烟草科技2026，Vol.59Issue(8)：91-102,12.DOI:10.16135/j.issn1002-0861.2025.0662
融合无人机激光雷达与多光谱影像的烟草叶面积指数反演
Tobacco leaf area index inversion by integrating UAV light detection and ranging and multispectral images
摘要
Abstract
To accurately estimate tobacco leaf area index(LAI),multispectral images,light detection and ranging(LiDAR)data and ground LAI data were synchronously collected at three key times selected at the fast growing stage(T1),bud stage(T2)and early maturity stage(T3),respectively,in a tobacco experimental site in Xuanzhou District,Xuancheng City,Anhui Province.Tobacco vegetation indexes(VIs)and texture features(TFs)were extracted from the multispectral images,and the canopy height model(CHM)was derived from the LiDAR data.Feature selection was performed via feature importance analysis.Tobacco LAI inversion models at different sampling times were established based on back propagation neural network(BPNN),random forest regression(RFR),support vector regression(SVR),convolutional neural network(CNN)and long short-term memory(LSTM)network.The accuracy of these models was evaluated to identify the optimal LAI inversion model and the results showed that:1)Compared with TFs,VIs had a stronger correlation with tobacco LAI.Among the single-feature models,VIs-based models generally outperformed TFs-based models,and the combination of VIs and TFs further improved the inversion accuracy.2)CHM data effectively enhanced the LAI inversion accuracy,and the improvement effect was highly coupled with the correlation between LAI and CHM.Specifically,significant improvement was observed at T1 and T2,while only negligible improvement was found at T3.3)The CNN model achieved the best LAI inversion performance with the combined input of CHM,VIs and TFs,yielding coefficients of determination(R²)of 0.822,0.847 and 0.820 at the three sampling time points,respectively.This study provides a methodological reference for tobacco growth monitoring and yield estimation based on multi-source remote sensing data.关键词
烟草/叶面积指数/多光谱影像/激光雷达/冠层高度模型/机器学习Key words
Tobacco/Leaf area index/Multispectral imagery/Light detection and ranging/Canopy height model/Machine learning分类
农业科技引用本文复制引用
解华明,周楚坤,吴钱娇,张婷,陈子贤,杜军,舒莹,陈良君..融合无人机激光雷达与多光谱影像的烟草叶面积指数反演[J].烟草科技,2026,59(8):91-102,12.基金项目
安徽省高校省级自然科学研究重点项目"引江济淮工程对区域交通可达性和经济联系影响及国土空间优化对策"(2022AH050232) （2022AH050232）
安徽建筑大学博士启动基金项目"面向复杂地表水动态过程的地表建模与动态模拟研究"(2021QDZ01) （2021QDZ01）
安徽皖南烟叶有限责任公司委托项目"基于无人机测绘遥感在皖南烟区的应用研究"(HYB20230062) （HYB20230062）
安徽省本科教学质量工程项目"面向工程教育认证的国家一流专业建设研究与实践"(2022jxgl014). （2022jxgl014）
