您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data

文献类型： 外文期刊

作者： Luo, Juhua 1 ; Huang, Wenjiang 2 ; Zhao, Jingling 3 ; Zhang, Jingcheng 3 ; Ma, Ronghua 1 ; Huang, Muyi 4 ;

作者机构： 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing, Jiangsu, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

4.Anhui Jianzhu Univ, Dept Environm & Energy Engn, Hefei, Peoples R China

关键词： Wheat aphid;Logistic regression;Remote sensing image;Meteorological data;Probability

期刊名称：OPTIK （ 影响因子：2.443； 五年影响因子：1.955 ）

ISSN： 0030-4026

年卷期： 2014 年 125 卷 19 期

页码：

收录情况： SCI

摘要： The occurrence and prevalence of wheat aphid has severe impact on wheat quality and bring about wheat yield loss. Infestation models commonly based on in situ meteorological data can be effective, but are usually lacking spatialized information, which could be provided using multispectral remote sensing datasets. The purpose of this study was to develop a prediction model for aphid occurrence probability by combining remote sensing images and meteorological data in a logistic regression based framework. In such framework, the predictor variables are: land surface temperature (LST), normalized difference vegetation index (NDVI) and perpendicular drought index (PDI) derived from satellite remote sensing image and temperature, precipitation and wind speed coming from meteorological stations. Logistic regression estimated predictor coefficients showed that LST was the most important factor inducing aphid occurrence, followed by NDVI, temperature, precipitation, wind speed and PDI. The prediction accuracy of the model was evaluated by calculating receiver operating characteristics (ROC) against reference data, scoring a value of area under the ROC curve of 0.993, and resulting in an overall predicted accuracy of 94.4% for the estimated subset. In addition, the results suggested that the probability of aphid occurrence obtained by the logistic regression model had a positive correlation with aphid damage level from field data. Based on the validation subset, it was observed that there were lower omission error with 23.68 and commission error with 23.81% and higher overall accuracy with 75.61% when the cut-off probability (p) value was 0.45. The model developed can be a useful tool to predict aphid occurrence and incidence over winter wheat with some advance, and it could be effective in protecting winter wheat from aphid infestation. (C) 2014 Elsevier GmbH. All rights reserved.

  • 相关文献

[1]Forecasting of Powdery Mildew disease with multi-sources of remote sensing information. Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yuan, Lin. 2014

[2]DISCRIMINATING WHEAT APHID DAMAGE LEVEL USING SPECTRAL CORRELATION SIMULATING ANALYSIS. Huang Wenjiang,Luo Juhua,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台