canopy cover, and high-resolution satellite imagery was utilized. The vegetation indices TSAVI, NDVI, and WDVI were calculated. Subsequently, the values derived from the vegetation cover indices at the sample plots were extracted using the Zonal Statistics function in ArcGIS. Multiple linear regression and artificial neural networks were employed to estimate vegetation density. To compare the performance of these two models, the metrics RMSE, RMSE%, and R² were utilized.Results: The results indicated that the MLR model achieved an R² value of 0.54 and an RMSE% of 10.4 at a 0.05 confidence level, while the MLP model yielded an R² of 0.82 and an RMSE% of 4.5.Conclusions: The comparative analysis demonstrated that the artificial neural network (MLP) provided more accurate estimates with lower error rates than the multiple linear regression (MLR) method in predicting vegetation density.