Litter vs. Lens: Evaluating LAI from Litter Traps and Hemispherical Photos Across View Zenith Angles and Leaf Fall Phases

Abstract. Leaf area index (LAI) is a key parameter for modeling ecosystem productivity, climate interactions, and hydrological processes, as well as monitoring vegetation health. While satellite-based estimates provide insights into large-scale vegetation dynamics, ground-based methods, including digital hemispherical photography (DHP), are essential to generate and validate such products and offer a practical alternative for fine-scale assessments. However, it remains unclear if the DHP method enables to robustly track temporal LAI dynamics. Here, we evaluate DHP-derived LAI time series with litter trap (LT)-derived LAI in a temperate deciduous broad-leaved forest. First, by comparing DHP-derived LAI estimates with LT-derived LAI across varying view zenith angles ranging from 10° to 90°, we investigate how well both methods align. Using 15 sample locations, we found the highest average correlation across all locations of DHP- and LT-derived LAI (R² =0.88) at a view zenith angle of 20°, indicating that litter traps represent a relatively narrow spatial footprint. Uncertainties for individual litter traps attributed to varying site conditions, such as tree stem density or canopy coverage. To overcome these uncertainties, we applied a site specific calibration using the litter traps and a generalized linear mixed model, which significantly increased correlation (R² =0.97).

This study highlights the potential of DHP for tracking spatio-temporal LAI dynamics in decideous forests. Moreover, we demonstrate that integrating DHP and LT data, alongside a mixed-effects model, can enhance the site specific accuracy and applicability of LAI assessments.