Mapping of Landscape Spatial Dynamics

Patterns by the Fuzzy Clustering Analysis

Daria Svidzinska

Department of Physical Geography and Geoecology

Faculty of Geography

Taras Shevchenko National University of Kyiv

“Four Dimensions of Landscape” Warsaw 15th - 17th of September 2011

The presentation proposes the method of spatial

dynamics pattern mapping

Rationale:

Landscape spatial dynamics is largely

controlled by lateral abiotic flows

Spatial configuration of lateral abiotic flows is

sufficiently controlled by relief

DEM and its geomorphometric analysis using

GIS is an appropriate basis for the automated

mapping of abiotic flows spatial pattern

configuration

Objective — to develop meaningful, objective and

reproducible method for mapping and classification of

landscape spatial dynamics patterns on the basis of

lateral flows properties

There were chosen four training sites with diverse

physico-geographical conditions

№ Abs. height, m Slope, deg. Natural zone

1 133-166 0-4 Mixed forests

2 209-356 0-10 Forest-steppe

3 70-168 0-8 Northern Steppe

4 27-48 0-2 Mid Steppe

Area - 10×10 = 100 sq. km

Resolution - 50 m

Matrix - 40 000 pixels

Every training site is

represented by:

Mapping procedure with the application of

geomorphometric analysis has a few stages

DEM Preprocessing

DEM Geomorphometric Analysis

Classification and Results

Interpretation

Free and Open Source Software (FOSS) was used to

perform data preprocessing and analysis

Open desktop GIS SAGA v. 2.0.7 (Conrad 2006, SAGA Development Team 2011)

DEM preprocessing and analysis

FuzME v. 3.5b software package (Minasny and McBratney 2002)

fuzzy unsupervised data classification

The SRTM DEM (Jarvis et al. 2008) preprocessing

includes three principal stages

Reprojecting and appropriate

spatial resolution choice GCS UTM

≈ 3 arcseconds 50 meters

Filtering Slope-based filter

(Vosselman 2000)

Multidirectional Lee filter

(Selige et al. 2006)

Hydrological correction (Planchon and Darboux 2002)

Primary topographic attributes describe the shape

of surface

absolute height slope mean curvature

plan curvature profile curvature

Secondary topographic attributes assess the

intensity of lateral processes

TWI SPI LS factor

downslope distance

gradient

downslope distance

gradient difference

Fuzzy clustering analysis is based on the k-means

algorithm (also known as the FCM)

Metric — the Euclidean distance

with preliminary data normalization

Fuzzy exponent — 1.3

Number of random start iterations

— 10, results with the lowest Wilk’s

lambda values were chosen

Number of clusters (classes) —

from 2 to 10

Valid optimal partition is determined by the minimal

values of clustering performance indices

FPI – Fuzziness Performance Index

MPE – Modified Partition Entropy

S – Separate distance

site ¹1: FPI, MPE, S; site ¹2: FPI, MPE, S;

site ¹3: FPI, MPE, S; site ¹4: FPI, MPE, S

2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Maps of crisped spatial dynamics classes for the

training sites № 1-4 (3 classes partition)

1 TE TA SaqT

2 AE TE SaqT

3 AE TE SaqT

4 AE TA EA

1 2

3 4

Maps of crisped spatial dynamics classes for the

training sites № 1-4 (6 classes partition)

1 AE TE TA SaqAT SaqTA SaqT

2 AE TE TA T SaqTA SaqT

3 AE TE TA T SaqTA SaqT

4 AE TA T EAT ETA EA

1 2

3 4

Conclusions

The SRTM DEM is a valid basis for geospatial analysis with the accuracy of

1:100 000 and smaller scales

Geomorphometric parameters obtained by DEM automated GIS-analysis

can be effectively applied for complex (ecological) classifications

Multidimensional fuzzy clustering analysis using the k-means method is an

objective and reliable procedure of data grouping

The primary topographic attributes are of principal importance for the spatial

units delineation, while the compound indices complement it with the process-

dynamic information

The method proposed is reproducible and well-applicable in low-budget

projects as it based on FOSS

The methodology proposed could be applied for the analysis and prediction

of contaminants lateral migration, for fast and accurate predictive landscape-

ecological, geomorphological, and soil mapping

Mapping of Landscape Spatial Dynamics

Patterns by the Fuzzy Clustering Analysis

Daria Svidzinska, Ukraine

d.svidzinska@gmail.com

Department of Physical Geography and Geoecology

Faculty of Geography

Taras Shevchenko National University of Kyiv

Thank you for your attention !

Dziękuję za uwagę !