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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[1] Wiegand C L, Gausman H W, Cuellar J A, et al. Vegetation Density as Deduced from ERTS-1 MSS Response, Proc. Third ETRS-1 Symposium Vol. 1, NASA Technical Reports Server, 1974. 93. [2] Driss H, John R M, Elizabeth P, et al. Remote Sensing of Environment,2004,90:337. [3] LIU Jiao-di, CAO Wei-bin, MA Rong(刘姣娣, 曹卫彬, 马 蓉). Scientia Agricultura Sinica(中国农业科学), 2008(12): 4301. [4] TIAN Yong-chao, YANG Jie, YAO Xia, et al(田永超, 杨 杰, 姚 霞, 等). Chinese Journal of Applied Ecology(应用生态学报), 2009,(7): 1685. [5] YANG Feng, FAN Ya-min, LI Jian-long, et al(杨 峰, 范亚民, 李建龙, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010,(2): 237. [6] LI Xin-chuan, XU Xin-gang, BAO Yan-song, et al(李鑫川, 徐新刚, 鲍艳松, 等). Scientia Agricultura Sinica(中国农业科学), 2012, 45(17): 3486. [7] HUANG Chun-yan, LIU Sheng-li, WANG Deng-wei, et al(黄春燕, 刘胜利, 王登伟, 等). Soybean Science(大豆科学), 2008,(2): 228. [8] WANG Hong-yan, LI Xiao-song, ZHANG Jin, et al(王红岩, 李晓松, 张 瑾, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013,33(10): 2803. [9] XIA Tian, WU Wen-bin, ZHOU Qing-bo, et al(夏 天, 吴文斌, 周清波, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013,(3): 147. [10] ZHU Xi-cun, ZHAO Geng-xing, LEI Tong(朱西存, 赵庚星, 雷 彤). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(12): 190. [11] Gitelson A A, Kaufman Y J, Merzlyak M N. Remote Sensing of Environment, 1996, 58(3): 289. [12] Cristianini N, Taylor J S. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods(支持向量机导论). Translated by LI Guo-zheng, WANG Meng, ZENG Hua-jun(李国正, 王 猛, 曾华军, 译). Beijing: Publishing House of Electronics Industry(北京: 电子工业出版社), 2004. [13] ZHANG Xue-gong(张学工). Acta Automatica Sinica(自动化学报), 2000, 26(1): 32. [14] Breiman L. Machine Learning,2001,45(1): 5. [15] Alexander J D, Butler B J, Hummel J W. Soil Science Society of America Journal, 1980, 44(6): 1282. [16] Vogelmann J E, Rock B N, Moss D M. International Journal of Remote Sensing, 1993, 14(8): 1563. [17] Gupta R K, Vijayan D, Prasad T S. Advances in Space Research, 2001, 28(1): 201. [18] Marshak A, Knyazikhin Y, Davis A, et al. Geophysical Research Letters, 2000, 27(12): 1695. [19] Gitelson A, Merzlyak M N. Journal of Plant Physiology, 1994, 143: 286. [20] Merzlyak M N, Gitelson A A, Chivkunova O B, et al. Physiologia Plantarum, 1999, 106(1): 135. [21] Peuelas J, Piologaya R, Filella I. International Journal of Remote Sensing, 1997, 18(13): 2869. [22] FANG Kuang-nan, WU Jian-bin, ZHU Jian-ping, et al(方匡南, 吴见彬, 朱建平, 等). Statistics & Information Forum(统计与信息论坛), 2011, 26(3): 32.
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