Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection

The issue of scale and change of support in the spatial analysis of environmental data.

The concept of spatial scale is of fundamental importance in the analysis of spatial data. Issues such as scaling, multi-scale, data fusion, and change of support when analyzing spatial data are currently not given due attention. There is no unique or only one approach to deal with scaling or change of scale. Geostatistics offers several tools to deal with these problems, such as the regularization technique that evaluates the effect of scaling by operating on spatial functions, such as the covariance and variogram, rather than on data. In this chapter, a case study is presented to illustrate how geostatistics can deal with the issue of spatial scale

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors

Mapping soil properties for unmanned aerial system-based environmental monitoring.

Optimal management of water and land resources is based on process-based eco-hydrological models (Kutilek and Nielsen, 1994), which have been increasingly used to solve several scientific and practical problems, such as retrieving soil moisture status and water infiltration patterns, assessing vegetation stress and drought conditions, controlling the spray of pesticides, monitoring potential landslides, evaluating post-fire damages and related restoration practices. The reliability of numerical simulations in critical zone (CZ) processes depends on an accurate parameterization of the soil hydrological behavior that is traditionally assessed using direct measurement methods. Nevertheless, for studies devoted to relatively large spatial scales, direct methods are hampered by the time and costs required for field activities and laboratory analyses. To circumvent somehow these limitations, pedotransfer functions (PTFs) were proposed to estimate the soil hydraulic properties. Basically, a PTF exploits the knowledge of readily available or easily measurable basic information on soil physical and chemical properties to infer the soil water retention and hydraulic conductivity functions. In this chapter, the methods for mapping physical, chemical, and other key properties of the soil will be discussed jointly with a presentation of recently developed proxy tools for monitoring soil-vegetation characteristics through unmanned aerial systems (UASs). Multi- or hyper-spectral sensors installed on a UAS enable field-scale spectral measurements to be performed even at centimeter-scale thus allowing the prediction of soil properties at unprecedented grid resolution. Exploiting the high potential offered by UAS-based multi-spectral imaging, a new family of soil transfer functions, here called spectral transfer functions (STFs), is proposed to estimate the soil hydraulic properties from spectral measurements. The input and/or output data of the PTF/STF mandate the use of advanced interpolation techniques to reliably obtain the soil hydraulic behavior in a study area for modeling purposes. Spatial interpolation is an important task for running distributed hydrological models over relatively large areas. Therefore, a key component of this chapter is devoted to the issues of scale, spatial variability, and geostatistical mapping of soil characteristics