Detection and Visualisations of Ecotones                      Landscape Pattern under Uncertainty                         ...
Definitions   Uncertainty    our imperfect and inexact knowledge of the world   Data    we are unsure of what exactly we...
Is uncertainty visualisation necessary?   Isn’t better to provide geoinformation    with some kind of uncertainty?   Isn...
Spatial variability   just about everything varies over space    (spatial dependence)   therefore, an estimation of unce...
Ecotones       ecotones are significant part of almost every landscape        structures and have a significant effect on...
Ecotone project   The aim of the project was to analyze spatial boundaries    of ecotones and to model dynamics structure...
Uncertainty   uncertainty of ecotones in the landscape arises from    many sources, including complexities inherent in   ...
Sources of uncertainty   Lineage (description of the source material from which the    data were derived and the methods ...
Approach in visualisation                                                             Examples                            ...
Visual variables in uncertainty visualisation Visual variable                                        Description Location ...
Uncertainty visulalisationof different data types and data quality            First InDOG Doctoral Conference, 29th Octobe...
Uncertainty visualisation methods classification                    (Senaratne & Gerharz 2011)           First InDOG Docto...
Usability studies   research on usability studies in uncertainty visualizations    have been performed from 1990   many ...
Area of interest   Trkmanka River basin       left tributary of the Dyje River       located in South-east Moravia    ...
Representation data   combination of disparate data sets, each of which may have    a very different uncertainty structur...
Landuse of Trkmanka river catchment                                                             - photointerpretation    ...
Delineation of ecotones – entropy approach   Land Facet Corridor Tools for ArcGIS                                        ...
Entropy visualisations           First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
Visualisation methods          First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
Results          First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
Results          First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
Uncertainty visualisation of ecotones                                    adjacent method      a) grid                b) bl...
Results   information entropy can be used to visualize    uncertainties in the landscape structures   gives an explanati...
Further methods to delinination and research   Fuzzy – POM demonstrator (Vullings, 2006)   Wobling with positional uncer...
Preliminary Eye-tracking results   we can deduce that the perception of areas with a low    level of uncertainty differs ...
Thank you for your attention                                                                Jan Brus                      ...
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Brus, J: Detection and Visualisations of Ecotones - Landscape Pattern under Uncertainty

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Brus, J: Detection and Visualisations of Ecotones - Landscape Pattern under Uncertainty

  1. 1. Detection and Visualisations of Ecotones Landscape Pattern under Uncertainty Jan BRUSThis presentation is co-financed by theEuropean Social Fund and the statebudget of the Czech Republic
  2. 2. Definitions Uncertainty our imperfect and inexact knowledge of the world Data we are unsure of what exactly we observe or measure in society or nature Rule we are unsure of the conclusions we can draw from even perfect data (how we reason with the observations) First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  3. 3. Is uncertainty visualisation necessary? Isn’t better to provide geoinformation with some kind of uncertainty? Isn‘t maps (geovisualizations) with information about data uncertainty confusing? What‘s the right/good way of uncertainty visualization? What‘s better in a real decision process? First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  4. 4. Spatial variability just about everything varies over space (spatial dependence) therefore, an estimation of uncertainty is important The estimate can be:  descriptive  quantitative First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  5. 5. Ecotones  ecotones are significant part of almost every landscape structures and have a significant effect on the distribution of species  spatial variability of ecotones has resulted in problematic modelling, analysis and visualization of these landscape forms ambiguous boundary in the landscape  forest – ecotone – field exploratory analysis based on remote sensing products, historical maps, field mapping plenty of datasets – different quality – several types of uncertainty First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  6. 6. Ecotone project The aim of the project was to analyze spatial boundaries of ecotones and to model dynamics structure of landscape system by an example of watershed of Trkmanka river in time period of 1764─2006 (app. 230 years). The base model element is landuse category acquired by mapping in scale 1 : 25 000 and by study of historical maps. Individual categories of landuse were analyzed. The project solved spatial organization and landscape dynamics by the study of boundary of landscape elements – ecotones. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  7. 7. Uncertainty uncertainty of ecotones in the landscape arises from many sources, including complexities inherent in ecosystems and their disturbance processes collection, analysis and visualization with geodata is more difficult further decisions are more complicated several sources of uncertainty  accuracy, nature (basis) of a phenomenon, data manipulation etc. