Adjusting line quantum sensing to improve leaf area index measurements and estimations in forests

Rapid and reliable estimation of leaf area index (LAI), a crucial parameter in process-based models of vegetation cover response, is important in ecological studies. The Beer-Lambert law is widely used to calculate forest LAI, but data collection methods are time-consuming and calculations are often inaccurate. Our objective was to improve the accuracy of Beer-Lambert law-based LAI estimation by employing indirect data collection and location-specific light extinction coefficients (K). Canopy transmittance and LAI of two 100 m² temperate forest stands in southwestern Germany, one managed and one protected, was estimated using line quantum sensing (LQS) at 45,000 points per stand. The Beer-Lambert law was then inverted to estimate LAI using the measured transmittance with a K of 0.53-0.54. Hemispherical reference photographs were used as independent validation data to determine ideal K values. Experimental data demonstrated that LAI values estimated using LQS with adjusted K values were more accurate than those calculated using the basic application of the Beer-Lambert law. LQS results correlated with those determined using hemispherical photography for both the managed (R² = 0.80) and protected (R² = 0.81) stands. Overall, these findings show that adjusting K values for individual forest systems improves the accuracy of LAI estimation.•The modified method is more accurate than that using fixed K ranges.•The modified method accounts for individual ecosystems, with different K values for different environments.•The method can accurately reflect the dynamic changes of forest canopy structure, allowing integration of additional environmental measurements.