Long-distance migration of adult corn earworm moths (Helicoverpa zea), and several other noctuid moth species, facilitates seasonal expansion of pest populations and consequent increased infestations of agricultural crops on a continental scale in North America. Long-term field studies of population dynamics and migratory flights of H. zea and fall armyworm (Spodoptera frugiperda) in the United States were evaluated using X-band radar observations and profiles of atmospheric conditions. These studies identified characteristic patterns of migratory flight that are largely associated with vertical profiles of temperature and wind speed. Collective patterns of moth migrations were generally highly correlated with wind headings, but often at a significant angular deviation. Preliminary analyses are presented between moth distributions in the aerosphere estimated from discrete moth counts using X-band radar and bulk reflectivity data from NEXRAD Doppler radar. Identification of associations between atmospheric factors and noctuid population dynamics and migratory flights will improve the ability to predict infestations by pest species throughout their broad seasonal range expansion.

This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One camera captures normal color images, while the other is modified to obtain near-infrared (NIR) images. The color camera is also equipped with a GPS receiver to allow geotagged images. A remote control is used to trigger both cameras simultaneously. Images are stored in 14-bit RAW and 8-bit JPEG files in CompactFlash cards. The second-order transformation was used to align the color and NIR images to achieve subpixel alignment in four-band images. The imaging system was tested under various flight and land cover conditions and optimal camera settings were determined for airborne image acquisition. Images were captured at altitudes of 305–3050 m (1000–10,000 ft) and pixel sizes of 0.1–1.0 m were achieved. Four practical application examples are presented to illustrate how the imaging system was used to estimate cotton canopy cover, detect cotton root rot, and map henbit and giant reed infestations. Preliminary analysis of example images has shown that this system has potential for crop condition assessment, pest detection, and other agricultural applications.

Many methods for trajectory simulation, such as Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), have been developed over the past several decades and contributed greatly to our knowledge in insect migratory movement. To improve the accuracy of trajectory simulation, we developed a new numerical trajectory model, in which the self-powered flight behaviors of insects are considered and trajectory calculation is driven by high spatio-temporal resolution weather conditions simulated by the Weather Research and Forecasting (WRF) model. However, a rigorous evaluation of the accuracy of different trajectory models on simulated long-distance migration is lacking. Hence, in this study our trajectory model was evaluated by a migration event of the corn earworm moth, Helicoverpa zea, in Texas, USA on 20–22 March 1995. The results indicate that the simulated migration trajectories are in good agreement with occurrences of all pollen-marked male H. zea immigrants monitored in pheromone traps. Statistical comparisons in the present study suggest that our model performed better than the popularly-used HYSPLIT model in simulating migration trajectories of H. zea. This study also shows the importance of high-resolution atmospheric data and a full understanding of migration behaviors to the computational design of models that simulate migration trajectories of highly-flying insects.