摘要
粗蛋白(crude protein,CP)是评价牧草品质和饲用价值的重要指标。利用高光谱技术实现大面积牧草CP含量实时、准确、无损监测是草地营养状况监测的重要内容。为掌握青海省海晏县金银滩草原牧草CP含量的分布状况,该文采用课题组航空飞艇搭载自主集成高光谱成像系统获取高分辨率高光谱图像,对高光谱数据进行光谱衍生变换,采用不同建模方法构建CP含量的反演模型。选取最佳组合的2种光谱指数为自变量构建牧草CP含量的单变量模型。对于包络线去除的衍生光谱和对数、归一化、一阶微分及其衍生组合变换光谱,使用逐步判别分析法筛选各光谱变量的特征波段作为自变量,构建牧草CP含量的多元线性和非线性回归模型,综合比较各模型的精度选择最优反演模型。结果表明,不同光谱变量相比,微分光谱变量对牧草CP含量拟合效果较好,R^2均达到0.794以上。不同多元回归模型相比,非线性回归模型精度高于对应的线性回归模型。以光谱对数后再一阶微分变量(D(log(R)))构建的多元非线性回归模型为牧草CP含量最优估算模型,R^2为0.918,RMSE为0.054。将D(log(R))建立的非线性回归模型应用于高光谱图像上,绘制研究区牧草CP含量空间分布图。研究为大区域尺度CP含量的定量反演及精准畜牧业的高效实施提供参考和技术依据,也为今后智慧畜牧业的发展奠定基础。
Crude protein（CP） is the key indicator for evaluation of the quality and feeding value of pasture grass. Timely, accurate and non-destructive assessment of pasture grass CP content is important for pasture grass growth monitoring and making-decisions for adjusting stocking rate and pasture management, eventually preventing grassland degradation. Hyperspectral remote sensing technology provides the potential for monitoring the nutrition in large areas of grassland. In order to obtain the distribution of pasture grass CP content in Jinyintan Grassland, which is a typical prairie in Haiyan County, Qinghai Province, a new type of hyperspectral imaging system based on high altitude airship（named ASQ-HAA380） was used to collect the high-resolution hyperspectral images, and the ground-based pasture grass CP samples datasets were collected at the same time and analyzed in Qinghai University. The aim of this study was to establish the regression model and seek the optimal model to estimate CP content and draw its distribution map. This study analyzed the possibility using several spectral variables and different modeling methods. On the basis of a comprehensive analysis of the hyperspectral data, the best spectral indices i.e. simple ratio spectral index（SR） and normalized difference spectral index（ND） were taken as independent variables to build univariate models. Besides, the multivariate stepwise linear regression method and multivariate nonlinear regression method were used to build estimate models of other spectral variables, including original reflectance spectrum（R）, first derivative of reflectance（D（R））, logarithm transformation of reflectance（log（R））, normalized transformation of reflectance（N（R））, first derivative of log（R）（D（log（R）））, logarithm transformation of N（R）（log（N（R）））, first derivative of N（R）（D（N（R）））, band depth（BD）, and continuum removed derivative reflectance（CRDR）. Afterwards, the accuracies of these models were evaluated through cross-validated coefficient of determination（R2） and cross-validated root mean square error（RMSE）. The results showed： 1） Derivative spectral variables could effectively estimate CP content with high stable ability among all spectral variables, and R2 is more than 0.794. 2） Compared with these multiple regression models, the nonlinear regression model had higher precision than the corresponding linear regression model. 3） The accuracy of the multiple nonlinear regression model of D（log（R）） built in the study was the highest, R2 was 0.918 and RMSE was 0.054, and the model of D（log（R）） was the optimal model for prediction of CP content. The inversion nonlinear regression model of D（log（R）） was applied to the hyperspectral image to obtain the spatial distribution of CP content in the study area. The research provides reference and technical basis for the quantitative inversion of CP content in large area scales and the efficient implementation of precision livestock husbandry based on hyperspectral images, and also lays the foundation for the development of wisdom livestock husbandry in the future.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第3期188-194,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金面上项目(NSFC 41571369)
青海省科技计划项目(2016-NK-138)
北京市长城学者(CIT&TCD20150323)
关键词
遥感
模型构建
验证
高光谱图像
粗蛋白
光谱指数
remote sensing
model building
vertification
hyperspectral image
crude protein
spectral indices
