| 光谱学与光谱分析 |
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| Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression |
| HAN Zhao-ying1, ZHU Xi-cun1, 2*, FANG Xian-yi1, WANG Zhuo-yuan1, WANG Ling1, ZHAO Geng-xing1, JIANG Yuan-mao3 |
1. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
2. Key Laboratory of Agricultural Ecology and Environment, Shandong Agricultural University, Tai’an 271018, China
3. College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an 271018, China |
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Abstract Leaf area index(LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, NDVI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.
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Received: 2014-12-23
Accepted: 2015-03-24
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Corresponding Authors:
ZHU Xi-cun
E-mail: zxc@sdau.edu.cn
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