Exploring the relationship between cyberbullying and unnatural child death: an ecological study of twenty-four European countries

King-Wa Fu
Chung-Hong Chan
Patrick Ip
Exploring the relationship between cyberbullying and unnatural child death: an ecological study of twenty-four European countries
R E S E A R C H A R T I C L E Open Access
Background: Internet risk has been recognised as a child safety problem, but evidence is insufficient to conclude that a child's online risk exposure can lead to physical harm. This study aims to explore the ecological relationship between Internet risk exposure and unnatural child death. Methods: Multiple secondary data sources were used: online exposure to content about self-harm, cyberbullying, and Internet addiction data (EU Kids Online survey, 2010); and mortality data (European Detailed Mortality Database, 2010 or the latest year if not available) of 24 European countries. Correlations were found using quasi-Poisson regression. Countries' prevalence rates of psychiatric problems (European Social Survey Round 3 and 6, 2006 and 2012) were used to test for possible spuriousness.Results: This study finds that countries with higher rates of cyberbullying were more likely to have a higher incidence of unnatural child death. A 1 percent rise in the prevalence of cyberbullying translated into a 28% increase in risk of unnatural child death (95% CI: 2%-57%). No evidence was found to substantiate confounding effect of the national prevalence of depressive symptoms or traditional bullying. Conclusions: Explanations are given for the findings. We conclude that intervention programs designed to serve as precautionary measures for risk minimisation should be considered.

Background

While the Internet is considered as an essential platform through which the younger generation can learn effectively, participate in a variety of social and civic engagements, and interact with a broader spectrum of human activities [1], concern over younger people's encounters with undesirable Internet content or risk is pervasive among health professionals, parents, and policy makers [2,3]. Broadly speaking, "Internet risk" is used as a collective term referring to the possibility of an unpleasant outcome, such as loss, injury, or harm, linked to an individual's online exposure, which can be classified as online content, contact, and conduct [2]. For instance, exposure to suicide content on the Internet and cyberbullying may lead to suicidal behaviours, which cause public health concern [4,5]; Internet gaming addiction is included in Section III of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a condition that requires further research before it might become a new disorder [6]. Individuals with Internet addiction are often found to have depressive symptoms and suicidal ideation [7]. Young victims of online harassment have become increasingly prevalent [8], and such online experience has been found to be associated with their suicidal behaviours [9,10]. In the light of these findings, widespread concern has escalated over a potential "child safety" problem [2,3]. Guidelines have been developed by paediatricians to encourage parental control and intervention [11,12].

But an opposite view is held by social scientists who contend that the widespread concern over harmful consequences of online risk is merely a "moral panic" [13]. The critique draws heavily on the argument that despite a pervasive public anxiety, no empirical evidence has been established to support a real threat to a child's physical safety literally caused by exposure to Internet risks, even though anecdotal evidence occasionally appears in the mass media [14]. Another consideration is that the Internet is only a secondary venue for manifest risk, whereas the victim of online harm may have been susceptible to other risk in the physical world, such as bullying or psychiatric problems [15], which may create a spurious relationship and thus confound the results.

In Europe, prevalence of Internet risks has been well documented [2,16]. For example, 7% of European children claimed to have experienced cyberbullying and 7% were exposed to "the websites that discuss ways of physically harming and hurting oneself" in the past twelve months [2]. In this study, we draw on secondary survey data and the mortality data sources collected from twenty-four European countries and examine the ecological relationship between Internet risk and unnatural child death.

Method

Data sources

Children's Internet risk data were derived from the dataset released by the Economic and Social Data Service's EU Kids Online survey (Study No. 6885, EUKOS) [17]. The EUKOS provides the data on Internet-related behaviours of children and parents in 25 European countries, including Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Lithuania, The Netherlands, Norway, Poland, Portugal, Romania, Slovenia, Spain, Sweden, Turkey, and the United Kingdom. The data were collected in a face-to-face survey in homes with Internet users aged 9 to 16 from the 25 countries; in all, 25,142 children were interviewed in 2010. The survey methodology and findings have been reported elsewhere [2,16]. Since another major data source in this studythe EDMD mortality databaseonly provides aggregated data in age groups 10-14 and 15-19 years, data samples of children aged 10-14, i.e. shared age range in both EUKOS and EDMD datasets, were selected for the analysis.

Four Internet risks were chosen and defined as follows:

1) Exposure to online information on self-harm or suicide: answering "Yes" to "Have you seen websites where people discuss ways of physically harming or hurting themselves or ways of committing suicide (either one)?"; 2) Experience of online and traditional bullying:

answering "Yes" to "Has someone acted in this kind of hurtful or nasty way [this can include teasing someone in a way that the person did not like (online), hitting, kicking or pushing someone around, or leaving someone out of things (traditional)] to you on the Internet?"; 3) Experience of exclusively online bullying (excluding traditional bullying): respondent confirmed having had "the experience of online bullying" but answered "no" when asked if the experience had occurred "in person, face to face" and "by mobile phone calls, texts or image/video texts"; 4) Internet addiction (measured by a composite score): choosing an option among "very often", "fairly often", "not very often", and "never/almost never" for a five-item scale: How often have the following things happened because of the Internet? 1) "Going without eating or sleeping", 2) "feeling bothered when I cannot be on the Internet", 3) "catching myself surfing when I'm not really interested", 4) "spending less time than I should with either family, friends or doing schoolwork", and 5) "trying unsuccessfully to spend less time on the Internet".

