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Terrestrial laser scanner (TLS)-based leaf area index (LAI) retrieval is an appealing concept, due to the ability to capture structural information of canopies as 3-D point cloud data (PCD). TLS-based LAI estimation methods promise a nondestructive tool for spatially explicit calibration of LAI estimated by aerial or satellite remote sensing techniques. These methods also overcome the sky condition restrictions of on-ground optical instruments such as hemispherical photography frequently used for LAI estimation. This paper presents a new method for estimating the effective LAI (LAIe) directly from PCD generated by TLS in heterogeneous forests. We converted the 3-D PCD into 2-D raster images, similar to hemispherical photographs, using two geometrical projection techniques in order to estimate gap fraction and LAIe using a linear least squares method. Our results indicated that the TLS-based algorithm was able to capture the variability in LAIe of forest stands with a range of densities. The TLS-based LAIe estimation method explained 89.1% (rmse = 0.01 ; p <; 0.001) of the variation in results from digital hemispherical photographs taken of the same stands and used for validation. The Breusch-Pagan test score confirmed that the stereographic-projection-based TLS LAIe model was more robust compared to the Lambert azimuthal equal-area projection TLS LAIe model. Finally, we explore and show significant relationships between airborne-laser-scanner (ALS)-based and TLS-based LAIe estimates, showing promise for further exploration of utilizing TLS as a calibration tool for ALS.