Abstract
Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees.
Funder
National Funds from the FCT-Portuguese Foundation for Science and Technology
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献