Leaf area index (LAI) is a primary descriptor of vegetation structure, function, and condition. It is a vegetation product commonly derived from earth observation data. Independently obtained ground-based LAI estimates are vital for global satellite product validation. Acceptable uncertainties of these estimates are guided by satellite product accuracy thresholds stipulated by the World Meteorological Organisation (WMO) and the Global Climate Observing System (GCOS). This study compared canopy openness, gap fraction and LAI estimates derived from ground-based instruments; the primary focus was to compare high-and low-resolution (HR and LR) digital hemispherical photography (DHP) to a terrestrial laser scanner (TLS), augmented with measurements using the LAI-2200 plant canopy analyser in a subset of plots. Additionally, three common DHP classification methods were evaluated including a manual supervised (S) classification, a global (G) binary automated threshold, and a two-corner (TC) automated threshold applied to mixed pixels only. Coincident measurements were collected across five diverse forest systems in Eastern Australia with LAI values ranging from 0.5 to 5.5. Canopy openness, gap fraction and LAI were estimated following standard operational field data collection and data processing protocols. A total of 75 method-to-method pairwise comparisons were conducted, out of which 37 had an RMSD ≥ 0.5 LAI and 26 were significantly different (p < 0.05). HR-DHP (S) and two-corner (TC) methods were in close agreement with LAI-2200 (LAI RMSD 0.18 and 0.19, respectively). Additionally, the supervised (S) and two-corner (TC) methods were in close agreement over all canopy openness and LAI levels, matching to within 6% (openness: RMSD 0.04, LAI: RMSD 0.19). The automated classification method (TC) demonstrated the potential to be used as a substitute for the manual (S) classification (openness and LAI not significantly different, p > 0.75). Although TLS produced on average 55% higher openness and LAI than the HR-DHP (S) and (TC) classification methods, the strong coefficient of determination indicated the potential to calibrate these methods (R 2 = 0.88 and 0.79, respectively). Overall, results demonstrate a level of variability typically above the targeted uncertainty levels stipulated by the WMO and GCOS for satellite product validation. Further instrument calibration of TLS and improved DHP image capture and processing methods are expected to reduce these uncertainties.