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林业科学 ›› 2020, Vol. 56 ›› Issue (2): 89-98.doi: 10.11707/j.1001-7488.20200210

• 论文与研究报告 • 上一篇    下一篇

基于植被指数及多光谱纹理特征的降香黄檀叶片全铁含量预测

陈珠琳,王雪峰*   

  1. 中国林业科学研究院资源信息研究所 北京 100091
  • 收稿日期:2018-12-12 出版日期:2020-02-25 发布日期:2020-03-17
  • 通讯作者: 王雪峰
  • 基金资助:
    国家自然科学基金项目(31670642);林业科学技术推广项目([2016]11号)

Prediction of Total Iron Content in Dalbergia odorifera Leaves Based on Vegetation Index and Multispectral Texture Parameters

Zhulin Chen,Xuefeng Wang*   

  1. Reserch Institute of Forest Resourse Information Techniqnes, Chinese Academy of Forestry Beijing 100091
  • Received:2018-12-12 Online:2020-02-25 Published:2020-03-17
  • Contact: Xuefeng Wang

摘要:

目的: 以海南省特有树种降香黄檀为研究对象,提出1种基于植被指数和多光谱纹理特征的叶片全铁含量(TIC)预测方法,为珍贵树种重金属营养诊断提供参考。方法: 分别设计4种梯度(CK、F1、F2、F3)的铁胁迫试验,胁迫结束后摘取叶片并获取多光谱图像,计算叶片图像的植被指数(VIs)及纹理特征(包括纹理特征均值TFMV和纹理特征方差TFV),分析其与TIC之间的关系。通过显著性检验筛选出与TIC在0.05和0.01水平上显著相关的变量,再使用相关性分析法(CA)、主成分分析法(PCA)、平均影响值法(MIV)和遗传算法(GA)进行二次筛选,将筛选结果作为粒子群优化-反向反馈神经网络(PSO-BPNN)的输入变量,分析比较预测结果。结果: 1)在CK ~F2梯度区间内,树高、冠幅和地茎的生长量随着施铁含量的增加而增加,而在F3梯度下,树高、冠幅生长量降低,地茎生长量出现大幅度上升;2)随着叶片TIC的上升,B波段呈先下降后上升趋势;G波段则与B波段相反;R波段先下降后上升,之后基本保持稳定;RE和NIR波段则一直呈上升趋势;3)大部分VI与TIC在0.01和0.05水平上相关,TFMV和TFV也可以反映叶片TIC,但TFV在相关水平和相关个数方面均优于TFMV。从波段角度分析,RE和NIR波段在植被指数和纹理特征方面与TIC的相关性均优于其他波段;4)使用不同筛选方法得到的预测结果不同,其中CA与GA得到的评价指标最佳且相似,但GA在150~300 mg·kg-1区间得到的预测结果偏低,不适用于田间施肥指导。5)仅使用植被指数得到的预测结果较差,加入纹理特征后很明显的提高了拟合优度及预测精度,纹理特征方差对模型精度的影响更大,说明叶片纹理的离散程度可以较好的作为预测全铁含量的辅助信息。结论: F1和F2梯度的施肥量可以促进降香黄檀植株生长及生物量累计。最佳叶片全铁含量为150~300 mg·kg-1。除植被指数外,MPV作为辅助因素可以提高模型的拟合优度及预测精度,同时,CA-PSO-BPNN方法可以有效的运用于田间施肥指导,为珍贵树种重金属含量监测提供较为准确的预测。

关键词: 降香黄檀, 多光谱图像, 植被指数, 纹理特征, BP神经网络

Abstract:

Objective: This paper proposed a prediction method of total iron content (TIC) in Dalbergia odorifera leaves based on vegetation index and multi-spectral texture features, in order to provide an alternative approach for the diagnosis of heavy metal nutrition of precious tree species. Method: In this paper, D. odorifera saplings were subjected to four levels (CK, F1, F2, F3) of iron treatment. At the end of treatment, the leaves were collected, and multi-spectral images were obtained, from which vegetation indexes and texture parameters, such as texture parameters mean value (TFMV) and texture parameters variance (TFV), were extracted and calculated, and the relationship between the variables and TIC was analyzed. The variables significantly correlated with TFC at 0.05 and 0.01 levels were screened out by significance test. Then, correlation analysis (CA), principal component analysis (PCA), average impact value (MIV) and genetic algorithm (GA) were used for secondary screening. The results were used as input variables of particle swarm optimization-back feedback neural network (PSO-BPNN) to analyze and compare the prediction values. Results: 1) In the CK ~F2 gradient range, the growth amount of tree height, crown width and stem increased with the increase of iron application, while under gradient F3, the growth amount of tree height and crown width decreased and growth of stem increased significantly. 2) With the increase of leaf TIC, band B first decreased and then increased; band G was contrary to band B; band R first decreased and then increased, and then remained stable; bands RE and NIR showed a trend of rise all the time; With the increase of TIC, different changes occurred in different bands. In band B, the spectral reflectance in level CK, F1 and F2 decreased but that in level F3 increased. In band G, the changes were contrary to band B. In band R, the spectral reflectance in level CK and F1 decreased, while it in level F2 increased, and maintained stability in level F3. bands RE and NIR showed an upward trend in all levels. 3) Most VI and TIC were correlated at 0.01 and 0.05 levels. TFMV and TFV were also able to reflect leaf TIC, but TFV was superior to TFMV in terms of correlation level and number. From viewpoint of band, the correlation between bands RE and NIR and TIC was better than other bands in vegetation index and texture features. 4) The results obtained by different screening methods were different, among which CA and GA had the best and similar evaluation indicators. But the prediction values of GA in the range of 150-300 mg/kg were lower than the measured values, which is not suitable for field fertilization guidance. (5) The prediction results obtained by vegetation index alone were inaccurate, and the goodness of fit and prediction accuracy were improved by adding texture parameters. By comparison, the variance of texture parameters had a greater impact on the accuracy of the model. It was shown that the degree of dispersion of leaf texture could be used as a good auxiliary information for predicting TIC. Conclusion: Gradient fertilization of F1 and F2 can promote the growth and biomass accumulation of Dalbergia odorifera. According to the experimental data, the optimum total iron content in leaves is 150-300 mg/kg. In addition to vegetation index, MPV as an auxiliary factor can improve the goodness of fit and prediction accuracy of the model. The CA-PSO-BPNN method can be effectively applied to field fertilization guidance and provide more accurate prediction for monitoring the heavy metal content of precious tree species.

Key words: Dalbergia odorifera, multi-spectral image, vegetation index, texture feature, BPNN

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