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

Abstract

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.