Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Design and LAI Measurements
2.2. UAV-Based Image Acquisition
2.3. Image Processing
2.3.1. Color Index (CI) Calculation
2.3.2. Texture Measurements
2.4. Regression Modeling Methods
2.4.1. Simple Linear Regression (LR)
2.4.2. Multiple Linear Regression (MLR)
2.4.3. Principal Component Regression (PCR) and Partial Least Squares Regression (PLS)
2.4.4. Random Forest (RF)
2.4.5. Support Vector Machine (SVM)
2.5. Statistical Analysis
3. Results
3.1. Variability of Rice Leaf Area Index
3.2. Simple Linear Regression Modeling for LAI Estimation
3.3. Multivariate Regression Modeling
3.4. Accuracy Assessment of the Predicted LAIs
4. Discussion
4.1. Combination of Color and Texture Information for Crop LAI Estimation
4.2. Comparison of Different Multivariate Regression Methods
4.3. Potential of Consumer-Grade UAV-Based Digital Imagery for Crop Monitoring
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Experiment | Year | Location | Samples | Transplanting Date | Sensing and Sampling Date |
---|---|---|---|---|---|
Experiment 1 | 2016 | Sihong | 144 | 25 June | 27 July (TI), |
3 August (SE), | |||||
11 August (PI), | |||||
18 August (BT) | |||||
Experiment 2 | 2017 | Lianyungang | 192 | 19 June | 27 July (TI), |
3 August (SE), | |||||
10 August (PI), | |||||
20 August (BT) |
CI | Name | Formula | Reference |
---|---|---|---|
VARI | Visible Atmospherically Resistant Index | (g − r)/(g + r − b) | Gitelson et al. [28] |
ExG | Excess Green Vegetation Index | 2g − r − b | Woebbecke et al. [29] |
ExR | Excess Red Vegetation Index | 1.4r − g | Meyer et al. [30] |
ExB | Excess Blue Vegetation Index | 1.4b − g | Mao et al. [31] |
ExGR | Excess Green minus Excess Red Vegetation Index | E × G − E × R | Neto et al. [32] |
NGRDI | Normalized Green-Red Difference Index | (g − r)/(g + r) | Tucker [33] |
MGRVI | Modified Green Red Vegetation Index | (g2 − r2)/(g2 + r2) | Tucker [33] |
WI | Woebbecke Index | (g − b)/(r − g) | Woebbecke et al. [29] |
IKAW | Kawashima Index | (r − b)/(r + b) | Kawashima et al. [34] |
GLA | Green Leaf Algorithm | (2g − r − b)/(2g + r + b) | Louhaichi et al. [35] |
RGBVI | Red Green Blue Vegetation Index | (g2 − b * r)/(g2 + b * r) | Bendig et al. [36] |
VEG | Vegetativen | g/(rab(1−a)), a = 0.667 | Hague et al. [37] |
Stages | Samples | LAI | ||||
---|---|---|---|---|---|---|
Min | Max | Mean | SD | CV | ||
Training dataset | ||||||
TI | 56 | 0.25 | 4.36 | 1.93 | 0.99 | 51.26% |
SE | 56 | 0.41 | 6.85 | 3.36 | 1.57 | 46.76% |
PI | 56 | 0.82 | 8.67 | 4.05 | 2.00 | 49.27% |
BT | 56 | 0.93 | 10.34 | 5.08 | 2.29 | 45.08% |
All stages | 224 | 0.25 | 10.34 | 3.60 | 2.11 | 58.48% |
Test dataset | ||||||
TI | 28 | 0.29 | 3.98 | 1.94 | 0.97 | 50.28% |
SE | 28 | 0.40 | 5.17 | 3.06 | 1.50 | 48.90% |
PI | 28 | 0.81 | 8.07 | 3.92 | 2.03 | 51.82% |
BT | 28 | 1.15 | 10.05 | 4.99 | 2.28 | 45.58% |
All stages | 112 | 0.29 | 10.05 | 3.48 | 2.07 | 59.69% |
Index | Variable | Model Equation | R2 | RMSE |
---|---|---|---|---|
CI | VARI | LAI = 14.836 * VARI − 0.6889 | 0.74 | 1.13 |
IKAW | LAI = −32.687 * IKAW + 4.2477 | 0.71 | 1.16 | |
NGRDI | LAI = 23.922 * NGRDI − 0.5704 | 0.68 | 1.24 | |
NDTI | NDTI (Rmea, Gmea) | LAI = −23.123 * NDTI(Rmea, Gmea) − 0.5719 | 0.72 | 1.15 |
NDTI (Rmea, Bmea) | LAI = −29.595 * NDTI(Rmea, Bmea) + 4.3718 | 0.71 | 1.17 | |
NDTI (Rcor, Bcor) | LAI = −22.009 * NDTI(Rcor, Bcor) + 4.548 | 0.58 | 1.38 |
Method | CI | Textures | Cis + Textures | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | |
MLR | 0.82 | 0.92 | 0.81 | 0.99 | 0.85 | 0.86 |
PLS | 0.83 | 0.91 | 0.82 | 0.96 | 0.86 | 0.84 |
PCR | 0.83 | 0.91 | 0.82 | 0.96 | 0.86 | 0.84 |
RF | 0.80 | 0.97 | 0.79 | 0.93 | 0.84 | 0.90 |
SVM | 0.76 | 1.06 | 0.83 | 0.92 | 0.85 | 0.89 |
Index | Variable | R2 | RMSE | MAE |
---|---|---|---|---|
CI | VARI | 0.70 | 1.14 | 0.94 |
IKAW | 0.65 | 1.31 | 1.06 | |
NGRDI | 0.66 | 1.22 | 0.98 | |
NDTI | NDTI (Rmea, Gmea) | 0.65 | 1.23 | 0.98 |
NDTI (Rmea, Bmea) | 0.65 | 1.30 | 1.02 | |
NDTI (Rcor, Bcor) | 0.41 | 1.72 | 1.33 |
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Li, S.; Yuan, F.; Ata-UI-Karim, S.T.; Zheng, H.; Cheng, T.; Liu, X.; Tian, Y.; Zhu, Y.; Cao, W.; Cao, Q. Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sens. 2019, 11, 1763. https://doi.org/10.3390/rs11151763
Li S, Yuan F, Ata-UI-Karim ST, Zheng H, Cheng T, Liu X, Tian Y, Zhu Y, Cao W, Cao Q. Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sensing. 2019; 11(15):1763. https://doi.org/10.3390/rs11151763
Chicago/Turabian StyleLi, Songyang, Fei Yuan, Syed Tahir Ata-UI-Karim, Hengbiao Zheng, Tao Cheng, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao, and Qiang Cao. 2019. "Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation" Remote Sensing 11, no. 15: 1763. https://doi.org/10.3390/rs11151763
APA StyleLi, S., Yuan, F., Ata-UI-Karim, S. T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2019). Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sensing, 11(15), 1763. https://doi.org/10.3390/rs11151763