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

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS. Information Technologies. Problems and Solutions, Ufa State Petroleum Technological University, May 2015, Ufa, Russia. pp.265-271, 10.6084/m9 figshare.7211639. hal-02000251

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Submitted on 19 Feb 2019

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# MODELLING LANDSCAPE CHANGES AND DETECTING LAND COVER TYPES BY MEANS OF REMOTE SENSING DATA AND ILWIS GIS

## ИСПОЛЬЗОВАНИЕ ГИС-ТЕХНОЛОГИЙ (ILWIS GIS) И СПУТНИКОВЫХ СНИМКОВ ДЛЯ МОДЕЛИРОВАНИЯ ИЗМЕНЕНИЙ ЛАНДШАФТОВ И ДЕШИФРИРОВАНИЯ ТИПОВ ПОЧВЕННО-РАСТИТЕЛЬНОГО ПОКРОВА

P. Lemenkova,

Charles University in Prague, Faculty of Science, Institute for Environmental Studies
(Přírodovědecká fakulta),
Prague, Czech Republic.

Леменкова П.А.,

Карлов университет в Праге, Институт экологических исследований,
г. Прага, Чехия

e-mail: pauline.lemenkova@gmail.com

**Abstract.** The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The study area is located in Yamal peninsula, the Russian Federation. Landsat imagery covering study area in 1988, 2001 and 2011 has been analyzed using ILWIS GIS. The image processing was performed using semi-automated method of image interpretation. The remote sensing data classification from ILWIS menu enabled to map vegetation coverage over research area, which helped to identify land cover types and distribution in Yamal. Results show that Landsat TM imagery with 30 m mesh spacing is useful for landscape mapping and the interpretation of the vegetation cover types.

**Аннотация.** Цель данной работы состоит в технической апробации данных дистанционного зондирования Земли (спутниковые снимки Ландсат) для картографирования почвенно-растительного покрова северных экосистем. Область исследований – п-ов Ямал, Россия. Космические снимки Ландсат, покрывающие нужный участок территории в 1988, 2001 и 2011 г.г., были классифицированны и проанализированны в ГИС ИЛВИС (ILWIS GIS). Обработка снимков была осуществлена в полуавтоматическом режиме методами ГИС. Встроенный режим обработки и распознавания растровых изображений в ILWIS GIS позволил провести картографирование почвенно-растительного покрова и распространение ландшафтов на п-ве Ямал. Результаты показывают, изменение ландшафтов за исследуемый участок времени. Работа проиллюстрировала успешное применение спутниковых снимков Ландсат с 30-м разрешением для мелкомасштабного ландшафтного ГИС-картографирования.

**Keywords:** Landsat, remote sensing, supervised classification, modeling.

**Ключевые слова:** Ландсат, спутниковые снимки, классификация, моделирование.
The research area is geographically located on the western part of Yamal Peninsula, Western Siberia, Russia. The physical-geographical setting of the research area naturally defines its environmental conditions: the Yamal Peninsula is located on the northern part of the Western Siberia geographic region, which occupies the world's largest high-latitude wetland system and covers totally 900,000 km² of peatlands [1]. The dominating landscape on Yamal is lowland tundra. In the frame of current research dealing with GIS analysis of land cover and environment of northern ecosystems in Yamal, the Landsat satellite imagery has been classified using ILWIS GIS. The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The main objective is to detect vegetation distribution and changes over the area of Yamal by identification of plant and vegetation types in their correlation with pixel digital numbers (DNs), i.e. value of spectral reflectance.

<figure><img src="image_2.png" /><figcaption>Figure 1. Land Cover Classes: year 1988 (Landsat TM)</figcaption></figure>

Changes in vegetation distribution in northern ecosystems are caused by various reasons, such as climate and environmental changes, plant phenology and growing season, which has direct impact on the chlorophyll content in leaves. Satellite imagery is indispensable in this case for monitoring vegetation changes, as it provides accurate and low-cost source of data and enables recurrent remote sensing observations. Land cover monitoring highlights the dynamic of changes in vegetation coverage, types and spatial distribution.

**Methods**

Using remote sensing data, such as Landsat scenes, for detecting changes in vegetation types has great potential for environmental land cover mapping [2].
Manipulation with certain multispectral channels enables to calculate vegetation indices, e.g. LAI, calculation of biomass, etc.

<figure><img src="image_3.png" /><figcaption>Table 1. Histogram for supervised classification of Landsat TM image, 1988.</figcaption></figure>

Visualization of the result raster images facilitates detection of changes in vegetation coverage. Methods of Landsat scenes interpretation include data preprocessing, supervised classification, orthorectification, image correction, and raster data processing. Due to the popularity of Landsat data, there are numerous successful researches reporting application of Landsat scenes for land cover mapping in general, and for northern Polar environment in particular [3], [4], [5], [6], [7], [8], [9]. Following working steps summarize research scheme used in this research: 1) data pre-processing, 2) creation of image composites of several bands, 3) supervised classification using various classifiers 4) performing spatial analysis and interpretation of the results, 5) time series analysis for detecting changes, 6) final GIS mapping. The research data included Landsat scenes, received from the open source USGS (the U.S. Geological Survey) taken in 23-year time span, from 1988 until 2011. Complete Landsat data pre-processing usually includes image resampling, radiometric normalization and quality assessment [10]. In the scope of the current research, these steps were limited to image resampling, supervised classification and mapping. The principle of Minimum Distance method used for classification is based on the calculating of the shortest straight-line distance in Euclidian coordinate system from each pixel's DN to the pattern pixels of land cover classes. The main weakness of the supervised classification method is caused by modeling approach and technical details of image recognition, i.e. errors in pixels classification. Thus, the misclassification by the Minimum Distance method may occur due to the ambiguity and erroneous recognition of some of the pixels as well as insufficient representation of classes.
<figure><img src="image_4.png" /><figcaption>Figure 2. Land Cover Classes: year 2001 (Landsat TM)</figcaption></figure>

Following types of land cover classes were defined on the current scenes: shrub (bushes), grass, forest, urban areas, water, ice (covering selected water areas), rocks and sands. Some few pixels in an upper-right corner in a scene for 2011 did not match to these areas and remained unclassified, as they are covered by a cloud. A lot of classes in the test areas are self-evident, e.g. such as ocean water, sand, shelf areas. However, in practice, more sub-classes for water areas could be distinguished, due to different color shadows of water changing with depth and salinity. The recognition of various vegetation types is more complex. The vegetation types vary in response to the local conditions and differences in regional settings, mostly due to changing geochemical soil content and through different geomorphic and elevation ranges.

