Within-field variation of leaf area index (LAI) plays an essential role in field crop monitoring and yield forecasting. Although unmanned aerial vehicle (UAV)-based optical remote sensing method can overcome the spatial and temporal resolution limitations associated with satellite imagery for fine-scale within field LAI estimation of field crops, image correction and calibration of UAV data are very challenging. In this project, a physical-based method was proposed to automatically calculate crop effective LAI (LAIe) using UAV based 3-D point cloud data. Regular high spatial resolution RGB images were used to generate point cloud data for the study area. The proposed method, simulated observation of point cloud (SOPC), was designed to obtain the 3-D spatial distribution of vegetation and bare ground points and calculate the gap fraction and LAIe from a UAV based 3-D point cloud dataset at vertical, 57.5°, and multi view angle of a winter wheat field in London, Ontario, Canada. Results revealed that the derived LAIe using the SOPC multi view angle method correlates well with the LAIe derived from ground digital hemispherical photography, R2 = 0.76. The newly proposed method performs well for LAIe estimation during the main leaf development stages (BBCH 20-39) of the growth cycle. This method has the potential to become an alternative approach for crop LAIe estimation without the need for ground based reference measurements, hence save time and money. This project is implemented with MATLAB software.