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

Detection and Visualisations of Ecotones
Landscape Pattern under Uncertainty
Jan BRUS

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

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

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?


Spatial variability
 just about everything varies over space
(spatial dependence)

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


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


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.


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.


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)


Approach in visualisation

Examples

future
Eye-Tracking study


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)


Uncertainty visulalisation
of different data types and data quality


Uncertainty visualisation methods classification

(Senaratne & Gerharz 2011)

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

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


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?


Landuse of Trkmanka river catchment

- photointerpretation
from historical maps
and aerial images

- subjectivity of
results

Woods
Arrable land
Pastures
Orchards
Vineyards
Buildings
Water
Transect


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


Entropy visualisations


Visualisation methods


Results


Uncertainty visualisation of ecotones

adjacent method

a) grid b) blur c) transparency d) mosaic


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


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


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


Thank you for your attention

Jan Brus
jan.brus@upol.cz
http://geoinformatics.upol.cz/


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

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