What are you interested in finding out?
Where are the pests?
Early detection?
Delimiting extent of impact/ area infested?
Deployments strategies will be integral to the integration of sUAS into surveilance programs, especially when attempting to quantify returns on investment. Quite simply, it is not cost effective to deploy sUAS everywhere, for every use case. Instead, this branch makes the user aware of different modeling strategies that can be used to select areas of interest, or to focus deployment to areas where the incursion is most likely to occur. Questions or statements are further refined to direct site visitors to different modeling appraoches, but also directing to other projects funded by the PBCRC.
Useful references
Baxter and Hamilton 2016
Claderon, R., Navas-Cortés, J.A., Lucena, C. and Zarco-Tejada, P. (2013) 'High-resolution hyperspectral and thermal imagery acquired from UAV platforms for early detection of Verticillium wilt using fluorescence, temperature and narrow-band indices', in UAV-based Remote Sensing Methods for Monitoring Vegetation, Cologne, Germany, 8.
Calderón, R., Navas-Cortés, J. and Zarco-Tejada, P. (2015) 'Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas', Remote Sensing, 7(5), 5584.
Hamilton, G., Rasmussen, R., Müllerová, J., Pergl, J. and Pyšek, P. (2014) 'Defining the spatiotemporal surveillance space for alien species’ invasions using approximate Bayesian computation', in Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), Elsevier, 1347-1352.
Matese, A., Toscano, P., Di Gennaro, S., Genesio, L., Vaccari, F., Primicerio, J., Belli, C., Zaldei, A., Bianconi, R. and Gioli, B. (2015) 'Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture', Remote Sensing, 7(3), 2971.
Puig et al., “Assessment of crop insect damage using unmanned aerial systems: a machine learning approach,” in Proc. of 21th Int. Congress on Modelling and Simulation, pp. 1420–1426 (2015).
Sandino J., Pegg G., Gonzalez F., and Smith G. (2018) 'Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence', Sensors, 18(4):944. Available online: [https://doi.org/10.3390/s18040944]
Vanegas, F., Bratanov, D., Powell, K., Weiss, J. and Gonzalez, F. (2018) 'A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data', Sensors, 18(1), 260.
Weiss, J., Clements, D., Dugdale, T., Hamilton, G., Gonzales, F., Hauser, C. and McCornack, B. (2015) 'Evaluating the effectiveness of UAVs for pest management', in MODSIM 2015: Modelling and Simulation, Gold Coast, Queensland.
Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., González, M.R. and de Frutos, A. (2005) 'Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy', Remote Sensing of Environment, 99(3), 271-287, available: http://dx.doi.org/http://dx.doi.org/10.1016/j.rse.2005.09.002.
Lindenmayer, DB, Likens, GE, Franklin, JF (2018) Earth Observation Networks (EONs): Finding the Right Balance. Trends in Ecology & Evolution 33, 1-3.
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