The reference period for the above questions was twelve months. Missing responses were excluded from the analysis.

Based on each country's data samples, prevalence rates of exposure to online information on self-harm or suicide, experiences of online and traditional bullying, experiences of exclusively online bullying, and the mean score of "Internet addiction" were calculated.

The European Detailed Mortality Database (EDMD) comprises the number of deaths for each European country, which are stratified by year, cause of death, age group, and sex [18]. The cause of death in EDMD was coded using the International Classification of Diseases (ICD-10), tenth revision [19], except for Greece. The sex and age stratified population figures of each country are also provided in the EDMD. Annual unnatural child death is defined as the collection of death causes including accidents (ICD-10: V01-X59), self-harm (ICD-10: X60-X84), assaults (ICD-10: X85-Y09), and undetermined cause of death (ICD-10: Y1-Y34) for children who died at age 10 to 14. For data from Greece, the cause of death is coded using ICD-9, the ninth revision [20]. The equivalent definition of unnatural death is devised as those who died from causes of death categorised with ICD-9 codes 800-999. For each country, the annual mortality incidence rate of unnatural deaths of children aged 10-14 was calculated (per 100,000).

Most countries' mortality data were based on year 2010 figures, i.e. same year as the EUKOS study. When unavailable, the latest figures were used. Turkey's data were not analysed because its death data were not available in the EDMD. Finally, data of twenty-four countries were included and analysed.

In the collected dataset, there were 1,013 unnatural child deaths (ages 10-14) among the twenty-four European countries. Of that total, 789 (77.9%) were accidental deaths, 138 (13.6%) were suicide deaths, 38 (3.8%) were caused by various forms of assault, and 48 (4.7%) were classified as undetermined causes of death. The unnatural death mortality incidence (ages [10][11][12][13][14] was ordered from the lowest 0 (Cyprus) to the highest 12.6 per 100,000 (Romania).

We also tested whether spurious associations of the national psychiatric problems might exist between Internet risk and unnatural causes of death, i.e. testing mediating and confounding effects using the same statistical procedure [21]. Proxy measures of a country's prevalent rates of psychiatric problems were derived from the 2006 and 2012 European Social Survey respectively (ESS3 and ESS6) [22,23]. As the two datasets do not provide age breakdowns, respondents who were 18 years old or younger were selected as a proxy for each country. The depression symptom score was calculated using an eight-item version of the Center for Epidemiologic Studies Depression Scale Revised (8-item CES-D) [24]. It ranges from 0 to 24 and the clinical cut-off score is chosen as 7 [25]. It is interpreted as a higher score indicating a greater severity of depressive symptoms. Each country's prevalence rates of depressive symptoms in 2006 and 2012 were computed to test for a possible spurious association respectively. Since some countries' CES-D scores were not available in the two datasets, Czech Republic, Greece, Italy, Lithuania, and Romania were excluded in the 2006 spuriousness test. Austria, Greece, and Romania were excluded in the 2012 test.

The protocol of this secondary data analysis was approved by the Human Research Ethics Committee for Non-Clinical Faculties, The University of Hong Kong.

Statistical analysis

The incidence of unnatural deaths was plotted against the prevalence rates of the four Internet risks and was analysed using quasi-Poisson regression [26]. The reason for using quasi-Poisson rather than conventional Poisson regression is to adjust for the spurious standard error due to the problem of dispersion. Negative binomial regression, which is also a common choice of over-dispersion adjustment, was not used because that technique gives higher weights to observations with a lower mean (that is to say, the countries with lower incidence of unnatural deaths), which may not provide a good estimate of the overall incidence of unnatural deaths for the current study [26].

The dependent variable was the raw count of unnatural deaths while the independent variable was the four Internet risks. The population difference among counties was adjusted by the introduction of an offset term of taking the logarithm of the age 10-14 population in the equation. Separate quasi-Poisson regression models were constructed for each Internet risks.

The Pearson's correlations between the prevalence rates of depressive symptoms in 2006 and 2012 and the prevalence of exclusively online bullying were calculated. A quasi-Poisson regression model was also constructed such that unnatural cause of death was treated as a dependent variable, and the years 2006 and 2012 prevalence rates of depressive symptoms were treated as independent variables respectively. Statistical test of significance was set at the 5% level. We further deploy Cook's distance to quantify the effect of each data point on the model outcome [27]. An outlier is determined to be influential if its Cook's distance is substantially larger than the rest [28].

