PEOPLEEkaterina Chuprikova

Ekaterina Chuprikova

Senior Researcher
Center for Sensing Solutions

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About

Dr.-Ing. Ekaterina Chuprikova is a Senior Researcher at Eurac Research (Bolzano, Italy), leading the Visual Analytics topic. She is a former Marie Skłodowska-Curie postdoctoral researcher (MSCA IF) and holds a PhD focused on cartography and geospatial visual analytics, with a strong interest in turning complex spatial and spatio-temporal data into clear, decision-ready evidence.

Her work advances data visualization and human-in-the-loop visual analytics by combining interactive dashboards with semantic data integration (e.g., ontologies/knowledge models and FAIR-oriented data practices).

Interests:

Cartography, data visualization & visual analytics
Precision mapping in agriculture
Geospatial data analysis and visualization
Ontology-based data integration
Open-source mapping and services

LinkedIn: https://www.linkedin.com/in/ekaterinachuprikova/

Scientific Publications

Conference proceedings article2025

Enhancing Apple Variety Testing with Ontology-Enriched Visual Analytics: A Decision-Support Framework

Chuprikova E, Guerra W, Stocker R, Mejia-Aguilar A, Monsorno R

Roberto Monsorno, Ekaterina Chuprikova

In this work, we propose an ontology-driven visual analytics approach to support apple variety testing by integrating heterogeneous data sources, including climate records and both qualitative and quantitative agronomic observations. Our methodology includes the development of the Apple Trait Ontology, which standardizes trait definitions and enhances semantic interoperability during data integration, and the implementation of an interactive visual analytics system. This system offers an in-depth overview of apple variety performance over the years by examining key attributes-such as red over color, size, firmness, acidity, starch content, and sugar levels-along with other qualitative and quantitative characteristics. By leveraging ontology-enriched data structuring, the system enables expert-driven interpretation of variety performance, providing a comprehensive decision-support tool for agricultural domain experts. The apple variety-testing visual analytics system integrates spatiotemporal analytics, multimodal visual representations, and interactive filtering, allowing users to explore trait performance trends, climate resilience, and overall suitability under different environmental conditions. The findings demonstrate that combining semantic models with visual analytics enhances the accessibility and usability of complex agricultural data, ultimately improving breeding strategies and decision-making in apple variety selection. This research contributes to the broader field of agricultural informatics by showcasing the potential of knowledge-based systems to support precision farming.

DOI

DOI: 10.2312/envirvis.20251148

Conference proceedings article2024

From Leaves to Forests: How to Map Stress in Plants due to Climate Change

Chew YZ, Chuprikova E, Mejia-Aguilar A, Obojes N, Kumke H, Meng L

Nikolaus Obojes, Ekaterina Chuprikova

Progetto: Servizio di sviluppo di gemelli digitali per la previsione di scenari di gestione ambientale attraverso la ricostruzione dinamica dell’ambiente alpino

This study aims to develop a map-based storytelling approach to illustrate the changes in forest ecosystems over the years as a response to tree stress induced by increasing biotic and abiotic conditions linked to climate change. Climate change is driven by positive feedback loops that amplify warming trends, complicating efforts in mitigating its impacts (Ripple et al., 2023). Forests face significant challenges adapting to changing environments due to their slow regeneration rates and long-life spans (Lindner et al., 2010). Over the past few decades, the Mediterranean region has seen a faster-than-average temperature increase, accompanied by variable precipitation rates (Castly et al., 2015), leading to increased drought frequency and intensity. These changes increase the forests' vulnerability to interconnected disturbances such as pest outbreaks and extreme weather events. The advancements in sensing techniques have allowed transformative solutions to integrate with local traditional forest monitoring methods. This approach holds promising, cost-effective, scalable forest management practices in addressing Sustainable Development Goal #15: Life of Land to ensure future generations can enjoy the benefits of forest ecosystems. This research focuses on assessing how trees on different elevation levels respond to biotic and abiotic stress in the Vinschgau / Val Venosta region in South Tyrol, Italy, based on the monitoring activities of the last 10 years. Different tree properties can be used to monitor stress responses. In traditional monitoring, ground measurements are usually based on the tree stem data such as tree circumference, tree ring growth and sap flow (Jacobs et al., 2021). Proximal sensing, which allows assessment on individual tree levels, provides calculations of vegetation indexes based on leaf properties reflecting water and nutrients stress response. Indexes such as the Photochemical Reflectance Index (PRI) is a measure sensitive to heat dissipation under water stress conditions in leaves via the de-epoxidation process of the xanthophyll cycle (D'Odorico et al., 2021). PRI can signal drought stress before visible signs appear. Integrating both data sources can improve foresters’ ability to monitor better and respond to tree drought stress efficiently. Cartographic methods are vital for visualizing and addressing this unconventional interdisciplinary challenge of assessing tree stress, thereby facilitating improved forest management. Innovative sensing solutions combined with historical ground tree data use changes in tree growth to understand responses to tree stress. This study employs a mapbased storytelling approach that allows foresters to discern the relationship between specific site data and broader local area trends across different temporal scales and vertical elevations, effectively conveying the impacts of climatic effects. Map-based storytelling may effectively translate complex ecological data into engaging visual narratives that allow stakeholders to visualize the temporal and spatial dynamics of forest health and stress factors. We will explore the use of Space Time Cubes, by exploiting the in-situ information based on sensors (sap flow and dendrometers) to create radial maps of the trees. The trees have specific coordinates that can be plotted to create g spatial maps. In addition, we
will combine the spectral information from drones to identify stress signals and link them to the ratio of growing and stress. This approach is particularly beneficial for domain experts and informed audiences, such as environmental scientists, policymakers, and conservation organizations, who can leverage these detailed visualizations to make data-driven decisions and implement targeted interventions. A usability study, based on an ad-hoc questionnaire and demo case, will be conducted with a group of foresters to evaluate their efficiency, effectiveness, and satisfaction from using the story map. This research contributes on dual levels: firstly, by designing visual approaches tailored for site-specific data, enabling the narration of local environmental changes; and secondly, by bridging the gap between scientific understanding and societal awareness at a broader level.

