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

This presentation is co-financed by the European Social Fund and the state budget of the Czech Republic

Detection and Visualisations of Ecotones Landscape Pattern under Uncertainty

Jan BRUS

First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

Uncertaintyour imperfect and inexact knowledge of the world

Datawe are unsure of what exactly we observe or measure in society or nature

Rulewe are unsure of the conclusions we can draw from even perfect data (how we reason with the observations)

Definitions


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?

Is uncertainty visualisation necessary?


just about everything varies over space(spatial dependence)

therefore, an estimation of uncertainty is important The estimate can be:

descriptive quantitative

Spatial variability


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 typesof uncertainty


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.

Ecotone project


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.

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)

Sources of uncertainty


Approach in visualisation

futureEye-Tracking study

Examples


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)

Visual variables in uncertainty visualisation


Uncertainty visulalisation of different data types and data quality


Uncertainty visualisation methods classification

(Senaratne & Gerharz 2011)


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

Usability studies


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

Area of interest


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?

Representation data


Landuse of Trkmanka river catchment

WoodsArrable landPasturesOrchardsVineyardsBuildingsWaterTransect

- photointerpretation from historical maps and aerial images

- subjectivity of results


Land Facet Corridor Tools for ArcGIS

Delineation of ecotones – entropy approach

• can be used for each map layer

• combinantion of entropies

• showing most uncertain

• map algebra


Entropy visualisations


Visualisation methods


Results


Results


Uncertainty visualisation of ecotones

d) mosaicc) transparencyb) blura) grid

adjacent method


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

Results


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

Further methods to delinination and research


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

Preliminary Eye-tracking results


Thank you for your attention Jan Brus

[email protected] http://geoinformatics.upol.cz/

Documents

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