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

Abstract

O_LISeasonal 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. C_LIO_LIWe 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. C_LIO_LIThe 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. C_LIO_LIThe 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. C_LI Data/Code for peer review statementOne of the key features of this method is coding-free. The method is explained in Protocolv20251118.docx. We have uploaded R codes for drawing figures in a zip pack. These codes and the protocol will be deposited in the Zenodo (or figshare) database under accession link [TBC]. Since Zenodo allows authors to archive updated versions after publication, we may update the protocol by uploading a revised version to Zenodo. Please check the Zenodo archive for any new versions. In the protocol, we note that users can use Image_conversion_20220407.m and lets_change_values.R instead of the Renormalise function of ilastik to modify values in the classification output images. These codes are not essential for users following our protocol, but could be useful for integrating ilastik with other LAI software not covered in this paper. Additionally, the protocol mentions that Gather_LAI_fapar_from_caneye.R can be used to consolidate output Excel files, eliminating the need to manually open each file. Field measurements of LAI and GCC are available on request.

Plant phenotyping relevance

森林のLAIという植物形態・構造形質を、AIによる画像セグメンテーションで推定する方法の開発・統合・比較評価が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The paper's LAI analysis assets (protocol document, R figure scripts, helper scripts like lets_change_values.R and Gather_LAI_fapar_from_caneye.R) are stated to be deposited in Zenodo/figshare, but the accession link is '[TBC]' and no public URL is provided, so they are not yet actionable. Field LAI and GCC data are 'a

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