EnvirVis2021

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633051
Table of Contents
Probabilistic and Uncertainty-based Techniques for Environmental Data Visualization
GPU-Assisted Visual Analysis of Flood Ensemble Interaction
[full paper Image] [meta data Image]
Donald W. Johnson and T. J. Jankun-Kelly
Probabilistic Principal Component Analysis Guided Spatial Partitioning of Multivariate Ocean Biogeochemistry Data
[full paper Image] [meta data Image]
Subhashis Hazarika, Ayan Biswas, Earl Lawrence, and Philip J. Wolfram
A Winding Angle Framework for Tracking and Exploring Eddy Transport in Oceanic Ensemble Simulations
[full paper Image] [meta data Image]
Anke Friederici, Martin Falk, and Ingrid Hotz
Uncertainty-aware Detection and Visualization of Ocean Eddies in Ensemble Flow Fields - A Case Study of the Red Sea
[full paper Image] [meta data Image]
Felix Raith, Gerik Scheuermann, and Christina Gillmann
Interactive Digital and Virtual Visualization Techniques for Environmental Data Visualization
Digital Earth Viewer: a 4D Visualisation Platform for Geoscience Datasets
[full paper Image] [meta data Image]
Valentin Buck, Flemming Stäbler, Everardo González, and Jens Greinert
Spatiotemporal Visualisation of a Deep Sea Sediment Plume Dispersion Experiment
[full paper Image] [meta data Image]
Everardo González, Kaveh Purkiani, Valentin Buck, Flemming Stäbler, and Jens Greinert
Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments
[full paper Image] [meta data Image]
Shubhi Harbola, Steffen Koch, Thomas Ertl, and Volker Coors
A Virtual Geographic Environment for the Exploration of Hydro-Meteorological Extremes
[full paper Image] [meta data Image]
Karsten Rink, Özgür Ozan Sen, Marco Hannemann, Uta Ködel, Erik Nixdorf, Ute Weber, Ulrike Werban, Martin Schrön, Thomas Kalbacher, and Olaf Kolditz
Assessing the Geographical Structure of Species Richness Data with Interactive Graphics
[full paper Image] [meta data Image]
Pauline Morgades, Aidan Slingsby, and Justin Moat

BibTeX (EnvirVis2021)
@inproceedings{
10.2312:envirvis.20211077,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
GPU-Assisted Visual Analysis of Flood Ensemble Interaction}},

author = {
Johnson, Donald W.
 and 
Jankun-Kelly, T. J.
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211077}

}
@inproceedings{
10.2312:envirvis.20211078,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Probabilistic Principal Component Analysis Guided Spatial Partitioning of Multivariate Ocean Biogeochemistry Data}},

author = {
Hazarika, Subhashis
 and 
Biswas, Ayan
 and 
Lawrence, Earl
 and 
Wolfram, Philip J.
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211078}

}
@inproceedings{
10.2312:envirvis.20211080,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Uncertainty-aware Detection and Visualization of Ocean Eddies in Ensemble Flow Fields - A Case Study of the Red Sea}},

author = {
Raith, Felix
 and 
Scheuermann, Gerik
 and 
Gillmann, Christina
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211080}

}
@inproceedings{
10.2312:envirvis.20211082,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Spatiotemporal Visualisation of a Deep Sea Sediment Plume Dispersion Experiment}},

author = {
González, Everardo
 and 
Purkiani, Kaveh
 and 
Buck, Valentin
 and 
Stäbler, Flemming
 and 
Greinert, Jens
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211082}

}
@inproceedings{
10.2312:envirvis.20211081,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Digital Earth Viewer: a 4D Visualisation Platform for Geoscience Datasets}},

author = {
Buck, Valentin
 and 
Stäbler, Flemming
 and 
González, Everardo
 and 
Greinert, Jens
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211081}

}
@inproceedings{
10.2312:envirvis.20211079,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
A Winding Angle Framework for Tracking and Exploring Eddy Transport in Oceanic Ensemble Simulations}},

author = {
Friederici, Anke
 and 
Falk, Martin
 and 
Hotz, Ingrid
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211079}

}
@inproceedings{
10.2312:envirvis.20211085,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Assessing the Geographical Structure of Species Richness Data with Interactive Graphics}},

author = {
Morgades, Pauline
 and 
Slingsby, Aidan
 and 
Moat, Justin
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211085}

}
@inproceedings{
10.2312:envirvis.20211084,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
A Virtual Geographic Environment for the Exploration of Hydro-Meteorological Extremes}},

author = {
Rink, Karsten
 and 
Sen, Özgür Ozan
 and 
Hannemann, Marco
 and 
Ködel, Uta
 and 
Nixdorf, Erik
 and 
Weber, Ute
 and 
Werban, Ulrike
 and 
Schrön, Martin
 and 
Kalbacher, Thomas
 and 
Kolditz, Olaf
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211084}

