基于无人机影像阴影去除的苹果树冠层氮素含量遥感反演

doi:10.3864/j.issn.0578-1752.2021.10.005

Abstract

Abstract:

【Objective】The shadows in UAV multi-spectral remote sensing images were removed to improve the accuracy of the nitrogen inversion model for apple canopy. 【Method】Using the UAV multi-spectral images collected in June 2019 at the apple orchard of Qixia city in Shandong province, as the experimental area, normalized shaded vegetation index (NSVI) and normalized canopy shadow index (NDCSI) were respectively used to remove shadow and to extract the spectral information of the canopy in non shadow area. The correlation analysis method was used to analyze the correlation between the spectral data, including the data obtained based on the original spectral images and the images after removing the shadow based on NSVI and NDCSI, and the measured leaf nitrogen content data, respectively, and then the sensitive wavelength of nitrogen content were screened and spectral parameters were constructed. Partial least squares (PLS) and support vector machine (SVM) methods were used to build the inversion model of nitrogen content and to carry out the precision inspection in the fruit tree canopy. 【Result】The results showed that the green band and red band were sensitive bands for the inversion of nitrogen content in fruit tree canopy based on UAV multi-spectral images. The spectral information of fruit tree canopy was weakened by shadow, and the spectral difference of canopy multispectral bands before and after shadow removal was significant, especially in red-edge band and near-infrared band. The accuracy of nitrogen inversion model based on two shadow indexes after shadow removal was improved, and the optimal model was the support vector machine nitrogen content inversion model based on NDCSI, the modeling set of this model R² and RPD was 0.774 and 1.828, the validation set R² and RPD were 0.723 and 1.819 respectively. 【Conclusion】NDCSI could effectively remove the shadow in the multi-spectral fruit tree canopy image of the UAV to improve remote sensing inversion accuracy of nitrogen content in apple canopy, so as to provide a useful reference for precise nitrogen management in orchard.

Key words: canopy shadow, shadow vegetation index, UAV, multispectral, remote sensing

LI MeiXuan,ZHU XiCun,BAI XueYuan,PENG YuFeng,TIAN ZhongYu,JIANG YuanMao. Remote Sensing Inversion of Nitrogen Content in Apple Canopy Based on Shadow Removal in UAV Multi-Spectral Remote Sensing Images[J].Scientia Agricultura Sinica, 2021, 54(10): 2084-2094.

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波段 Band	中心波长 Band center (nm)	带宽 Band width (nm)
绿光（Bg）	550	40
红光（Br）	660	40
红边（Breg）	735	10
近红外（Bnir）	790	40

波段 Band	相关系数R
波段 Band	原始光谱反射率 Original spectral reflectance	NSVI去除阴影后提取光谱反射率 Spectral reflectance based on NSVI	NDCSI去除阴影后提取光谱反射率 Spectral reflectance based on NDCSI
绿光（Bg）	-0.663	-0.745	-0.786
红光（Br）	-0.680	-0.672	-0.684
红边（Breg）	-0.374	-0.356	-0.486
近红外（Bnir）	-0.415	-0.547	-0.612

序号 Number	光谱指数 Spcetral index	相关系数R
序号 Number	光谱指数 Spcetral index	原始光谱信息 Original reflectivity	基于NSVI提取光谱信息 NSVI reflectivity	基于NDCSI提取光谱信息 NDCSI reflectivity
1	Bg+Br	-0.692	-0.756	-0.790
2	Bg-Br	-0.527	-0.587	-0.627
3	Bg×Br	-0.700	-0.738	-0.763
4	Br/(Bg+Br)	-0.290	-0.264	-0.246
5	$\sqrt{B\text{g}\times Br}$	-0.699	-0.740	-0.766
6	$\sqrt{B{{g}^{2}}+B{{r}^{2}}}$	-0.679	-0.754	-0.793
7	1/(Bg+Br)	0.687	0.754	0.792
8	1/(Bg-Br)	0.512	0.584	0.627
9	$\sqrt{B\text{g}+B\text{r}}$	-0.691	-0.756	-0.791
10	$\sqrt{B\text{g}-Br}$	-0.523	-0.587	-0.628

影像类型 Images type	建模精度 Calibration accuracy		验证精度 Verification accuracy
影像类型 Images type	决定系数 R²	相对分析误差 RPD	决定系数 R²	相对分析误差 RPD
原始多光谱影像 Original multispectral image	0.575	1.432	0.154	0.878
NSVI去除阴影后影像 Image after shadow removal based on NSVI	0.622	1.474	0.670	1.426
NDCSI去除阴影后影像 Image after shadow removal based on NDCSI	0.750	1.837	0.748	1.660

影像类型 Images type	建模精度 Calibration accuracy		验证精度 Verification accuracy
影像类型 Images type	决定系数 R²	相对分析误差 RPD	决定系数 R²	相对分析误差 RPD
原始多光谱影像 Original multispectral image	0.607	1.415	0.141	0.895
NSVI去除阴影后影像 Image after shadow removal based on NSVI	0.656	1.551	0.627	1.479
NDCSI去除阴影后影像 Image after shadow removal based on NDCSI	0.774	1.828	0.723	1.819

Remote Sensing Inversion of Nitrogen Content in Apple Canopy Based on Shadow Removal in UAV Multi-Spectral Remote Sensing Images

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