Results

The ecological associations between the incidence of unnatural child deaths and the four Internet risks are presented in Figure 1. The quasi-Poisson regression models are shown in Table 1. It confirmed that both the prevalence rates of online and traditional bullying and exclusively online bullying were both significantly associated with the mortality incidence of unnatural deaths of children in the twenty-four European country samples. The model also indicates that for a one percent increase in the prevalence of exclusively online bullying across a country, the risk ratio (relative risk) of unnatural deaths was expected to be e 0.244 = 1.28 (95% CI: 1.02 to 1.57). For example, an increase in prevalence of exclusively online bullying from 5% to 6% between two countries represents an expected increase of annual incidence of unnatural child deaths from 6.48 to 8.28 per 100,000 people at risk (8.28/6.48 = 1.28).

However, no significant correlations were found with the exposure to online information regarding self-harm or suicide and Internet addiction.

While controlling for the national prevalence rate of psychiatric problems, the prevalence of exclusively online bullying was not correlated with the national prevalence rate of depressive symptoms in 2006 (r = 0.213, p = 0.38) nor with the rate in 2012 (r = 0.085, p = 0.71). The quasi-Poisson regression analysis indicated that the prevalence rate of depressive symptoms in 2006 was not associated with the rate of unnatural child deaths (regression coefficient: −0.328, p = 0.78) and neither was the CES-D score in 2012 (regression coefficient: −1.553, p = 0.19). Since all spuriousness tests yielded nonsignificance, the spuriousness of the national prevalence rate of psychiatric problems is not substantiated.

Cook's distance analysis found that the two most influential data points were Romania (Cook's distance = 1.140) and the United Kingdom (Cook's distance = 0.995), both of which had median high prevalence rates of exclusively cyber-bullying among all countries. The two countries with highest prevalence rates were Denmark (Cook's distance = 0.041) and Estonia (Cook's distance = 0.015). Since both values were considerably lower than those of Romania and the United Kingdom, we therefore concluded that the outlier data points had no substantial impact on the overall results.

Discussion

To the authors' knowledge, this paper reports the first study to explore the ecological relationship between the prevalence of Internet risks and the unnatural death of children in a country-level analysis. Even though the statistical power is limited, a statistically significant positive correlation between the prevalence of exclusively online bullying and unnatural-death mortality among children aged 10-14 was detected. This correlation is independent from the offline bullying because the correlation retained significance when those respondents who experienced offline bullying were excluded. This echoes a previous finding indicating that online bullying is a form of behaviour distinct from offline bullying [29], although another study suggests otherwise [30]. Moreover, no evidence is established to support a contention that a country's prevalence of depressive symptoms confounds or mediates the observed ecological association. Even while low statistical power could be a reason for the insignificance, that lack of ecological correlation between variables cannot disprove the role of mental health conditions at the individual level.

Moreover, children's exposure to suicide content and Internet addiction are found to have a statistically nonsignificant ecological correlation with unnatural child death rate. This may be a result of insufficient statistical power to detect small effect size or it may be because such correlations do not actually exist. Future research is needed to reexamine the question in a study design with sufficient power. Figure 1 Ecological associations between the incidence of unnatural child deaths (in log scale) and the four Internet risks. Exploratory note: Each country's incidence of unnatural deaths (y-axis in log scale) was plotted against its prevalence rates of the Internet risks (x-axis) in four separate panels respectively. Linear regression lines were fitted for easy inspection. Each data point represents a sampled country (See abbreviations). Table 1 Regression analysis of the incidence of unnatural deaths among 10-14-year-olds in twenty-four European countries Previous work has suggested that experiencing online risk can result in negative emotions or self-harm [9]. However, general etiological theory behind the linkage remains unclear. It is believed to be multifactorial but the interplays between factors are subject to further investigation. There are a number of plausible explanations. One possible mechanism is that an individual's mental health and well-being can be a mediator between Internet risk and unnatural death risk. Previous studies find adolescents involved in bullying are likely to develop mental health problems [31] associated with increasing risk of natural and unnatural causes of death (including accidental deaths) [32]. Internet risk, say cyberbullying, can escalate the risk of intentional injuries (deliberate self-harm or physical assault) or impaired mental health leading to increased risk behaviours, self-harm, impaired physical health, and consequently a higher likelihood of accidental and unnatural death. Another potential pathway is the linkage between poor mental health and well-being and exposure by some individuals to higher Internet risk, thus elevating the risk of unnatural causes of death as indicated in the previous study [32]. But it seems reasonable that these two mechanisms are not mutually exclusive. Each factor can reinforce the other and contribute to a lethal outcome. Moreover, there may be a reverse causation. An increase in suicide or unnatural death may arouse public interest and lead to increased attention and exposure to online content about suicide or cyberbullying.

Also, the observed association may be spurious due to unexamined ecological confounders. Examples could include health condition indicators such as prevalence rates of mental health disorders other than depression, alcoholism, or substance abuse, as well as country conditions such as household income, unemployment, social disparity, or economic development.

