Use of Geostatistics for Multi-Scale Spatial Modeling of <i>Xylella fastidiosa</i> subsp. <i>pauca</i> (<i>Xfp</i>) Infection with Unmanned Aerial Vehicle Image

Abstract

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has been spreading widely, as in plant pest control. The collection of huge amounts of spatial data raises various issues including that of scale. Data from UAVs generally explore multiple scales, so the problem arises in determining which one(s) may be relevant for a given application. The objective of this work was to investigate the potential of UAV images in the fight against the Xylella pest for olive trees. The data were a multiband UAV image collected on one date in an olive grove affected by Xylella. A multivariate geostatistics approach was applied, consisting firstly of estimating the linear coregionalization model to detect the scales from the data; and secondly, of using multiple factor kriging to extract the sets of scale-dependent regionalized factors. One factor was retained for each of the two selected scales. The short-range factor could be used in controlling the bacterium infection while the longer-range factor could be used in partitioning the field into three management zones. The work has shown the UAV data potential in Xylella control, but many problems still need to be solved for the automatic detection of infected plants in the early stages.

Keywords

• multi-band image
• linear model of coregionalization (LMC)
• nested variogram
• multiple factor kriging
• scale-dependent factor