## Findings

The results show classified maps covering the same geographic region of Bovanenkovo in 1988, 2001 and 2011 years, respectively. The GIS mapping is performed using results of the image classification, based on the relationship between the spectral signatures and object variables, i.e. vegetation types over the research area. The results of the supervised classification show maps of the vegetation distribution. The water areas are defined as “no vegetation” class.
<figure><img src="image_5.png" /><figcaption>Figure 3. Land Cover Classes: year 2011 (Landsat TM)</figcaption></figure>

The statistical results of the classified land cover classes are represented in Histograms created by ILWIS (Table 1 and Table 2).

<figure><img src="image_6.png" /><figcaption>Table 2. Histogram for supervised classification of Landsat TM image, 2011.</figcaption></figure>

For the year 1988 The “forest” class covered 8,188,926 pixels, which is 11,32% from the total amount. The maximal area, except for water, is covered by the shrubland
(15,29% from the total). As for 2011, the percentage of the shrubland decreased down to 6,26%, while the area of forests increased from 11,32 to 15,97%. The area of grass remained relatively stable with values slightly increasing to about 2%, (Figure 1, Figure 2, Figure 3).

## Conclusion

Remote sensing plays important role in land use studies and serves as a valuable source of spatial information for time series analysis. 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. 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. The basis for this classification is DN of pixels. The complexity of the surface includes shapes, texture, area, size of selected fields, geographic context (location of the object within the area) as well as spectral color. The supervised classification enabled to assign land cover classes by adjusting classification parameters and thresholds in spectral signature of pixels. The results show successful use of ILWIS GIS software for spatio-temporal classification of the satellite images aimed at ecological mapping.

## Acknowledgements

The financial support of this research has been provided by the Fellowship of the Center for International Mobility (CIMO) of Finland. Contract No. TM-10-7124.

## References

1. Kremenetski, K.V., Velichko, A.A., Borisova, O.K., MacDonald, G.M., Smith, L.C., Frey, K.E., Orlova, L.A., 2003. Peatlands of the Western Siberian lowlands: current knowledge on zonation, carbon content and Late Quaternary history. Quaternary Science Reviews 22, pp 703–723.

2. Lo, P., & Choi, J., 2004. A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. International Journal of Remote Sensing, 25 (14): 2687-2700.

3. Epting, J., Verbyla, D., & Sorbel, B., 2005. Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment, 96, 328–339

4. Hansen, M.J., Franklin, S.E., Woudsma, C.G., & Peterson, M., 2001. Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM, and GIS data in the North Columbia Mountains, British Columbia, Canada. Remote Sensing of Environment, 77, 50–65.

5. Rees, W.G., Williams, M., & Vitebsky, P., 2003. Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge. Remote Sensing of Environment, 85, 441–452

6. Tommervik, H., Hogda, K.A., Solheim, I., 2003. Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat
MSS/TM data. Remote Sensing of Environment, 85, 370–388

7. Yuan, F., Sawaya, K.E., Loeffelholz, B.C., & Bauer, M.E., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment 98, 317–328

8. Schneider, J., Grosse, G., & Wagner D., 2009. Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions. Remote Sensing of Environment 113, 380–391

9. Ranson, K.J., Sun, G., Kharuk, V.I., & Kovacs, K., 2004. Assessing tundra–taiga boundary with multi-sensor satellite data. Remote Sensing of Environment, 93, 283–295.

10. Potapov, P., Turubanova, S., Hansen, M.C., 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment, 115, 548–561

УДК 681.3.06,658.512.2.011.56

# АВТОМАТИЗАЦИЯ ПРОЕКТИРОВАНИЯ КАРТЫ РАСКРОЯ ОБЕЧАЙКИ<fnref n="20" />

## COMPUTER-AIDED DESIGN CUTTING CARD SIDEWALL

Кульга К.С., Меньшиков П.В.,
ФГБОУ ВПО «Уфимский государственный авиационный технический университет»,
г. Уфа, Российская Федерация

K.S. Kulga, P.V. Menshikov,
FSBEI HPE «Ufa State Aviation Technical University»
Ufa, Russian Federation

e-mail: admin@stalkerplm.com

**Аннотация.** Рассматриваются результаты теоретических и экспериментальных исследований в области новых моделей и методов для создания программного обеспечения автоматизированной информационной системы, предназначенной для решения прямой и обратной задачи оптимального геометрического покрытия многосвязного ортогонального полигона (корпусная деталь «обечайка») для изделий химического и нефтегазового машиностроения.

**Abstract.** The results of theoretical and experimental research in the field of new models and methods for creating software automated information system for solving direct and inverse problems of optimal geometric cover of multi-linked orthogonal polygon (basic part "sidewall") for the products of the chemical and oil and gas engineering.

<footnote n="20">²⁰ Работа выполнена в рамках научного проекта №15-07-00928 при финансовой поддержке ФГБУ РФФИ</footnote>