Caution should be taken when interpreting the findings because of some study limitations. As this study follows an ecological design, the results reflect only country-level correlations. We should avoid any claim regarding a correlation at the individual level [33]. Future research is warranted to assess the question using individual data, for example register-based or prospective cohort studies. Furthermore, secondary data analysis is used and therefore the data are not primarily collected for our study purpose and the data quality is out of our control. Another major limitation is the small sample size. Lack of statistical variability among countries may limit the study generalisation. The use of the CES-D is a proxy measure, and the prevalence rate of depressive symptoms may not entirely reflect a country's mental health problems. Lastly, there were variations in the year of data collection among sampled countries.

Given these limitations and the exploratory nature of the study, some clinical implications are still worth noting.

This study can generate useful hypotheses or research questions for future study, particularly for retesting the same hypothesis in non-European settings. Second, intervention and educational programs should be seriously considered as precautionary measures to minimise the chance of children's experience of cyberbullying. Such proposed measures should be subject to rigorous effectiveness evaluation. Preliminary findings support restrictive parental control as an efficacious approach to reducing the likelihood of experiencing online risk [34]. Media literacy programs are suggested to mitigate the negative impact of Internet risk exposure on youth, but their effectiveness remains uncertain [35]. If future research consistently finds that some forms of Internet risk, for instance cyberbullying, can have an adverse universal impact on a country's public health, policy making efforts and legislation to protect children's online safety would be justified. Resources should also be allocated to fund and support intervention research and relevant studies focusing on policy and legal frameworks.

Conclusion

This study reveals a positive ecological association between the rates of exclusively online bullying and unnaturaldeath mortality of the age 10-14 children among 24 European countries. It is also evident that this correlation is independent of the countries' prevalence of depressive symptoms and traditional bullying. We therefore call for further research on this problem using individual study design and, for precautionary reasons, intervention programs for risk minimisation.

coefficient for intercept (SE) Regression coefficient for exposure to risk (SE) Self-harm/suicide −10.496 (0.284)** 0.058 (0.041) Online bullying −10.747 (0.232)** 0.116 (0.036)* Exclusively online bullying −10.623 (0.249)** 0.244 (0.109)* Internet addiction −10.196 (0.488)** 0.024 (0.225) **Statistically significant, p < 0.001. *Statistically significant, p < 0.05.

Abbreviations AT: Austria; BE: Belgium; BG: Bulgaria; CY: Cyprus; CZ: Czech Republic; DE: Germany; DK: Denmark; EE: Estonia; GR: Greece; ES: Spain; FI: Finland; FR: France; HU: Hungary; IE: Ireland; IT: Italy; LT: Lithuania; NL: Netherlands; NO: Norway; PL: Poland; PT: Portugal; RO: Romania; SE: Sweden; SI: Slovenia; TR: Turkey; UK: The United Kingdom.
http://www.biomedcentral.com/1471-2431/14/195
Acknowledgments CH Chan's Postgraduate Scholarship is partially supported by the HKU-SPACE Research Fund.Competing interestsThe authors declare that they have no competing interests.Authors' contributions KWF, CHC and PIP were involved in the study design and the analysis and interpretation of data. KWF and CHC were involved in drafting the article. All were involved in the final approval of the version to be published.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Fu, BMC Pediatrics. 14Exploring the relationship between cyberbullying and unnatural child death: an ecological study of twenty-four European countries. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Fu et al.: Exploring the relationship between cyberbullying and unnatural child death: an ecological study of twenty-four European countries. BMC Pediatrics 2014 14:195. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform
2020

Marco Sozzi
Dept. LEAF
University of Padova
Viale dell'Università 16LegnaroPDItaly

Ahmed Kayad
Dept. LEAF
University of Padova
Viale dell'Università 16LegnaroPDItaly

Francesco Marinello
Dept. LEAF
University of Padova
Viale dell'Università 16LegnaroPDItaly

NEOS srl
University of Padova
Piazzetta Modin, 12PadovaPDItaly

James A Taylor
ITAP
IRSTEA
Montpellier SupAgro

University of Montpellier
Bât. 21, 2 Pl. Pierre VialaMontpellierFrance

Bruno Tisseyre
ITAP
IRSTEA
Montpellier SupAgro

University of Montpellier
Bât. 21, 2 Pl. Pierre VialaMontpellierFrance

Marco Sozzi
Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform
2202010.20870/oeno-one.2020.54.2.2557Received: 18 August 2019 y Accepted: 18 March 2020 y Published: 2 April 2020189
a b s t r a c tAim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index. Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated. Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R 2 = 0.87) and sub-field scale (R 2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index. Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics. k e y w o r d s precision viticulture, remote sensing, spatial correlation, vineyard segmentation

