Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning

Leaf area index (LAI) has frequently been measured in the field using traditional optical methods such as digital hemispherical photography (DHP). However, in the DHP retrieved LAI, there is always contribution of woody components due to the difficulty in distinguishing woody and foliar materials. In addition, the leaf angle distribution which strongly affects the estimation of LAI is either ignored while using the convergent angle 57.5 degrees, or inversed simultaneously with LAI using multiple directions. Terrestrial laser scanning (TLS) provides a 3-dimensional view of the forest canopy, which we used in this study to improve LAI estimation by directly retrieving leaf angle distribution, and subsequently correcting foliage clumping and woody effects. The leaf angle distribution was retrieved by estimating the angle between the leaf normal vectors and the zenith vectors. The clumping index was obtained by using the gap size distribution method, while the woody contribution was evaluated based on an improved point classification between woody and foliar materials. Finally, the gap fraction derived from TLS was converted to effective LAI, and thence to LAI. The study was conducted for 31 forest plots including deciduous, coniferous and mixed plots in Bavarian Forest National Park. The classification accuracy was improved by approximately 10% using our method. Results showed that the clumping caused an underestimation of LAI ranging from 1.2% to 48.0%, while woody contribution led to an overestimation from 3.0% to 31.9% compared to the improved LAI. The combined error ranged from -46.2% to 32.6% of the leaf area index (LAI) measurements. The error was largely dependent on forest types. The clumping index of coniferous plots on average was lower than that of deciduous plots, whereas deciduous plots had a higher woody-to-total area ratio. The proposed method provides a more accurate estimate of LAI by eliminating clumping and woody effects, as well as the effect of leaf angle distribution.