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  8. 8. Sources of uncertainty Lineage (description of the source material from which the data were derived and the methods of derivation) Positional accuracy (resolution of the measurement) Attribute accuracy (both measurement accuracy and class assignment accuracy) Logical consistency (describing the fidelity of relationships inside data structure) Completness (relationship between the objects represented and the abstract universe) Currency (time currency, time relevance) Credibility (reliability of information source, experiences) Subjectivity (amount of human judgments in the information) Interrelatedness (source independence) (Shi, 2010) First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  9. 9. Approach in visualisation Examples future Eye-Tracking study First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  10. 10. Visual variables in uncertainty visualisation Visual variable Description Location (position) (x,y) position of an element on the visual plane Size dimensions of an element Shape combination of size and orientation Value local amount of black that is perceived Color local hue and saturation Orientation local angle of the elements Texture (grain) local variation in the scale of the elements Focus power of attraction of an element to the eye Realism perceptual similarity of an element to a real-world object Bertin (1983), MacEachren (1992) and McGranaghan (1993) First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  11. 11. Uncertainty visulalisationof different data types and data quality First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  12. 12. Uncertainty visualisation methods classification (Senaratne & Gerharz 2011) First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  13. 13. Usability studies research on usability studies in uncertainty visualizations have been performed from 1990 many tests on several techniques were conducted Evans (1997) assessed Static Color Bivariate Maps Fisher examined the Flickering Animation method (1993) MacEachren considered Toggling (1992) MacEachren et al. assessed Adjacent Maps (1998) and a Color Model (2005) the Texture Overlay method was assessed by Kardos et al. (2003) Sanyal et al. (2009) found that the perception of uncertainty is not uniform First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  14. 14. Area of interest Trkmanka River basin  left tributary of the Dyje River  located in South-east Moravia  the river is of lowland characteristics  it flows through an open countryside  vegetation cover  72 % agricultural area  18 % forests  10 % vegetation-free area First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  15. 15. Representation data combination of disparate data sets, each of which may have a very different uncertainty structure associated with it land use biotype mapping of the Czech Republic which was processed by methodology introduced by NATURA 2000 pedoecological unit (soil-ecological unit, BPEJ in Czech, used for land appraisal) forest topology and more How best to represent the data (uncertainty) so that the results best reflect the overall uncertainty? First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  16. 16. Landuse of Trkmanka river catchment - photointerpretation from historical maps and aerial images - subjectivity of results Woods Arrable land Pastures Orchards Vineyards Buildings Water Transect First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  17. 17. Delineation of ecotones – entropy approach Land Facet Corridor Tools for ArcGIS • can be used for each map layer • combinantion of entropies • showing most uncertain • map algebra First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  18. 18. Entropy visualisations First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  19. 19. Visualisation methods First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  20. 20. Results First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  21. 21. Results First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  22. 22. Uncertainty visualisation of ecotones adjacent method a) grid b) blur c) transparency d) mosaic First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  23. 23. Results information entropy can be used to visualize uncertainties in the landscape structures gives an explanation where uncertainties (transition zones as ecotones) may occur. beyond pure visualization, the measure can be interpreted in a quantitative way we can clearly distinguish areas with high uncertainty from results these areas highly correspond with actual presence of ecotones (transitions zones) in the landscape proved by field survey First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  24. 24. Further methods to delinination and research Fuzzy – POM demonstrator (Vullings, 2006) Wobling with positional uncertainty – Boundary seer etc… Usability testing Eye-tracking Developing representation methods for depicting multiple kinds of uncertainty First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  25. 25. Preliminary Eye-tracking results we can deduce that the perception of areas with a low level of uncertainty differs from the perception of places with a high degree of uncertainty a legend expressing the uncertainty of data is a very important component of the map, this element in maps in most cases attracts significant attention the difference of correct answers within the same map with and without a legend was 45% in extreme cases. An average difference was around 20% results also showed that the length of observation did not affect the accuracy of answers in general First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  26. 26. Thank you for your attention Jan Brus jan.brus@upol.cz http://geoinformatics.upol.cz/First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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