title: Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale.
abstract: BACKGROUND
  Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data.
  
  
  RESULTS
  Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79.
  
  
  CONCLUSIONS
  A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice.
authors.affiliation: Beijing Research Centre for Information Technology in Agriculture, Beijing, China.|National Engineering Research Centre for Information Technology in Agriculture, Beijing, China.|Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, China.|Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China.
authors.forename: Jingcheng
authors.initials: J
authors.lastname: Zhang
authors.affiliation: USDA-ARS, CPSRU, 141 Experiment Station Road, Stoneville, MS 38776, USA.
authors.forename: Yanbo
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Beijing Research Centre for Information Technology in Agriculture, Beijing, China.|National Engineering Research Centre for Information Technology in Agriculture, Beijing, China.|Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, China.|Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Yuan
authors.affiliation: Beijing Research Centre for Information Technology in Agriculture, Beijing, China.|National Engineering Research Centre for Information Technology in Agriculture, Beijing, China.|Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, China.|Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China.
authors.forename: Guijun
authors.initials: G
authors.lastname: Yang
authors.affiliation: Beijing Research Centre for Information Technology in Agriculture, Beijing, China.|National Engineering Research Centre for Information Technology in Agriculture, Beijing, China.|Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, China.|Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China.
authors.forename: Liping
authors.initials: L
authors.lastname: Chen
authors.affiliation: Beijing Research Centre for Information Technology in Agriculture, Beijing, China.|National Engineering Research Centre for Information Technology in Agriculture, Beijing, China.|Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, China.|Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China.
authors.forename: Chunjiang
authors.initials: C
authors.lastname: Zhao
mesh_terms: D000818:Animals; D002681:China; D005240:Feasibility Studies; D008962:Models, Theoretical; D010935:Plant Diseases / Q000469:parasitology / Q000706:statistics & numerical data*; D018515:Plant Leaves / Q000469:parasitology; D058998:Remote Sensing Technology / Q000379:methods*; D012987:Soil; D018411:Spodoptera / Q000502:physiology*; D003313:Zea mays / Q000469:parasitology*
chemical_list: D012987:Soil
keywords: armyworm; maize; mapping; modified soil-adjusted vegetation index; multispectral remote sensing