INTRODUCTION

Precision viticulture (PV) has been suggested as an effective approach to reach the high-quality standards required for wine production (Bramley and Hamilton, 2007). In PV, sensors are used to detect inter-field and sub-field variability in order to allow the application of site-specific management for the most important field operations (i.e. fertilising, trimming or harvesting) (Llorens et al., 2010). Although high-resolution and proximal sensors may be used in PV, lower-resolution remote-sensing applications, such as satellite imagery, have proved to be challenging due to peculiarities in vineyards such as vegetation within inter-rows or diversity of training systems. However, remote sensors allow the collection of a significant amount of data in a short time, and performances are rapidly improving . The most common remote-sensing platforms in viticulture are unmanned aerial vehicles (UAV), airborne sensors and satellites (Hall et al., 2002;. UAV and airborne sensors provide high-resolution imagery (spatial, spectral and radiometric) that can be implemented in order to extract various types of vineyard information (Pichon et al., 2019), however, these are expensive, limited in the area of acquisition possible, and require specialised postprocessing to achieve good final imagery (Candiago et al., 2015). Conversely, satellites are more time effective and cheaper for large areas, but they are less adaptive to the growers' needs in terms of revisit time and spatial resolution (Sozzi et al., 2019). High-resolution satellites (below 1 m of spatial resolution) were shown to be potentially useful in assessing leaf area of vines (Johnson et al., 2003). In the last few years, the availability of free-of-charge data from satellites such as Sentinel-2 (European Space Agency) has caused increased interest in its potential use in viticulture. Sentinel 2 is a constellation of two satellites with polar orbits. Sentinel 2A was launched in 2015 and Sentinel 2B was launched in 2017. Since 2018 Sentinel 2 constellation has provided images every 5 days. Both satellites carry a multispectral imaging sensor (MSI) able to acquire images from 433 nm up to 2280 nm, in 13 bands. Red (665 nm) and near-infrared (842 nm) are of particular interest for agriculture application as they make it possible to retrieve several vegetation indices at 10 m of spatial resolution.

Recent studies have demonstrated the potential of Sentinel-2 images in viticulture at a territorial scale to extract agronomic information and to quantify the impact of drought (Devaux et al., 2019;Cogato et al., 2019).

In this study, performances and limitations of Sentinel-2 imagery in vineyards without inter-row grass were assessed, based on the normalised difference vegetation index (NDVI) (Rouse et al., 1973). The ability of satellite NDVI images to characterise variability at field and sub-field scale were estimated using the Gini coefficient (Gini, 1921). To achieve these objectives, Sentinel-2 NDVI was compared to UAV extracted data as a reference layer.

MATERIALS AND METHODS

Study area

For the study, 30 non-irrigated vineyard blocks in the Minervois Appellation d'Origine Contrôlée in the Languedoc-Roussillon region in the south of FIGURE 1. Block size distribution: in the selected study area, 20 blocks were less than 1.5 hectares and 10 blocks were between 1.5 and 5 hectares.

France (43°18'25'' N,2°37'21'' E,WGS84) were selected. The selected blocks were trained using vertical shoot positioning (VSP), with an inter-row space ranging from 2.0 to 2.5 m without any vegetation cover. The planting dates ranged from 2003 to 2011. The mean block area was 1.4 ha, with a minimum and maximum area of 0.22 and 4.9 ha, respectively ( Figure 1). The features of the correspond to the ordinary vineyard conditions in the Languedoc-Roussillon region.

Data acquisition and processing

The NDVI (Rouse et al., 1973) was selected because it is one of the most common vegetative indices used in agriculture (Psomiadis et al., 2017). Data acquisition was performed on August 2016. Blocks borders were delineated using high-resolution images in Google Earth Pro (Google Inc., Mountain View, CA, USA). The UAV images were acquired on 22 August 2016 by means of a multispectral camera MicaSense RedEdge-MX. UAV-acquired data were converted to reflectance based on the measurement in the field of a calibrated reflectance target. All collected images were elaborated through a standard photogrammetry process, then orthorectified and tiled. Orthorectification, mosaicing and resampling were performed using Pix4D software (Pix4D S.A., Prilly, Switzerland) by the UAV operator (DELAIR, Toulouse, France). As a result of this process, a multispectral image, with a spatial resolution of 0.08 m, was obtained for all blocks.

The Sentinel-2 imagery was downloaded from the official Copernicus Open Access Hub (www. scihub.copernicus.eu). A 1C product acquired on 26 August 2016 was selected and downloaded due to the lack of 2A products (not provided before May 2017). The atmospheric correction was carried out using SNAP software (ESA, European Union).

From the UAV and Sentinel-2 imagery there were four defined datasets (layers): three were extracted from the UAV imagery and one from Sentinel-2 imagery. The Sentinel-2 imagery (10 m pixel) was chosen as reference spatial resolution for the upscaling of other layers. The UAV extracted layers were considered as a reference of vineyard conditions at field level.

For the first layer extracted from UAV, the images were clipped to the polygon of each block and then NDVI was calculated for all pixels within the block using the formula proposed by Rouse et al. (1973). This layer was then upscaled to the Sentinel-2 spatial resolution using the mean value of the pixel located in the same area (10 m). Such upscaling methodology is commonly implemented for this type of analysis Qin et al., 2015). The result of this upscaling process corresponds to UAV NDVI mixed 10 m (point 1 in Figure 2).

