Improving accuracy of optical methods in estimating leaf area index through empirical regression models in multiple forest types

Abstract

We developed the empirical regression models relating the direct LAI and optical LAI from initial leaf out to the leaf fall in different forest types in China. Optical methods have usually been used to estimate the leaf area index (LAI) in a forest stand because of rapidity and reduced labor requirements. However, few studies have reportedly improved the accuracy of the optical LAI estimates for seasonal dynamics using empirical models in different forest types. In the present study, we directly measured the seasonal dynamics of LAI from leaf out to leaf fall based on litter collection (defined as direct LAI) in a mixed evergreen–deciduous forest, an evergreen forest and a deciduous forest. Meanwhile, the effective LAI was estimated using digital hemispherical photography (DHP) and LAI-2000 instruments. Our main objective was to explore the seasonal changes in the relationship between direct LAI and effective LAI values and to find the best LAI empirical estimation model in different forest types. The season-dependent models relating direct LAI and effective LAI in each period were developed through a power function regression model in several forest types. Then, significance tests were applied to compare the different season-dependent models. The analysis showed that the season-dependent models can be merged into different aggregated models depending on forest types and optical methods. We confirm that the seasonal changes in LAI in different forest types can be fully estimated through aggregated models using both DHP and LAI-2000 methods with accuracies of more than 87 and 92 %, respectively. Meanwhile, our results suggest that the forest type (i.e., species composition of forest stand) and optical method should be seriously considered to correctly and quickly estimate the seasonal changes of LAI through the aggregated models.