To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spect...To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.展开更多
【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...【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。展开更多
基金Supported by the National Key R&D Program of China(2016YFD0300610)
文摘To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.
基金the Knowledge Innovation Program of the Chinese Academy of Sciences (No.kzcx2-yw-313)the National High Technology Research and Development Program of China (No. 2007AA12Z157)
文摘【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。