The second layer consisted of the selection of vineonly pixels in UAV imagery, achieved by removing inter-row and potentially mixed pixels. This process was carried out using a supervised classification process. Taking advantage of the classification wizard in ArcGIS Pro 2.4 (ESRI, Redlands, CA, USA), a multiclass support vector machine (SVM) algorithm was trained using 850 polygons FIGURE 2. Processing flow applied to datasets. NDVI was extracted from UAV multispectral image (UAV NDVI mixed), then was segmented to obtain pure vine index (UAV NDVI pure) and vine area. These three datasets were upscaled to the same resolution on Sentinel-2 image (10×10 m). NDVI Sentinel-2 were calculated from multispectral Sentinel-2 image manually classified into four classes: high vigour vine, low vigour vine, soil, and shadow. This learning dataset covered an area of 718 m 2 (0.1 % of the total area). The segmentation algorithm was trained considering spectral and shape features of grouped UAV NDVI mixed pixels. The accuracy assessment of the classification process was performed using Cohen's kappa, which is based on a confusion matrix of true positive/negative and false positive/ negative. The final Cohen's kappa of the SVM classification algorithm was 0.86. Pixels arising from high vigour and low vigour vines were merged and extracted as a shapefile. This pure vine mask was used to clip the original UAV NDVI image. The resulting raster was filled with zero NDVI value in the inter-row (non-vine) space, in order to simulate a no effect of soil in terms of NDVI. Then, the raster was upscaled using the same methodology described for the UAV NDVI mixed layer. The result of this workflow corresponds to UAV NDVI pure 10 m pixels (point 2 in Figure 2) and it only considers the vine response.

The pure vine mask was also used to extract the projected vine area per pixel. This latter parameter is abbreviated in the following paragraphs as Vine Area, although it represents the planar vine area and not the vine area commonly defined in viticulture (total area of the canopy). The pixel grid of the Sentinel-2 product was intersected with the pure vine mask to obtain the value in square metres of vine over a 10 m pixel. This layer corresponds to vine area 10 m (point 3 in Figure 2).

The Sentinel-2 NDVI (S2 NDVI) was calculated with band 4 (Red) and band 8 (near-infrared), obtaining an NDVI image with 10 m pixel resolution. The last step of satellite image processing was to clip the NDVI according to the border of each block, done by means of ArcGIS Pro 2.4 software. The result of these processes corresponds to S2 NDVI 10 m (point 4 in Figure 2).

Partially different spectral and optical properties characterise the Sentinel-2 and MicaSense-MX sensors. However, when accounting for the major sensitivity of silicon sensors to the central wavelength, differences in bandwidths were considered to be negligible in the calculation of NDVI and for other reported analyses.

Statistical analysis

Data were analysed using four different approaches. First, the border effect on S2 NDVI was evaluated in comparison to UAV NDVI pure. Even if some studies have already applied masks for border effects, a statistical justification is still missing (Devaux et al., 2019). In the second and third approaches, each block was analysed singularly (sub-field scale) and in relation to all other blocks (inter-field scale). Pearson's correlation coefficient was used to compare the similarity between different information sources. The Dutilleul correction was applied to account for spatial autocorrelation of data (Dutilleul et al., 1993) in sub-field scale analysis. Spatial autocorrelation may violate the assumption of independence in the correlation analysis, leading to a biased estimation of variances and correlation coefficient (Dutilleul et al., 1993). Although the Dutilleul correction is commonly applied in ecology, it is not widespread in agricultural studies, which often overestimate the significance of the Pearson's coefficient in spatial datasets (Taylor and Bates, 2013). The analysis at sub-field level was performed using PASSaGE v2 software (Pattern Analysis, Spatial Statistics and Geographic Exegesis; Tempe, AR, USA) to calculate the modified significance test as proposed by Dutilleul (Rosenberg and Anderson, 2011). The field-scale comparison was performed without considering spatial autocorrelation, as each block can be considered an independent observation. In order to compare data acquired from different sensors, correlations between all four layers (S2 NDVI, UAV NDVI mixed, UAV NDVI pure, and Vine Area) were also analysed, considering effects of borders and block dimensions.

Additionally, the variability of each layer was estimated using the Gini coefficient (Gini, 1921) to highlight the magnitude of variation of each block in each layer. The use of the Gini coefficient in agriculture is not common, even though it is useful in describing the inequality in a distribution (Sadras and Bongiovanni, 2004;Woodward et al., 2007). To calculate G, all observations (S2 NDVI, UAV NDVI mixed, UAV NDVI pure, and Vine Area) were ranked for each block from smallest to largest, then the cumulative fraction of each variable was plotted against the cumulative fraction of the population. In case of perfect equality (low variability) the cumulative fraction of the variable corresponds to the cumulative fraction of population. The Gini coefficient (G) was calculated according to the following formula:

RESULTS

Border effect and block dimension evaluation

The first analysis compared the sub-field correlation between S2 NDVI and UAV NDVI pure, with this latter layer considered as a reference for the block variability. The correlations were calculated using 10×10 m pixel information and considering the whole block and the block after removing border pixels (within 10 m of the block boundary) on the S2 NDVI. This test was carried out to evaluate the influence of border pixels, which might be mixed (vineyard and headland).

