Potato LAI estimation by fusing UAV multi-spectral and texture features
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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R2adj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VI-MLR(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。
Abstract:ObjectiveDevelop a method to improve the potato (Solanum tuberosum) leaf area index (LAI) estimation accuracy using the UAV multiple spectral wavebands and texture information.
MethodThe 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 R2adj. 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.
ResultFrom 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.
ConclusionThis study proposed a method of PCA-MLR to estimate the potato LAI and improve the levels of the potato growth monitoring and field management.
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Keywords:
- UAV /
- Remote sensing /
- LAI /
- Multi-spectral /
- Texture feature /
- Potato
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图 1 实测LAI值的箱线图(a)和实测光谱曲线(b)
Figure 1. Box plot of the measured LAI value (a) and measured spectral curve (b)
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图 3 马铃薯提取前后的马铃薯植被区域对比
Figure 3. Comparison of potato planting area before and after potato extraction
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图 4 基于R2adj全子集回归优选的光谱特征重要度
Figure 4. Importance of spectral features based on R2adj full subset regression optimization
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图 5 基于R2adj全子集回归优选的纹理特征重要度
b1、b2、b3、b4和b5分别代表蓝、绿、红、红边、近红外波段
Figure 5. Importance of texture features based on R2adj full subset regression optimization
b1, b2, b3, b4 and b5 represent blue, green, red, red edge and near-infrared bands respectively
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图 7 马铃薯各生育期LAI预测值与实测值
Figure 7. Estimated and measured values of LAI of potato at different growth stages
表 1 大疆P4M多光谱相机波段信息
Table 1 Band information of DJI P4M multispectral camera
波段 Band 中心波长/nm Center wavelength 波长宽度/nm Wavelength width 蓝波段 Blue band 450 32 绿波段 Green band 560 32 红波段 Red band 650 32 红边波段 Red edge band 730 32 近红外波段 Near-infrared band 840 52 
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表 2 多光谱植被指数
Table 2 Multispectral vegetation index
植被指数 Vegetation index 计算公式1) Calculating formula 文献来源 Literature source NDVI NDVI= (NIR−Red)/ (NIR−Red) [15] GNDVI GNDVI=(NIR−Green)/ (NIR−Green) [16] NDRE NDRE=(NIR−Rededge)/ (NIR+Rededge) [17] LCI LCI=(NIR−Rededge)/ (NIR+Red) [18] OSAVI OSAVI=(NIR−Red)/ (NIR+Red+0.16) [19] DVI DVI=NIR−Red [20] DVI_GRE DVI_GRE=NIR−Green DVI_EDG DVI_EDG=NIR−Rededge RVI RVI=NIR/Red [21] RVI_GRE RVI_GRE=NIR/Green RVI_EDG RVI_EDG=NIR/Rededge RDVI RDVI=(NDVI)1/2 [22] RDVI_GRE RDVI=(GNDVI)1/2 RDVI_EDG RDVI_EDG=(NDRE)1/2 G G=Green R R=Red EDG EDG=Rededge NIR NIR=NIR 1) Green、Red、Rededge和NIR分别表示绿、红、红边及近红外波段反射率 1) Green, Red, Rededge and NIR represent the reflectance of green, red, red edge and near-infrared bands respectively
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表 3 光谱特征与LAI的相关系数绝对值
Table 3 Absolute value of correlation coefficient between spectral feature and LAI
植被指数 Vegetation index 幼苗期 Seedling period 现蕾期 Budding period 块茎膨大期 Tuber swelling period Green 0.49 0.46 0.30 Red 0.73 0.58 0.64 Rededge 0.30 0.48 0.15 NIR 0.43 0.61 0.24 NDVI 0.77 0.76 0.73 GNDVI 0.66 0.70 0.65 NDRE 0.53 0.63 0.30 OSAVI 0.69 0.70 0.66 LCI 0.58 0.66 0.33 RVI 0.76 0.79 0.73 RVI_GRE 0.54 0.64 0.54 RVI_EDG 0.67 0.74 0.24 DVI 0.54 0.64 0.35 DVI_GRE 0.50 0.65 0.32 GVI_REDED 0.51 0.70 0.30 RDVI 0.77 0.76 0.73 RDVI_GRE 0.66 0.69 0.54 RDVI_EDG 0.52 0.62 0.30
