Modelling Landscape Changes and Detecting Land Cover Types by Means of the Remote Sensing Data and ILWIS GIS

Arctic Center, University of Lapland
Modelling Landscape Changes and Detecting Land Cover
Types by Means of the Remote Sensing Data and ILWIS GIS
Presented at 3rd
International Conference Information Technologies. Problems and Solutions
Venue: Ufa State Petroleum Technological University.
Location: Ufa, Bashkortostan, Russia
Polina Lemenkova May 20, 2015

Table of Contents
1. Introduction
Research Summary
Study Area
2. Yamal Peninsula: Geographic Settings
Climate and Environment
Landscapes
3. Methodology
Minimum Distance Method
Workﬂow
Data Import and Conversion
Data Georeferencing
Supervised Classiﬁcation
4. Results
Computations
Mapping
Comments
Statistical Histograms
5. Discussion
6. Conclusion
7. Thanks
8. Bibliography
Polina Lemenkova Modelling Landscape Changes and Detecting Land Cover Types by Means of the RS Data and ILWIS GIS 2 / 25

Research Summary
Application
GIS and RS application for environmental studies of Yamal Peninsula, Russian North
Data
Landsat TM scenes for 1988, 2001 and 2011
Aim
Analysis of the land cover change dynamics in the past two decades (1988-2011).
Mapping vegetation in tundra landscapes
ILWIS GIS
GIS spatial analysis tools and Landsat imagery, calculation of NDVI by ILWIS GIS

Study Area
Source: B. C. Forbes
Location
Yamal Peninsula, north Russia.
Map source: Web.

Climate and Environment
Yamal Peninsula: geomorphology : ﬂat
geomorphology, elevations lower than 90 m
Processes:
seasonal ﬂooding,
active erosion processing,
permafrost distribution,
cryogenic landslides formation
Landslides affect local ecosystem structure.
Landslides change vegetation types
recovering after the disaster.
Landscapes of Yamal.
Source: http://pixtale.net/

Landscapes of the Yamal Peninsula - I

Landscapes of the Yamal Peninsula - II
Dry grass heath tundra (left). Sedge grass tundra (center). Dry short shrub tundra (right)
Landscapes of Yamal (left). Sphagnum moss (right)

Landscapes of the Yamal Peninsula - III
Dry short shrub sedge tundra (left). Wetlands (right)
Short shrub tundra

Methodology: ILWIS GIS
Technical tools: The RS data processing
was performed in ILWIS GIS software.
Research Methods:
Image interpretation (Landsat TM
scenes).
Supervised classification
Following working steps summarize
research scheme used in this research:
Data pre-processing
Creation of image composites of
several bands
Supervised classification using various
classifiers
Spatial analysis and interpretation of
the results
Time series analysis for detecting
changes
Final GIS mapping
ILWIS GIS

Minimum Distance Method
NDVI
Calculation of vegetation indices, e.g. Normalized Difference Vegetation Index (NDVI), is
one of the most useful attempts in biogeographical research methods.
Examples
Disadvantages: Technical details in modeling approach of image recognition: errors in
pixels classification due to the ambiguity
Concept
The principle of Minimum Distance method used for classification is based on calculating
the shortest straight-line distance in the Euclidian coordinate system from each pixel’s
DN to the pattern pixels of the land cover classes.
Examples
Misclassification: The misclassification by the Minimum Distance method may occur:
erroneous recognition of some pixels, insufficient representation of classes.

Workflow
Data pre-processing
import .img into ASCII raster format (GDAL). After converting, each image contained
collection of 7 raster bands
Pre-processing (visual color and contrast enhancement)
Geographic referencing of Landsat scenes, initially based on WGS 1984 datum:
UTM (Universal Transverse Mercator) Projection, Eastern Zone 42, Northern Zone
W, (Georeference Corner Editor)
Crop of study area: the area of interest (AOI) was identified and cropped on the raw
images. This area shows Bovanenkovo region in a large scale and best represents
typical tundra landscapes.
Supervsised Classification vi.GIS visualization and mapping

Data Import and Conversion
Test area selection (Mask): 67◦00’ - 72◦00’ E - 70◦00’ - 71◦00’ N.
3 selected Landsat TM satellite images show Yamal region in 1988, 2001, 2011.
Time span: 23 years (1988, 2001, 2011).
Summer months selected for vegetation assessment.
Data conversion / original images in format .TIFF converted to Erdas Imagine .img.
Initial remote sensing data, left to right: Landsat TM 1988, bands 7-3-1; Landsat TM
2011, pseudo natural colors composite; Landsat ETM + 2001 bands 6-3-1.

Georeferencing: Google Earth

Supervised Classiﬁcation of the Landsat TM Image

Computing Pixels on Various Land Cover Classes

Land Cover Classes
Results show classified maps of the selected region of Bovanenkovo on 1988, 2001 and
2011 yr (from left to right). GIS mapping is performed using image classification. Results
of the supervised classification show 3 maps of the vegetation distribution (above).

Comments
Classiﬁcation is based on the relationship between the spectral signatures and object
variables, i.e. vegetation types. Water areas are deﬁned as “no vegetation” class.
1988
For year 1988 “forest” class covered 8,188,926 pixels, which is 11,32% from the total
amount.
Examples
1988: Maximal area, except for water, is covered by the shrubland (15,29% from the
total).
2011
For year 2011, the percentage of the shrubland decreased down to 6,26%, while area of
forests increased from 11,32 to 15,97%.
Examples
2011: The area of grass remained relatively stable with values slightly increasing to
about 2%

Statistical Histograms: 1988 and 2011

Discussion
This research presented GIS based studies of the environment of Yamal Peninsula
The study is technically based on ILWIS GIS, effective tool for spatial analysis
The results of the spatial analysis are presented as 3 GIS maps illustrating changes
in vegetation based on the image analysis using Landsat TM.
Calculated land cover changes indicated vegetation dynamics in years 1988, 2001
and 2011.
Application of the RS data is especially important for studies of the northern
ecosystems, because it enables studying remotely located areas of Arctic
GIS-based processing of the RS data (Landsat TM) improves technical aspects of
the landscape studies and monitoring
The results show successful use of ILWIS GIS software for spatio-temporal
classiﬁcation of the satellite images aimed at ecological mapping.

Conclusion
Time-Series Analysis
Remote sensing plays important role in land use studies and serves as a valuable source
of spatial information for the time series analysis.
ILWIS GIS
Using enhanced ILWIS GIS tools to analyze and process satellite imagery contributes to
the environmental analysis of the land cover changes. The classiﬁcation used in the
current work is pixel-based aimed to allocate and categorize pixels on the image to the
created classes.
Examples
Remote Sensing: While traditional methods for vegetation monitoring are ﬁeldwork and
ground surveys, usually performed in large-scale areas, the use of remote sensing
techniques enables to monitor extended areas in a small scale, as well as to assess
temporal changes.

Thanks
Thank you for attention !
Acknowledgement:
Current research has been funded by the
Finnish Centre for International Mobility (CIMO)
Grant No. TM-10-7124, for author’s research stay at
Arctic Center, University of Lapland (July 1 - December 31, 2011),
Rovaniemi, Finland.

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