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

文献类型： 外文期刊

第一作者： Luo, Juhua

作者： Luo, Juhua;Ma, Ronghua;Huang, Wenjiang;Zhao, Jingling;Zhang, Jingcheng;Huang, Muyi

作者机构：

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.

分类号：

  • 相关文献

[1]Classification Methods of Remote Sensing Image Based on Decision Tree Technologies. Jiang, Lihua,Wang, Wensheng,Yang, Xiaorong,Xie, Nengfu,Jiang, Lihua,Wang, Wensheng,Yang, Xiaorong,Xie, Nengfu,Cheng, Youping. 2011

[2]Research on remote sensing image processing based on discrete global grids. Zhang Shengmao,Zhou Sufang,Lv Zhenhua,Zhao Hui. 2010

[3]Study on the spatial differences and its time lag effect on climatic factors of the vegetation in the Longitudinal Range-Gorge Region. Bao YaJing,Song GuoBao,Li ZhengHai,Song GuoBao,Li ZhengHai,Gao Jixi,Lue, HaiYan,Cheng Yan,Wang HaiMei,Xu Tian.

[4]Data assimilation for crop yield and CO2 fixation monitoring in Asia by a photosynthetic sterility model using satellites and meteorological data. Kaneko, Daijiro,Kumakura, Toshiro,Yang, Peng. 2009

[5]Direct and indirect effects of wastewater use and herd environment on the occurrence of animal diseases and animal health in Pakistan. Elahi, Ehsan,Zhang, Liqin,Abid, Muhammad,Abid, Muhammad,Javed, Muhammad Tariq,Han Xinru.

[6]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

[7]FARMERS WILLINGNESS TO PURCHASE AGRICULTURAL INFORMATION. Zheng, Xiaoping,Tian, Dong,Vlad, L. B.,Antohi, V. M.,Liu, Zhanhu. 2014

[8]Simulation of changes in heavy metal contamination in farmland soils of a typical manufacturing center through logistic-based cellular automata modeling. Qiu, Menglong,Liu, Liming,Wang, Qi,Li, Fangbai,Chen, Junjian,Yang, Guoyi.

作者其他论文 更多>>

微信小程序