An AI-based and coding-free protocol for forests Leaf Area Index (LAI) calculation

Measurements of Leaf Area Index (LAI) have become an important component in modern forest ecology and conservation. Detecting subtle annual, seasonal, and spatial variations in tropical forests is particularly challenging, due to their complex structure and dynamic environmental conditions. Traditional processing of hemispherical photographs involves time-consuming pixel classification that is prone to subjective systematic errors, impeding comparison across months, years, and study sites. While automated methods that ensure consistency are available, they typically require coding expertise, which limits their wider adoption among non-specialists. We present a streamlined protocol that enables automatic, coding-free calculation of LAI by integrating two free software packages: ilastik, for AI-based pixel classification, and CAN-EYE, for LAI estimation. To evaluate its performance, we compared the seasonal and spatial variations in LAI derived from our protocol with those obtained using traditional pixel classification. Four sub-types of tropical forests were used to test the protocols in different scenarios, including moist evergreen forest, semi-deciduous forest, dry forests, and woody savanna. We found that traditional pixel classification (which requires manual threshold adjustments by operators) was highly influenced by subjective bias, with results varying depending on the time invested in pixel classification. In contrast, the fully automated workflow (our protocol: ilastik pixel classification + CAN-EYE calculation) eliminates subjective bias, enabling consistent cross-years and cross-operator comparisons. Careful validation using field photos and local forest phenology showed that the automated approach captured seasonal LAI variations more accurately than traditional processing, aligning more closely with observed phenological patterns. However, analyses of spatial variation found no significant difference between protocols, as both captured site-to-site differences with comparable accuracy. Additionally, ilastik can classify pixels into non-binary categories (sky, leaves, and twigs), providing an added layer of detail that is useful for delineating savanna tree phenology. We present an automated, user friendly, and widely applicable protocol that enhances the accuracy, consistency, and reproducibility of LAI measurements, offering a robust tool for advancing tropical forest monitoring and research.