Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Abstract

Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second flights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for detecting O. nubilalis-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments are not as effective as those targeting chlorophyll. Analysis of image data suggests that feeding and stem boring by O. nubilalis larvae may increase the rate of plant senescence causing detectable differences in plant biomass and vigor when compared with control plots. Further, we identified an approximate time frame of 5-6 wk postinoculation, when spectral differences of manually inoculated "second" generation O. nubilalis plots seem to peak.

Remote Sensing and Reflectance Profiling in Entomology

“…Numerous methods have been used to classify entomological remote sensing data, including analysis of reflectance values of single spectral bands (93,98), spectral band indices (17,24,79,85,90,95), partial least square (PLS) (2,4,63), principal component analysis (PCA) (90,102), linear discriminant analysis (LDA) (94), decision trees (37), neural network (73), support vector machine (SVM) (111), variogram analysis (90-93, 95, 97, 100), and spatial pattern analysis (5-7). Sometimes reflectance values of spectral bands are transformed prior to classification, and the most common transformations include conversions of reflectance profiles into first-or second-order derivatives (26).…”

“…Even with the newest source of commercially available satellite imagery (e.g., Quickbird, Worldview-2, and GeoEye-1), practical constraints for use of satellite imagery in agricultural insect pest management include slow data delivery time to end users, fixed orbital cycles with generally low temporal resolution, generally low spatial resolution, and weather dependency (challenges imposed by cloud cover) of image quality (111). Numerous studies describe the use of airborne multispectral and hyperspectral reflectance data acquired from crop plants (17,19,86,131,150,152,156) and forests (130) under arthropod-induced stress.…”

Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data

“…It is especially suitable for monitoring spatial and temporal variations of land surface features at regional, continental, and even global scales (Su et al, 2003). Satellite-based indicators are playing an increasingly important role in disaster monitoring and evaluating, such as flood (Townsend and Walsh, 1998;Brivio et al, 2002;Sanyal and Lu, 2005), drought (Su et al, 2003;Wan et al, 2004;Bhuiyan et al, 2006;Vicente-Serrano, 2007), pest and disease injuries (Carroll et al, 2008;Luedeling et al, 2009).…”

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

“…Despite the fact that remote detection and quantification of foliar disease and insect infestation have been successful in plant sciences [61][62][63][64][65][66][67][68] and regional occurrence of WSM in the US and other countries, the application of moderate resolution multitemporal imagery to WSM epidemics is still not well studied. Ability to utilize moderate resolution imagery to discriminate and separate WSM from healthy areas within a field can greatly improve monitoring population dynamics, understanding disease ecology, and developing long-term site-specific management practices for WSM.…”