融合无人机多光谱和纹理特征的马铃薯LAI估算  

Potato LAI estimation by fusing UAV multi-spectral and texture features

作　　者：李健[1] 江洪[1] 罗文彬[2] 麻霞 张雍 LI Jian;JIANG Hong;LUO Wenbin;MA Xia;ZHANG Yong(Academy of Digital China(Fujian),Fuzhou University/Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education/Local Joint Engineering Research Center of Satellite Geospatial Information Technology,Fuzhou 350108,China;Crop Research Institue,Fujian Academy of Agricultural Sciences,Fuzhou 350013,China)

机构地区：[1]福州大学数字中国研究院(福建)/空间数据挖掘与信息共享教育部重点实验室/卫星空间信息技术综合应用国家地方联合工程研究中心,福建福州350108 [2]福建省农业科学院作物研究所,福建福州350013

出　　处：《华南农业大学学报》2023年第1期93-101,共9页Journal of South China Agricultural University

基　　金：福建省科技计划引导性项目(2021Y0005);国家重点研发计划(2017YFB0504203)。

摘　　要：【目的】研究融合无人机遥感影像多光谱信息和纹理特征估算马铃薯Solanum tuberosum叶面积指数(Leaf area index,LAI)方法,提高马铃薯LAI反演精度。【方法】利用大疆P4M无人机采集2021年2-4月南方冬种马铃薯幼苗期、现蕾期、块茎膨大期多光谱影像,用LAI-2000冠层分析仪实测LAI数据。提取影像光谱、纹理等信息,分析植被指数、纹理特征与LAI的相关性,基于R^(2)_(adj)的全子集分析优选特征变量。采用主成分分析,融合光谱和纹理特征,用PCA-MLR(Principal component analysis-multiple linear regression)模型估算马铃薯LAI。【结果】从幼苗期到块茎膨大期,PCA-MLR估算模型优于T-MLR(Texture multiple linear regression)和VIMLR(Vegetation index multiple linear regression)模型,R2分别为0.73、0.59和0.66。【结论】本研究提出一种估算马铃薯LAI的PCA-MLR方法,为马铃薯的长势监测和田间管理提供数据支持。【Objective】Develop a method to improve the potato(Solanum tuberosum)leaf area index(LAI)estimation accuracy using the UAV multiple spectral wavebands and texture information.【Method】The DJI P4M drone was used to collect multispectral images of the southern winter potato at seedling period,budding period and tuber swelling period from February to April 2021.LAI data were measured by LAI-2000 canopy analyzer.The spectral and texture characteristics of images were extracted.The correlations between vegetation index,texture characteristics and LAI were analyzed.The selected characteristic variables were analyzed based on subset of adjusted R^(2)_(adj).The principal component analysis was used to fuse spectrum and texture features,and the principal component analysis-multiple linear regression(PCA-MLR)model was used to estimate potato LAI.【Result】From the seedling period to the tuber swelling period,the PCA-MLR estimation model was better than texture multiple linear regression(T-MLR)and vegetation index multiple linear regression(VI-MLR)model,with R2 of 0.73,0.59 and 0.66 respectively.【Conclusion】This study proposed a method of PCA-MLR to estimate the potato LAI and improve the levels of the potato growth monitoring and field management.

关 键 词：无人机 遥感影像 LAI 多光谱 纹理特征 马铃薯 

分 类 号：S532[农业科学—作物学] S127