Cost-Effective Raster Image Processing for Geoecological Analysis Using “ISOCLUST” Classifier: a Case Study of Estonian Landscapes

Cost-Effective Raster Image Processing
for Geoecological Analysis
Using “ISOCLUST” Classiﬁer:
a Case Study of Estonian Landscapes
Presented at 5th
International Conference
’Modern Problems of Geoecology and Landscapes Studies’
Belarus State University (BSU)
Minsk, Belarus
Who? Polina Lemenkova
When? October 14, 2014

Introduction
Study Area
Research Aim
Landscapes
Examples of
Landscapes
Baltic Sea Coasts
Marine Landscapes: Pärnu
Lacustrine landscapes: Luitemaaa
Forest landscapes: Luitemaa
Anthropogenic
Impacts
Facts
Ecohouses Construction
Data
Methods
Results
Thanks
Bibliography

Study Area: Pärnu Region, Estonia
Study Area The research region
encompasses coastal area of
Baltic Sea: south-western
Estonia
Spatial extent Spatial extent of the study
area is limited to the
surroundings of Pärnu County.

Research Aim
The purpose of this study is following two aims:
GIS Analysis ﬁrst, a geographic (GIS) analysis of land cover types in the
coastal landscapes of western Estonia, Pärnu surroundings
at two various temporal dates (1992 and 2006)
IDRISI GIS second, an overview of the technical methods enabling
image processing by different tools of IDRISI GIS software"
Landsat TM
Images
Hence, the main research methods consists in processing
and classiﬁcation of satellite remote sensing data Landsat
TM aimed at land cover types recognition and thematic
mapping.

Landscapes
South-West
Estonia: Unique
Environment
South-west Estonia is known for unique environmental
settings: mild maritime climate, broad beaches, coniferous
pine forests on the coastal zone
Landscapes Landscapes here are rich and world-known for their
diversity, variability, unique composition structure and high
esthetic value
Examples Types of Landscapes Landscape types include, for
example, mixed and broadleaved forests, traditional
agricultural semi-natural landscapes, wooded meadows,
plant communities, heathland, bogs and moors, complex
anthropogenic areas with different land use structure,
shrubland, grasslands, birch-dominating coastal areas and
ﬂooded meadows

Examples of Landscapes: Baltic Sea Coasts
Examples Landscapes: Baltic Sea Coasts. Photos: author.

Marine Landscapes: Pärnu Coasts
Examples Marine Landscapes: Pärnu Coasts. Photos: author.

Lacustrine landscapes: Luitemaa Nature
Conservation Area
Examples Lacustrine landscapes: Luitemaa Nature Conservation
Area. Photos: author.

Forest landscapes: Luitemaa Nature
Conservation Area
Examples Forest Landscapes: Luitemaa Nature Conservation Area.
Photos: author.

Anthropogenic Impacts
Tourism
Activities
Pärnu region is traditionally popular as a tourism destination
due to favorable combination of factors:
geographic value: advantageous location on the coasts of
Baltic Sea
social value: good facilities for the tourism and tourism
reputation
environmental value: unique nature (marine landscapes,
pine forests)
Agricultural
Activities
Pärnu region is also known for traditional agricultural
activities (ﬁeld crops cultivation, intensive planting, etc), as
well as extensive housing development in the rural area.
Human Pressure All these factors create additional human pressure on the
local ecosystems and may lead to fragmentation of the
landscape structure.

Ecohouses Construction
Examples Ecohouses Construction Photos: author.

Examples: Eco-housing
Examples Eco-housing Photos: author.

Data
The research data used in this project include vector and
raster types of data:
Raster data: Thematic raster layers (GeoTIFF) Landsat TM including
scenes taken on 18 June 2006 and 03 June 1992.
Both images cover summer months, thus enabling
vegetation coverage to be easily recognized.
The images were downloaded from the Earth Science Data
Interface, Global Land Cover Facility.
Vector data: CORINE vector layers (abbreviation from Coordination of
Information on the Environment), developed by the
European Environmental Agency, EU Commission).
The CORINE data were stored in ESRI format shape-ﬁles.
They contain information on land use types provided by the
Estonian Land Board and available at the University of Tartu

Methods
GIS Projects The GIS projects has been organized and executed in two
different software: Arc GIS 10.0 and IDRISI GIS Andes 15.0.
Landsat TM The raster processing GIS approach and classification was
applied in the current work towards Landsat TM two
images."
ISOCLUST The method is based on the ISOCLUST unsupervised
classification executed by means of IDRISI GIS.
Machine
Learning
The ISOCLUST available in IDRISI GIS performs the most of the
image processing workflow in semi-automatically regime
Land Cover
Classes
It results in a map with pre-defined number of 16 land class
categories which enable to compare two different stages
of landscape development: “earlier” and “now”.
Objectivity The ISOCLUST method was chosen, since it enables to avoid
subjectivity in classification.

Workﬂow Step-1
IMPORT Initially, the data of Landsat TM were imported to IDRISI
Andes GIS from GeoTIFF format to IDRISI speciﬁc format .rst,
through Data Provider Format import.
As each Landsat TM scene is a multispectral image with
several spectral bands, each band was displayed and
visualized as a separate image.
COLOR
COMPOSITE
Afterwords, the images were composed using Color
Composite function.
The combination of three bands was made as a single color
composite image (bands 2-3-4).
This composition displays urban areas distinctively, which
enables to clearly recognize them
PROJECT Then data were organized in a created project in IDRISI GIS.

Workﬂow Step-1
Examples Image Processing

Workflow Step-2
CLASSIFICATION The next step includes application of chosen classification
method of ISOCLUST approach towards images processing.
ISOCLUST classifier technique is based on the histogram
peak selection technique"
Analysis of
Spectral
Signatures
The ground principle of the classification consists in the
analysis of spectral signatures that are individual for each
land cover class.
Analysis of
Spectral
Reflections
The analysis of spectral reflections strongly depends on the
local surface features: texture, structure, color, etc.
Spectral
Signatures
Information on spectral signatures is received by the satellite
sensors and recorded on the images (in this case, Landsat
TM). This information is used for the image classification.
Examples Information Extraction: Using individual characteristics of
objects, derived from the multispectral Landsat TM bands,
information from the images was extracted, analyzed and
used for land classification

Workﬂow Step-3
Image
Comparison
Changes in land cover types in selected Estonian
landscapes are shown on the histograms on 1992 and 2006.
2006 vs 1992 In 2006 the urban area became larger than in 1992 (land
cover class "3" on the histogram. This can be explained by
various reasons.
Impact Factors The most important reason is intense suburbanization =>
the major process in the current urban dynamics of modern
Estonia: intensive construction of summer homes and
cottages in the coastal area.
Buildings New buildings and houses created along the Pärnu Bay, =>
increased area of urban areas.

Image Processing
ISOCLUST ISOCLUST classiﬁcation of the images enabled to create
thematic maps of the same study areas
CORINE According to CORINE, there are 16 land cover types typical
for the study area.

Land Cover Classes
discontinuous urban fabric
industrial or commercial units
green urban areas
pastures
complex cultivation patterns
agriculture lands with grass
broad-leaved forest
coniferous forest
mixed forest
natural grassland
moors and heathlands
transitional woodland
beaches, dunes, sand
island marshes
water bodies
sea and ocean

Thanks
Thank you for attention !
Photo: author.
Acknowledgement:
The research has been done at the University of Tartu
under support of DoRa grant (ESF, Estonia).
The University of Tartu provided data and software:
CORINE vector layers, IDRISI GIS 15.0 and ArcGIS 10.0.

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