DOI Fonte

DOI: 10.5194/ica-abs-7-24-2024

Conference proceedings article2024

Near Real-Time Mapping to Estimate Snow Cover Fraction Area

Romero Candanedo G, Chuprikova E, Mejia-Aguilar A, Kumke H, Meng L

Ekaterina Chuprikova

Progetto: Servizio di sviluppo di gemelli digitali per la previsione di scenari di gestione ambientale attraverso la ricostruzione dinamica dell’ambiente alpino

The 13th Sustainable Development Goal (SDG) “Climate Action” intends to have an impact on the reduction of greenhouse gases by 2030, as they contribute to warming the atmosphere. According to the Intergovernmental Panel on Climate Change (IPCC), warming beyond 1.5 degrees could have irreversible and irreparable consequences. The increase of temperature has been reducing the number of snow events, resulting in less snow cover, earlier melting periods reflected on poor recorded volumes of snow. Once it melts and incorporates into the water cycle, activities with environmental and agricultural importance are affected. A specific approach to describing the water content in alpine areas in an automated protocol has been described by Stevanato et al. (2019) and Schattan et al. (2020). They proposed using a Cosmic Ray Neutron Sensor (CRNS) calibrated by historical data to provide real-time measurements of snow and soil moisture properties. Moreover, research on complex data integration developed by Premier et al. (2021) proposes hierarchy-based techniques from remote sensing and in situ measurements integration. The goal is to overcome temporal gaps present in situ data from sparsely acquired time series with high and low-resolution snow cover maps. Aside regarding visualization research Franke (2023), points out that data visualization is one of the core components in the current state of the internet communication, and thus it should consider incorporating the emerging technological advancements to enhance human understanding on the surrounding changes. Here we present a visualization approach for near real-time (NRT) mapping of snow cover fraction (SCF), achieved by incorporating data from sensor stations using the Internet of things (IoT) philosophy and aerial imagery. By improving data assimilation and spatial visualization, we aim at supporting domain experts to enhance the comprehensibility of snow dynamics in complex terrains and enable comprehensive Geo-data visualization for non-experts. Furthermore, it will contribute to the 13th SDG's data governance through FAIR data principles (Findable, Accessible, Interoperable, Reusable) and optimizing the Data Life Cycle (DLC) for geoinformation available to society. This work is based on data integration and assimilation following next data pipeline (Fig. 1) 1) Geo-data management: in situ CRNS and weather station snow data, transmitted every 15 and 10 minutes to a centralized ftp repository; UAV-based data, and remote sensing data (Planet Scope) retrieved via API, all stored to Eurac premises. Some of the analyzed parameters were acquired on site (dedicated field campaigns) for calibration of the historical snow dataset and validation. Subsequently, the datasets which meet the quality requirements are prepared and stored on a database to facilitate constant retrieval and integration. Finally, aside from the collaboration with domain experts (final user at this research point) an interactive analytical dashboard is being developed for enhancing the analysis of SCF state in the area. The data organization follows IoT and FAIR principles. 2) Analytical process: It consists in different scripts prepared in Python and R to estimate snow cover fraction, harmonized across both spatial and temporal dimensions. 3) Geo-visualization: An interactive dashboard developed in Power BI, which integrates the acquired sources into a thematic mapping-based visualization platform. It can simultaneously present different temporal and spatial scales and visualize the latest observations from the IoT sensors along side free-cloud historical remote sensing datasets. This will offer a new way to present research, uncovering hidden insights enhancing the analytical and communicational processes of domain experts through geospatial and interactive visualizations.
The research outcome aims to contribute to the ongoing visual analytics and spatial visualization research for innovative research presentations, such as dashboards as they have not been deeply investigated. Additionally, it will enhance domain expertise by incorporating cartographic knowledge, making the analysis and visualization of research data more intuitive. This, in turn, will open a new cartographic research agenda tailored to the specific needs of the scientific community. The presented approach can be duplicated and extended to the different COSMOS CRNS stations to predict snow coverage in the entire Alps region.

