Empirical models for tracing seasonal changes in leaf area index in deciduous broadleaf forests by digital hemispherical photography

Accurate estimation of seasonal leaf area index (LAI) variations is essential for predicting forest growth, but rapid and reliable methods for obtaining such estimates have rarely been reported. In this study, direct measurements of LAI seasonal variations in deciduous broadleaf forests in China were made through leaf seasonality observations in the leaf-out season and litter collection in the leaf-fall season. Meanwhile, indirect LAI measurements were made using a digital hemispherical photography (DHP) method. Our objectives were to explore the relationship between direct and indirect LAI measurements and to recommend a rapid and reliable method to determine the seasonal variation of LAI in forests. To achieve these objectives, we first evaluated seasonal variations of the biases due to key factors (woody materials, clumping effects and incorrect automatic exposure) known to influence the estimation of LAI by DHP. The results showed that the biases due to these factors exhibited different seasonal variation patterns, and the total contribution of these factors could explain 72% of the difference between direct LAI and DHP LAI throughout the entire growing season. Second, linear regression models between direct and DHP LAI were first constructed for each 10-day period as well as the entire growing season. Significance tests were made to the differences among the models for different dates, and models for estimating LAI based on DHP in each date were aggregated to 4 periods with R2 and RMSE values of 0.91 and 0.22, 0.79 and 0.29, 0.81 and 0.14, 0.97 and 0.14, respectively. There was no significant difference between direct LAI and estimated LAI using the four models in each aggregated period (p <0.01). Thus, we confirm that these models can fully simulate the seasonal variations in LAI from the initial leaf emergence to leaf fall in deciduous broadleaf forests.