新疆农业科学2024，Vol.61Issue(10)：2527-2536,10.DOI:10.6048/j.issn.1001-4330.2024.10.020
基于多特征融合的无人机天山假狼毒地上生物量估算
Estimation of above ground biomass of drone Diarthron tianschanicum based on multi feature fusion
摘要
Abstract
[Objective]This project aims to explore the ability of UAV multi feature to construct D.tian-schanicum aboveground biomass(AGB)estimation model.The finding has provided a reference for the grading basis for the classification of grassland degradation degree.[Methods]Diarthron tianschanicum is one of the degradation indicator plants,and its growth status can reflect the degree of grassland degradation.and to ex-tract the spectral features,texture features and D.tianschanicum coverage from visible high spatial resolution remote sensing images,and the three were used as inputs to establish a univariate linear model.The three types of features were fused to construct multiple stepwise regression and artificial neural network models,and the effect of multi feature fusion to estimate AGB was analyzed.[Results](1)The best coverage extraction window period of D.tianschanicum was in full bloom,and the effect of D.tianschanicum extraction model constructed by RF algorithm was ideal,and the overall accuracy was more than 81%.(2)Spectral features,texture features and coverage were all correlated with AGB,and the texture feature G had the highest correla-tion,which was 0.784.(3)Compared with single vegetation index,texture feature,coverage and any two feature combinations as input amount,the accuracy of AGB was the highest,with R2 and RMSE of 0.870 and 15.383,respectively.[Conclusion]It is verified by artificial neural network mode that the fusion of spectral features,texture index and coverage can effectively improve the accuracy of AGB estimation.关键词
无人机/天山假狼毒生物量/可见光/覆盖度/纹理特征Key words
drone/Diarthron tianschanicum biomass/visible light/coverage/texture features分类
农业科技引用本文复制引用
侯正清,颜安,谢开云,袁以琳,夏雯秋,肖淑婷,张振飞,孙哲..基于多特征融合的无人机天山假狼毒地上生物量估算[J].新疆农业科学,2024,61(10):2527-2536,10.基金项目
新疆维吾尔自治区重点研发任务专项计划(2022B02003) Special Project for Key R&D Task in Xinjiang Uygur Autonomous Region(2022B02003) （2022B02003）