DOI Fonte

DOI: 10.5194/ica-abs-7-140-2024

Journal article2023

Identification of spectral ranges that contribute to phytoplasma detection in apple trees – A step towards an on-site method

Barthel D, Cullinan C, Mejia-Aguilar A, Chuprikova E, McLeod BA, Kerschbamer C, Trenti M, Monsorno R,

Roberto Monsorno, Ekaterina Chuprikova

Progetto: Costruire competenze digitali per l'individuazione e il monitoraggio delle malattie nei campi dell'agricoltura di montagna, Un approccio di analisi visiva basato sull'ontologia per Big_Data per infrastruttura di monitoraggio in agricoltura

'Candidatus Phytoplasma mali' is the bacterial agent associated with Apple Proliferation, a disease that causes high economic losses in affected commercial apple growing regions. The identification of the disease is carried out by visual inspection performed by skilled professionals in the orchards. To confirm an infection, costly molecular laboratory methods must be applied. Furthermore, both methods are very time-consuming. Here, we analysed the potential of a non-destructive method using in-field measurements to differentiate infected from non-infected apple trees (Malus domestica) based on spectral signatures of fresh leaves. By using multivariate statistics, we were able to distinguish infected from non-infected trees and identified the wavelengths relevant for the differentiation. Factors affecting the differentiation performance were the sampling date and bacterial colonization behaviour.

DOI Fonte

DOI: 10.1016/j.saa.2023.123246

Conference proceedings article2021

An Ontology-based Visual Analytics for Apple Variety Testing

Chuprikova E, Mejia Aguilar A, Monsorno R

Roberto Monsorno, Ekaterina Chuprikova

Progetto: Un approccio di analisi visiva basato sull'ontologia per Big_Data per infrastruttura di monitoraggio in agricoltura

Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages). The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts.

This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.

Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer "what" and "why" about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.

This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 894215.

DOI

DOI: http://doi.org/10.5194/egusphere-egu21-15804

Journal article2017

Organizing earth observation data inside a spatial data infrastructure

Innerebner M, Costa A, Chuprikova E, Monsorno R, Ventura B

Ekaterina Chuprikova, Armin Costa, Roberto Monsorno, Bartolomeo Ventura

Scientists as well public institutions dealing with geospatial data often work with a large amount of heterogeneous data deriving from different sources. Without a well-defined, organized structure they face problems in finding and reusing existing data, and as consequence this may cause data inconsistency and storage problems. A catalog system based on the metadata of spatial data facilitates the management of large amount of data and offers service to retrieve, discover and exchange geographic data in an quick and easy fashion. Currently, most online catalogs are more focusing on the geographic data and there has been only few interests in catalogizing Earth observation data, in which in addition the acquisition information matters. This article presents an automatic metadata extraction approach that creates from different optical data deriving from various satellite missions of scientific interest (i.e. MODIS, LANDSAT, RapidEye, Suomi-NPP VIIRS, Sentinel-1A, Sentinel-2A) metadata information, based on an extended model of the standard ISO 19115. The XML schema ISO 19139-2 with the support of gridded and imagery information defined in ISO 19115-2 was examined, and based on the requirements of experts working in the research field of Earth observation the schema was extended. The XML schema ISO 19139-2 and its extension has been deployed as a new schema plugin in the spatial catalog Geonetwork Open Source in order to store all relevant metadata information about satellite data and the appropriate acquisition and processing information in an online catalog. A real-world scenario that is productively used in the EURAC research group institute for Applied Remote Sensing illustrates a workflow management for Earth observation data including data processing, metadata extraction, generation and distribution.

DOI Fonte

DOI: 10.1007/s12145-016-0276-0

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Conference proceedings article2024

Near Real-Time Mapping to Estimate Snow Cover Fraction Area

Romero Candanedo G, Chuprikova E, Mejia-Aguilar A, Kumke H, Meng L

Ekaterina Chuprikova

Progetto: Servizio di sviluppo di gemelli digitali per la previsione di scenari di gestione ambientale attraverso la ricostruzione dinamica dell’ambiente alpino

DOI Fonte

DOI: 10.5194/ica-abs-7-140-2024

Ekaterina Chuprikova

About

Scientific Publications

Enhancing Apple Variety Testing with Ontology-Enriched Visual Analytics: A Decision-Support Framework

From Leaves to Forests: How to Map Stress in Plants due to Climate Change

Near Real-Time Mapping to Estimate Snow Cover Fraction Area

Identification of spectral ranges that contribute to phytoplasma detection in apple trees – A step towards an on-site method

An Ontology-based Visual Analytics for Apple Variety Testing

Organizing earth observation data inside a spatial data infrastructure

Science Shots Eurac Research Newsletter

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Scientific Publications

Enhancing Apple Variety Testing with Ontology-Enriched Visual Analytics: A Decision-Support Framework

From Leaves to Forests: How to Map Stress in Plants due to Climate Change

Near Real-Time Mapping to Estimate Snow Cover Fraction Area

Identification of spectral ranges that contribute to phytoplasma detection in apple trees – A step towards an on-site method

An Ontology-based Visual Analytics for Apple Variety Testing

Organizing earth observation data inside a spatial data infrastructure