The r values computed with the whole blocks (including border pixels) had a wide range of variation, with 93 % of r values (28/30 blocks) ranging from 0.52 up to 0.93, and two extreme values of -0.32 and 0.45. When the border pixels were removed, 93 % of r values (28/30 blocks) ranged from 0.76 to 0.97 with two extreme values of 0.07 and 0.56. The border pixel removing process influenced the correlation coefficient, leading to an increase of the mean r (from 0.772 to 0.837) but conversely decreased (from 29 to 27) the number of blocks with a significant correlation (Table 1). Figure 3 shows how the significance of the correlation between S2 NDVI (without border pixels) and NDVI pure was affected by block area. According to Figure 3, non-significant correlations occurred in smaller blocks (0.193 ha), whereas the average area of the blocks where it was significant was larger (1.03 ha).

The difference between the average block area of both groups was significant (t-test, p < 0.001).

FIGURE 3.

Effect of block area on the significance of correlation (with the Dutilleul correction) between S2 NDVI (after border pixel removal) and the UAV NDVI derived from pure vine-only pixels. The embedded table indicates the mean and variance associated with the two groupings.

Sub-field scale correlation

In agreement with results reported in the previous paragraph, the following analysis focuses only on data (layers) where border pixels have been removed.

The sub-field correlation coefficients were calculated between S2 NDVI and the other three datasets (UAV NDVI mixed, UAV NDVI pure, and Vine Area), again adjusting the significance test using the Dutilleul correction. The comparison of the Pearson's coefficient distribution is reported in Table 2. At the sub-field scale, the average correlation between S2 NDVI and UAV NDVI pure was 0.837, for UAV NDVI mixed it was 0.834, and for Vine Area it was 0.798. A different ranking was found for the standard deviation of Pearson's coefficient, which decreased from UAV NDVI pure to UAV NDVI mixed. Table 2 also reports the average correlation coefficients between S2 NDVI and all the other three datasets when blocks with only significant correlation were considered. In this case, an overall increase of correlation and decrease of standard deviation was found. Moreover, the highest correlation was reached by the couple S2 NDVI -UAV NDVI mixed, which is characterised by the same mixed pixel composition.

Field-scale correlation

The following analysis was carried out considering mean block values, rather than the pixel-wise comparisons implemented in the case of Results 1 and 2. The analysis were focused on blocks where significant correlation (p < 0.05) at sub-field scale was found. In this case, measurements were considered to be independent (with no spatial autocorrelation), and the significance test was done without the Dutilleul correction. Figure 4 shows three linear regression models fitting the three UAV-derived layers to the S2 NDVI layer. The R² values of the linear regressions between S2 NDVI and UAV NDVI mixed, UAV NDVI pure and Vine Area were 0.81, 0.87, and 0.79, respectively. R² was significant in all cases (p < 0.01), which is evidence of a relevant link between S2 NDVI and all derived UAV indices. Considering slope and intercepts, UAV NDVI mixed and Vine Area are proportionally correlated with S2 NDVI (1:1 regression), while UAV NDVI pure exhibits lower variance than S2 NDVI. Figure 5 shows the boxplot graph of Gini coefficient (G) distribution, which explains the variability of the four layers. A similar G distribution was found between S2 NDVI and UAV NDVI mixed. Such behaviour was caused by the similar mixed pixel composition of the two layers. In the same way, a similar distribution was found between UAV NDVI pure and Vine Area. In this case, the similarity was a result of the two layers only including information related to the vine response (and masking the inter-row response). Figure 5 reports also some example values of NDVI and G, with respective maps. In the case of Field 1, a low value of NDVI was combined with a high G while, in Field 2, a high NDVI was paired with a low G. These examples suggest that in case of high average NDVI, this might be not equally distributed over the block.

Gini coefficient interpretation

DISCUSSION

The comparison between NDVI from Sentinel-2 and pure vine NDVI extracted from UAV images showed an increasing correlation if the border pixels were removed from the satellite's images. According to this result, it is advisable to remove  At sub-field scale, S2 NDVI is more correlated with UAV NDVI mixed than Vine Area than UAV NDVI pure. border pixels of Sentinel-2 imagery for viticulture applications. Edge effects are introducing uncertainty into the spectral response and the information at the borders is less indicative of actual vine properties. However, the process for removing border pixels is problematic in smaller blocks, because such borders constitute a large percentage of the whole block pixels. For blocks < 0.5 ha, removal of border pixels can lead to a decrease in the significance of correlation between satellite and UAV layers. According to these considerations, Sentinel-2 images with border pixels removed can be used for blocks larger than 0.5 ha, whereas for smaller blocks (<0.5 ha), the satellite imagery is less reliable. For these blocks higher resolution sensing is required, such as UAV-acquired images. This characteristic is an important limitation for the use of Sentinel-2 images for viticulture regions with small vineyard blocks.