}
@inproceedings{
10.2312:envirvis.20211083,

booktitle = {
Workshop on Visualisation in Environmental Sciences (EnvirVis)},

editor = {
Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
},
title = {{
Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments}},

author = {
Harbola, Shubhi
 and 
Koch, Steffen
 and 
Ertl, Thomas
 and 
Coors, Volker
},
year = {
2021},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-148-9},

DOI = {
10.2312/envirvis.20211083}

}

Browse

Recent Submissions

Now showing 1 - 10 of 10
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    GPU-Assisted Visual Analysis of Flood Ensemble Interaction
    (The Eurographics Association, 2021) Johnson, Donald W.; Jankun-Kelly, T. J.; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Analysis of overlapping spatial data sets is a challenging problem with tension between clearly identifying individual surfaces and exploring significant overlaps/conflicts. One area where this problem occurs is when dealing multiple flood scenes that occur in an area of interest. In order to allow easier analysis of scenes with multiple overlapping data layers, we introduce a visualization system designed to aid in the analysis of such scenes. It allows the user to both see where different data sets agree, and categorize areas of disagreement based on participating surfaces in each area. The results are stable with regard to render order and GPU acceleration via OpenCL allows interaction with large datasets with preprocessing dynamically. This interactivity is further enhanced by data streaming which allows datasets too large to be loaded directly onto the GPU to be processed. After demonstrating our approach on a diverse set of ensemble datasets, we provide feedback from expert users.
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    EnvirVis 2021: Frontmatter
    (The Eurographics Association, 2021) Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
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    Probabilistic Principal Component Analysis Guided Spatial Partitioning of Multivariate Ocean Biogeochemistry Data
    (The Eurographics Association, 2021) Hazarika, Subhashis; Biswas, Ayan; Lawrence, Earl; Wolfram, Philip J.; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Farm-scale cultivation of macroalgae for the production of renewable biofuel depends on complex ocean hydrodynamics and also on the availability of different essential nutrients. To better understand such conditions that are conducive for the growth of macroalgae, scientists implement large-scale computational models, simulating several physical variables (essential nutrients, and other chemical compounds), relevant to study oceanic biogeochemistry (BGC). Visualizing and analysing the different physical variables and their inter-variable relationships across the spatial domain is crucial to form concrete understanding of the underlying physical phenomenon. To facilitate such multivariate analyses for large-scale simulation data, a popular and effective way is to decompose the spatial domain into smaller local regions based on the variable relationships. However, spatial decomposition of multivariate data is not trivial. In this paper, we propose a novel multivariate spatial data partitioning approach using probabilistic principal component analysis. We also perform detailed study of other prospective multivariate partitioning schemes and compare them with our proposed method. To demonstrate the efficacy of our approach, we studied nutrient relationships across different regions of the ocean using a high-resolution Ocean BCG simulation data set, which comprises of multiple physical variables essential for macroalgae cultivation. We further validate the results of our analyses by getting feedback from domain experts in the field of ocean sciences.
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    Uncertainty-aware Detection and Visualization of Ocean Eddies in Ensemble Flow Fields - A Case Study of the Red Sea
    (The Eurographics Association, 2021) Raith, Felix; Scheuermann, Gerik; Gillmann, Christina; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Eddy detection is a state of the art tool to examine transport behavior in oceans, as they form circular movements that are highly involved in transferring mass in an ocean. To achieve this, ocean simulations are run multiple times, and an eddy detection is performed in the final simulation results. Unfortunately, this process is affected by a variety of uncertainties. In this manuscript, we aim to identify the types of uncertainty inherent in ocean simulations. For each of the identified uncertainties, we provide a quantification approach. Based on the quantified uncertainties, we provide a visualization approach that consists of domain embedded views and an uncertainty space view connected via interaction. We showed the effectiveness of our approach by performed a case study of the Red Sea.
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    Spatiotemporal Visualisation of a Deep Sea Sediment Plume Dispersion Experiment
    (The Eurographics Association, 2021) González, Everardo; Purkiani, Kaveh; Buck, Valentin; Stäbler, Flemming; Greinert, Jens; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Deep sea mining for metals as Ni, Cu, and Co as in manganese nodules (Mn-nodules) is currently further developed e.g. with respect to technological and economical feasibility but always poses the threat that these sensitive ecosystems are destroyed for a long time. To evaluate the impact of Mn-nodule mining activities, the JPI Oceans project Mining Impact II, studies the distribution of a sediment plume created by a mining vehicle. It uses in situ observations of a small-scale experiment and related ocean current and sediment settling numerical models. This is done to validate the model itself, to have a prognostic tool to determine at which location what type of sensor is need to capture the plume dispersion in the best possible way, and, finally, to present the results to none-experts. Through the contextualisation of a wide array of sensors and computer model parameters, we created a visualisation of a small-scale deep sea sediment plume dispersion experiment. Our 4D visualisation environment helps explore the dynamics of the sediment transport and deposition across time and space in an interactive and user-explorable way.
