Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

Effective and efficient systems for surveillance, eradication, containment and management of biosecurity threats require methods to predict the establishment, population growth and spread of organisms that pose a potential biosecurity risk. To support Victorian forest biosecurity operations, Agriculture Victoria has developed a landscape-scale, spatially explicit, spatio-temporal population growth and dispersal model of a generic pest pine beetle. The model can be used to simulate the incursion of a forestry pest from a nominated location(s), such as an importation business site (approved arrangement, AA), into the surrounding environment. The model provides both illustrative and quantitative data on population dynamics and spread of a forestry pest species. Flexibility built into the model design enables a range of spatial extents to be modelled, from user-defined study areas to the Victoria-wide area. The spatial resolution of the model (size of grid cells) can be altered from 100 m to greater than 1 km. The model allows core parameters to be altered by the user, enabling the spread of a variety of windborne insect species and pathogens to be investigated. We verified the model and its parameters by simulating and comparing the outputs with the 1999/2000 Melbourne incursion, but no establishment of a forestry pest beetle was believed to be Monochamus alternatus Hope (Coleoptera: Cerambycidae). The model accurately predicts the distance and direction of the historic incursion, and the subsequent failure to establish is due to low overall population density of the pest species.