When Sentinel-2 images are acquired in larger blocks (>0.5 ha), they can be used to compare different vineyard blocks, considering the mean block values (inter-field comparison). In this situation, Sentinel-2 images can therefore constitute a relevant decision support for advisory services to manage vigour or vegetative expression or for any other application requiring the characterisation of the vegetative expression levels of blocks at a denomination or regional scale. However, endusers still need to be conscious that the Sentinel-2 mixed pixels integrate information about the spectral properties of vines, the projected vine area, and inter-row space and training systems, which may affect comparisons and decisions.

However, for blocks larger than 0.5 ha, Sentinel-2 images may well be used to characterise the withinfield variability. Indeed, this study shows that they can constitute an interesting decision-making support for the following: i) identifying blocks with high spatially organised variability ; ii) prioritising blocks on which spatial variability requires the use of differentiated management strategies at the within-field level; and iii) possibly defining within-field management zones or broad trends in vine properties. This is in contrast with other reported studies of satellite imagery in viticulture (Khaliq et al., 2019). However, it is necessary to consider that the 10 m mixed pixels do smooth the real magnitude of variation of vine properties (with UAV NDVI of pure vines as a reference) and cannot be used for any vine-specific application. It is recommended that variability revealed by Sentinel-2 and other medium-resolution sensors should not be compared without considering this latter point. The results of this study in terms of R 2 between S2 NDVI and UAV NDVI mixed are comparable to other studies. Conversely, the R 2 between S2 NDVI and UAV NDVI pure is higher than seen in previous work (Di Gennaro et al., 2019). This latter condition may be ascribable by the simulation of no soil effects on NDVI performed in this study.

All the analyses performed in this study were applied to blocks without cover crops in the inter-row space. The presence of an inter-row crop would introduce additional noise to spectral data, and misleading considerations could be drawn. This study cannot be used to validate the use of FIGURE 5. Range of variation, expressed using the Gini coefficient in boxplot form, in the four layers across 30 vineyard blocks. Three example blocks are shown to visually illustrate the variability in the S2 NDVI response across blocks. For each block the field-specific mean S2 NDVI and G value are also provided.

Sentinel-2 imagery (or any medium-resolution sensor with mixed pixels) in vineyard blocks with inter-row growth. Indeed, the spectral information on inter-row crops cannot be divided by the vine signal, leading to an erroneous variability estimation: in this case, high-resolution images should be preferred as they allow extraction of vine pixels (Khaliq et al., 2019). However it should be noticed that, at present, the majority of vineyards are not irrigated; due to the summer water deficit, the inter-row grass is dry at the beginning of the summer, thus at an NDVI state that does not differ too much from the zero NDVI condition simulated for the present work. Therefore, at veraison, the effect of inter-row grass may be considered negligible (Kazmierski et al., 2011).

CONCLUSION

Vegetation index patterns in vineyard blocks with no inter-row cropping were similar for Sentinel-2 imagery (medium resolution) and high-resolution UAV imagery upscaled to a medium-resolution. There were effects on the canopy vigour pattern associated with pixels along the border of blocks, which is likely due to variable mixed pixel effects at the vineyard border. Removing border pixels is recommended when analysing Sentinel-2 imagery of vineyards, but this is only feasible in blocks >0.5 ha. For blocks <0.5 ha, Sentinel-2 imagery applications appear limited. For broad canopy and vine management decisions within and between vineyard blocks, Sentinel-2 imagery appears just as useful as high-resolution UAV/ aerial imagery and should provide cost and access benefits (Sozzi et al., 2019). Sentinel-2 imagery cannot currently provide vine-specific information and high-resolution imagery is required for such applications. These results are not transferable to vineyard blocks with inter-row growth; indeed, the experimentation was carried out in blocks without inter-row grass coverage.

FIGURE 4 .
4Regression models between S2 NDVI and UAV-derived datasets based on the mean value of blocks.

TABLE 1 .
1Distribution of correlation coefficients and their significance between S2 NDVI and the upscaled (10×10 m) UAV NDVI derived from pure vine pixels only when the border pixels are kept (Whole) and when these pixels are removed (Border removed). Significance was assessed with Dutilleul correction for spatial autocorrelation.Whole Border removed

Mean
0.772
0.837

Dev.St
0.147
0.164

Count (n. field)
30
30

Significant at p < 0.05
29
27

Not significant
1
3

Only significant fields p < 0.05

Mean (only significant)
0.787
0.865

Dev.st (only significant)
0.123
0.083

TABLE 2 .
2Descriptive statistics of correlation for all block considered calculated using Dutilleul correction for significance test.
© 2020 International Viticulture and Enology Society -IVES
OENO One 2020, 2, 189-197
Marco Sozzi et al.
Acknowledgements:The present work was founded by Fondazione Ing. Aldo Gini (Padova,  Italy). The authors would like to acknowledge TerraNIS SAS (Ramonville-St-Agne, Occitanie, France) and Sylvie Duthoit for UAV images supply. UAV data acquisition campaign was partly funded within the regional OENOMIP project which was co-funded by the European Union.
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