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"Four Dimensions of Landscape" 15-17/09/2011 Warsaw, Poland
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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
Department of Physical Geography and Geoecology
Faculty of Geography
Taras Shevchenko National University of Kyiv
Thank you for your attention !
Dziękuję za uwagę !