区间优化提高牧草粗蛋白含量遥感估算精度

牧草粗蛋白含量的估算对于草地营养状况监测以及草地资源可持续利用和管理具有重要意义。针对当前地面遥感与航天遥感的限制,尝试基于航空飞艇高光谱遥感进行牧草粗蛋白含量估算研究,以便更好满足智慧畜牧业的应用需求。针对现有植被生化含量反演不确定性的问题,基于区间划分与判别分析的思想,提出了一种结合等宽区间划分法、逐步判别分析与Fisher判别法相结合的多步骤牧草粗蛋白含量估算模型,以青海省海晏县金银滩草原为研究区进行方案可行性研究。结果表明,提出的模型能够较好的实现牧草粗蛋白含量的精准估算,设定的3种不同划分区间(3区间,5区间和7区间)所对应的全样本检验精度和交叉检验精度分别达到了95%、90%,95%、80%和85%、65%。与传统的逐步线性回归方法相比,估算精度有了明显提高(总体检验精度提高18.7%~70%,交叉检验精度提高20%~62.5%)。3种不同区间模型(3区间,5区间和7区间)所对应的特征波段依次为870、815、802、737、391 nm;988、391、398、405、548 nm;870、815、946、888、839 nm。此外,模型的估算精度与划分区间数量成反比关系,在实际中可以根据不同的应用需求来调节划分区间数量。综上,该文利用航空飞艇高光谱数据实现了牧草粗蛋白含量的精准反演估算,对后期牧场营养状况实时监测以及草地资源可持续利用和管理具有重要的指导意义。

Improving remote sensing estimation accuracy of pasture crude protein content by interval analysis

Abstract: Crude protein is the key indication for evaluation of the quality and feeding value of pasture grass. Estimating crude protein content of pasture grass is necessary for monitoring grassland nutrition status, sustainable utilization and management of grassland resources, eventually preventing grassland degradation. Hyperspectral remote sensing technology is supplied as a new approach for scientists to study properties and processes of ecosystems and their inner biochemical content variation. In view of the limitation of ground remote sensing and astronautics remote sensing, we try to construct estimation model of pasture crude protein content based on the hyperspectral aerial airship imaging system, in order to meet the application needs of smart animal husbandry. In view of the uncertainty problems of traditional biochemical parameter inversion models and practical application needs in agriculture and animal husbandry production, we propose a multi-step pasture crude protein content estimation model, which combined the equal width interval division method, stepwise discriminant analysis and Fisher discriminant method. An experiment was designed to determine whether pasture crude protein content could be predicted by means of the developed strategy. Jinyintan grassland, a typical prairie in Haiyan County, Qinghai Province was chosen as the research area. The hyperspectral data were acquired with the hyperspectral mapping system installed on an airship (named ASQ-HAA380), which was developed by our research group. Pasture crude protein samples were collected at the same time and analyzed in Qinghai University. The results show that the proposed model can accurately estimate the crude protein content of pasture. The test accuracy of the 3 models with different interval numbers (3, 5, and 7) for all samples is 95%, 95% and 85% respectively, while their corresponding cross-check accuracy is 90%, 80% and 65% respectively. Compared with the traditional stepwise linear regression method, the estimation accuracy also has a great improvement (overall test accuracy is increased by 18.7%-70%, and cross test accuracy is increased by 20%-62.5%). The selected bands of the 3 models with different interval numbers (3, 5, and 7) are 870, 815, 802, 737, 391 nm; 988, 391, 398, 405, 548 nm; and 870, 815, 946, 888, 839 nm respectively. In addition, we can adjust content interval range according to different application requirements. And our experimental results indicate that the model accuracy is inversely proportional to interval number. In general, this paper has successfully realized the accurate estimation of the crude protein content of pasture with hyperspectral aerial airship imaging data, which provides reference and technical basis for quantitative estimation of crude protein content and efficient implementation of precision livestock husbandry based on hyperspectral images, and also lays the foundation for the development of intelligent livestock husbandry in the future.