地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (9): 1705-1713.doi: 10.12082/dqxxkx.2021.200475

• 遥感科学与应用技术 • 上一篇    

融合植被遥感数据的北京市次日花粉浓度预测

卞萌1(), 郭树毅2, 王威2, 欧阳昱晖3,*(), 黄颖菁4, 费腾4   

  1. 1. 武汉大学 遥感信息工程学院,武汉 430079
    2. 国家林业和草原局调查规划设计院,北京 100714
    3. 首都医科大学附属北京同仁医院,北京100730
    4. 武汉大学资源与环境科学学院,武汉 430079
  • 收稿日期:2020-08-20 修回日期:2021-02-19 出版日期:2021-09-25 发布日期:2021-11-25
  • 通讯作者: *欧阳昱晖(1970— ),女,云南昆明人,博士,主任医师,主要研究方向为鼻及鼻窦炎症性疾病,过敏性疾病的临床和基础研究。E-mail: oyyuhui@sina.com
  • 作者简介:卞 萌(1981— ),女,山东德州人,博士,高级实验师,主要从生态环境定量遥感研究。E-mail: bian@whu.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(2017YFB0503600)

Next-day Prediction of Pollen Concentration in Beijing by Integrating Remote Sensing Derived Leaf Area Index

BIAN Meng1(), GUO Shuyi2, WANG Wei2, OUYANG Yuhui3,*(), HUANG Yinqin4, FEI Teng4   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    2. Survey Planning and Design Institute of the State Forestry and Grassland Administration, Beijing 100714, China
    3. Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital (Capital Medical University), Wuhan 100730, China
    4. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;
  • Received:2020-08-20 Revised:2021-02-19 Online:2021-09-25 Published:2021-11-25
  • Supported by:
    National Natural Science Foundation of China, Key Program(2017YFB0503600)

摘要:

中国国土绿化状况公报指出,2010—2020年中国许多城市的绿化面积增加、绿化质量提高,可随之而来的影响人体健康的致敏性花粉风险也逐渐提高。本文利用遥感手段获得北京市乔木和草地生长区域平均植被叶面积指数(LAI)时间序列作为植被物候信息,并将其作为花粉浓度预测因子之一,结合日气象数据,使用具有外部输入的非线性自回归神经网络模型(NARXnet),进行北京市次日花粉浓度的预测。结果显示:① 通过逐步回归计算,对于春季数据,日均气温3日平滑,积温,叶面积指数(LAI)和叶面积指数一阶导为次日花粉浓度预测的关键变量;对于秋季数据,日均气温、平均风速、最低日气温、日均气温3日平滑、积温和叶面积指数(LAI)为次日花粉浓度预测的关键变量;② 加入遥感物候信息可显著地提高NARXnet模型的春秋时段的花粉浓度的预测精度。使用本文提出的结合叶面积指数的NARX模型后,预测模型的总体精度为71%。由此,本研究认为在原有气象因子的基础上,辅之以用遥感技术手段获取的大面积植被物候信息,如叶面积指数动态,可作为预测次日花粉浓度的一种有效手段。

关键词: 风传花粉, 植被遥感, 浓度预测, 非线性自回归神经网络, 时间序列

Abstract:

There has been an increase in the area and quality of vegetation coverage in many cities of China, which leads to a concomitant increase in the risk of allergenic pollen that affects human health. However, there is still limitation in the accuracy and regional applicability of pollen forecasting services, partly because pollen concentration predictors are more focused on meteorological observations rather than phenological observation of plants. For the seasonal trend of allergenic pollen concentration, phenological observations of vegetation may be an important indicator as well as meteorological factors, because the characteristics of vegetation phenology are directly correlated with pollen release. In this study, the time series Leaf Area Index (LAI) for tree and grass covers that reflects vegetation growth processes was derived from remote sensing techniques and represented as one of the predictors of pollen concentration in air. By combining the derived LAI information with the daily meteorological data, Nonlinear Autoregressive Neural Networks with External Input (NARXnet) combined with stepwise regression were employed to predict the pollen concentration in air of the next day in Beijing. The results show that (1) the three-day moving average of daily temperature, the cumulative temperature, LAI, and the first-order derivatives of LAI were key predictors of the next-day pollen concentration for the spring season, while the mean daily temperature, mean wind speed, the minimum daily air temperature, the three-day moving average of daily temperature, the cumulative temperature, and the LAI were key predictors of the next-day pollen concentration for the fall season; (2) in Beijing, the inclusion of remotely sensed phenological information could significantly improve the prediction accuracy of the pollen concentration for both the spring and autumn seasons from NARXnet model. According to the results, we conclude that, in combination with the meteorological factors, vegetation phenology information such as LAI obtained from remote sensing is an effective predictor of the next-day pollen concentration.

Key words: windborne pollen, vegetation remote sensing, pollen concentration prediction, NARXnet, time series