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表 4 纹理特征与LAI的相关系数绝对值(|r|)
Table 4 Absolute value of correlation coefficient (|r|) between texture feature and LAI
生育期 Growth period 纹理特征1)Texture feature |r| 幼苗期 Seedling period b1_Contr 0.57 b1_Hom 0.63 b2_Cor 0.58 b2_Mean 0.42 b3_Dis 0.55 b3_En 0.44 b3_Mean 0.30 b3_Var 0.38 b4_Cor 0.56 b5_Mean 0.56 现蕾期 Budding period b1_Contr 0.53 b1_En 0.50 b1_Mean 0.36 b2_Cor 0.46 b2_Con 0.57 b2_Dis 0.52 b2_En 0.65 b3_Mean 0.49 b4_Cor 0.62 b5_Mean 0.54 块茎膨大期 Tuber swelling period b1_Mean 0.63 b2_Var 0.24 b3_Com 0.53 b3_En 0.47 b3_Hom 0.43 b3_Sm 0.62 b3_Mean 0.69 b4_Hom 0.63 b5_Mean 0.45 b5_Hom 0.53 1)b1、b2、b3、b4和b5分别代表蓝、绿、红、红边、近红外波段 1) b1, b2, b3, b4 and b5 represent blue, green, red, red edge and near-infrared bands respectively
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表 5 马铃薯各生育期LAI估算建模比较1)
Table 5 Comparison of LAI estimation modeling for potatoes at different growth stages
生育期 Growth Period 自变量 Independent variable 建模方式 Modeling method R2 R2adj RMSE 优化的回归模型 Optimized regression model 幼苗期 Seedling period RDVI、 RVI_GRE、 Red、 RDVI_EDG、 GVI_EDG、 LCI GNDVI VI-MLR 0.647 0.587 0.490 b2_Hom、b2_Dis、 b1_Mean、b3_Contr T-MLR 0.637 0.581 0.511 PC1、PC2、PC3、PC4、PC5、PC6 PCA-MLR 0.739 0.674 0.426 LAI=PC6×4.16+PC4×0.27+ PC2×0.89−PC1×0.19− PC3×3.06−PC5×3.17+0.9 现蕾期 Budding period RDVI、NDRE、LCI、GNDVI、Green VI-MLR 0.483 0.470 0.571 b3_Mean、b4_Cor、b5_Mean T-MLR 0.465 0.417 0.609 PC1、PC2、PC3 PCA-MLR 0.592 0.558 0.542 LAI=PC3×1.17−PC2×0.84− PC1×0.11+0.34 块茎膨大期 Tuber swelling period NDVI、OSAVI、RVI_EDG、LCI、NIR、GNDVI VI-MLR 0.608 0.539 0.540 b3_Mean、b5_Mean、b5_Hom、 b3_Hom、b3_En、b1_Mean T-MLR 0.594 0.561 0.536 PC1、PC2、PC3、PC4 PCA-MLR 0.659 0.592 0.432 LAI=PC1×0.53+PC2×0.26− PC3×0.72−PC4×0.15−0.08 1)PC1~PC6表示对应的主成分 1)PC1−PC6 represent the corresponding principal components respectively
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表 6 施肥试验各处理地块LAI统计结果
Table 6 Statistical results of LAI of each treatment plot in fertilization experiment
施肥试验类型 Type of ferilization experiment 处理 Treatment 幼苗期 Seedling period 现蕾期 Budding period 块茎膨大期 Tuber swelling period 肥料联合筛选试验 Fertilizer joint screening experiment 常用有机肥+常规化肥 Commonly used organic fertilizer + conventional chemical fertilizer 2.90 4.95 5.67 生命源黄腐酸生物有机肥+缓释高钾肥 Life source fulvic acid bio-organic fertilizer + slow-release high potassium fertilizer 2.91 5.08 5.63 沃尔田生物有机肥+缓释高钾肥 Waltian bio-organic fertilizer + slow-release high potassium fertilizer 2.99 5.05 5.46 沃尔田生物有机肥 Waltian bio-organic fertilizer 2.82 4.78 5.00 不施肥 No fertilization 2.81 4.61 4.75 生命源黄腐酸生物有机肥 Life source fulvic acid bio-organic fertilizer 2.88 4.79 4.72 均值 Mean 2.89 4.88 5.21 氮肥分期施用试验 Nitrogen fertilizer application experiment by stages 25%基肥+75%追肥 25% basic fertilization+75% additional fertilization 1.37 4.09 4.68 50%基肥+50%追肥 50% basic fertilization+50% additional fertilization 1.36 4.12 4.40 100%基肥+无追肥 100% basic fertilization+no additional fertilization 1.36 4.11 4.44 不施肥 No fertilization 1.33 4.03 4.25 无基肥+100%追肥 No basic fertilization+100% additional fertilization 1.55 4.24 4.69 75%基肥+25%追肥 75% basic fertilization+25% additional fertilization 1.45 4.16 4.92 均值 Mean 1.4 4.13 4.56
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