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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #306839
Title: Using satellite multispectral imagery for damage mapping armyworm (Spodoptera frugiperda) in maize damage at a regional scale

Author
item ZHANG, JINGCHENG - National Engineering Research Center For Information Technology In Agriculture
item Huang, Yanbo
item YUAN, LIN - National Engineering Research Center For Information Technology In Agriculture
item YANG, GUIJUN - National Engineering Research Center For Information Technology In Agriculture
item CHEN, LIPING - National Engineering Research Center For Information Technology In Agriculture
item ZHAO, CHUNJIANG - National Engineering Research Center For Information Technology In Agriculture

Submitted to: Pest Management Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2015
Publication Date: 1/15/2016
Citation: Zhang, J., Huang, Y., Yuan, L., Yang, G., Chen, L., Zhao, C. 2016. Using satellite multispectral imagery for damage mapping armyworm (Spodoptera frugiperda) in maize damage at a regional scale. Pest Management Science. 72:335-348.

Interpretive Summary: Insect monitoring and control is an important part of pest management in crop production. For effective insect control over crops a rapid assessment method is needed to timely reveal the scope and the severity of the damage. In this study scientists from National Engineering Research Center for Information Technology in Agriculture, China and USDA-ARS Crop Production Systems Research Unit, Stoneville, Mississippi, collaboratively developed satellite multispectral images to map armyworm damage for maize in 2012 in the corn fields in Tangshan region, Hebei Province in China. The results indicated that armyworm infestation could cause significant change in plant’s leaf area and suggested that the satellite imagery can be used for mapping crop insect damage at a regional scale.

Technical Abstract: Armyworm, as a destructive insect for maize, causes wide range of damage in both China and U.S. in recent years. To obtain the spatial distribution of damage area and assess the damage severity, a fast and accurate loss assessment method is of great importance for effective management. This study, taking an armyworm outbreak in Tangshan region, Hebei Province, China in 2012 as an example, attempted to apply satellite multispectral images to map its damage in corn field. A framework, starting from image preprocessing to insect damage mapping, was developed. Mapping performances between images on one day alone and a variation in two days, as well as between univariate and multivariate models, were examined and compared to identify the most effective way for mapping the insect damage. The result showed that armyworm infestation could cause significant change in plant’s leaf area index, which could serve as a basis of infestation monitoring. The mapping result with two-stage images outperformed the mapping result with one-day image. It was also found that an univariate model produced higher accuracy than a multivariate model, which allows us to map the insect damage in an easier way. A number of vegetation indices were also examined for their capability in characterizing the insect damage. The two-stage Modified Soil-Adjusted Vegetation Index (MSAVI) was identified as the optimal spectral feature that produced the highest mapping accuracy with overall accuracy of 0.79. The reasonable accuracy suggested that the satellite imagery would have a great potential to be used for mapping the maize damage caused by armyworm to assess its severity at a regional scale.