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    Digital Earth Viewer: a 4D Visualisation Platform for Geoscience Datasets
    (The Eurographics Association, 2021) Buck, Valentin; Stäbler, Flemming; González, Everardo; Greinert, Jens; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    A comprehensive study of the Earth System and its different environments requires understanding of multi-dimensional data acquired with a multitude of different sensors or produced by various models. Here we present a component-wise scalable web-based framework for simultaneous visualisation of multiple data sources. It helps contextualise mixed observation and simulation data in time and space.
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    A Winding Angle Framework for Tracking and Exploring Eddy Transport in Oceanic Ensemble Simulations
    (The Eurographics Association, 2021) Friederici, Anke; Falk, Martin; Hotz, Ingrid; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Oceanic eddies, which are highly mass-coherent vortices traveling through the earth's waters, are of special interest for their mixing properties. Therefore, large-scale ensemble simulations are performed to approximate their possible evolution. Analyzing their development and transport behavior requires a stable extraction of both their shape and properties of water masses within. We present a framework for extracting the time series of full 3D eddy geometries based on an winding angle criterion. Our analysis tools enables users to explore the results in-depth by linking extracted volumes to extensive statistics collected across several ensemble members. The methods are showcased on an ensemble simulation of the Red Sea. We show that our extraction produces stable and coherent geometries even for highly irregular eddies in the Red Sea. These capabilities are utilized to evaluate the stability of our method with respect to variations of user-defined parameters. Feedback gathered from domain experts was very positive and indicates that our methods will be considered for newly simulated, even larger data sets.
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    Assessing the Geographical Structure of Species Richness Data with Interactive Graphics
    (The Eurographics Association, 2021) Morgades, Pauline; Slingsby, Aidan; Moat, Justin; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Understanding species richness is an important aspect of biodiversity studies and conservation planning, but varying collection effort often results in insufficient data to have a complete picture of species richness. Species accumulation curves can help assess collection completeness of species richness data, but these are usually considered by discrete area and do not consider the geographical structure of collection. We consider how these can be adapted to assess the geographical structure of species richness over geographical space.We design and implement two interactive visualisation approaches to help assess how species richness data varies over continuous geographical space. We propose these designs, critique them, report on the reactions of four ecologists and provide perspectives on their use for assessing geographical incompleteness in species richness.
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    A Virtual Geographic Environment for the Exploration of Hydro-Meteorological Extremes
    (The Eurographics Association, 2021) Rink, Karsten; Sen, Özgür Ozan; Hannemann, Marco; Ködel, Uta; Nixdorf, Erik; Weber, Ute; Werban, Ulrike; Schrön, Martin; Kalbacher, Thomas; Kolditz, Olaf; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    We propose a Virtual Geographic Environment for the exploration of hydro-meteorological events. Focussing on the catchment of the Müglitz River in south-eastern Germany, a large collection of observation data acquired via a wide range of measurement devices has been integrated in a geographical reference frame for the region. Results of area-wide numerical simulations for both groundwater and soil moisture have been added to the scene and allow for the exploration of the delayed consequences of transient phenomena such as heavy rainfall events and their impact on the catchment scale. Implemented in a framework based on Unity, this study focusses on the concurrent visualisation and synchronised animation of multiple area wide datasets from different environmental compartments. The resulting application allows to explore the region of interest during specific hydrological events for an assessment of the interrelation of processes. As such, it offers the opportunity for knowledge transfer between researchers of different domains as well as for outreach to an interested public.
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    Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments
    (The Eurographics Association, 2021) Harbola, Shubhi; Koch, Steffen; Ertl, Thomas; Coors, Volker; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed patterns analyses. While previous approaches lack predictive air quality options, this interface provides back-and-forth dialogue with the designed multiple Machine Learning (ML) models and comparisons for better visual predictive assessments. These models can be dynamically selected in real-time, and the user could visually compare the results in different time conditions for chosen parameters. Moreover, AQTA provides data selection, display, visualisation of past, present, future (prediction) and correlation structure among air parameters, highlighting the predictive models effectiveness. AQTA has been evaluated using Stuttgart (Germany) city air pollutants, i:e:, Particular Matter (PM) PM10, Nitrogen Oxide (NO), Nitrogen Dioxide (NO2), and Ozone (O3) and meteorological parameters like pressure, temperature, wind and humidity. The initial findings are presented that corroborate the city’'s COVID lockdown (year 2020) conditions and sudden changes in patterns, highlighting the improvements in the pollutants concentrations. AQTA, thus, successfully discovers temporal relationships among complex air quality data, interactively in different time frames, by harnessing the user's knowledge of factors influencing the past, present and future behavior, with the aid of ML models. Further, this study also reveals that the decrease in the concentration of one pollutant does not ensure that the surrounding air quality would improve as other factors are interrelated.