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

Abstract

1. Seasonal and spatial variations in leaf area index (LAI) are challenging to detect in tropical forests due to dynamic lighting conditions and the subtle differences in the variation. Many existing LAI software tools offer one-click processing of all images through auto-threshold segmentation (e.g., HemispheR, HemiPy and Hemisfer), but they produce results with large discrepancies. Some software (e.g. CAN-EYE) requires manual tuning of each image, making large-scale analysis impractical.

2. We analysed 19,000 images from four tropical forest subtypes and found that using coding-free AI software to process hemispherical images can significantly improve the consistency of leaf-sky segmentation, thereby enhancing LAI outcomes.

3. The results show that replacing the auto-threshold with AI substantially reduced inter-software disagreement and delineated correct seasonal and spatial patterns. CAN-EYE was able to identify seasonal patterns but produced less accurate results than the CAN-EYE-AI integrated approach due to subjective user bias.

4. The high consistency achieved through AI integration enables reliable cross-site and cross-operator comparisons. As users can customise the AI model according to local images and combine the AI model with other LAI software, our integrated, affordable, and coding-free method offers wide applicability and high consistency of LAI measurements, facilitating the advancement of tropical forest monitoring and research.