Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GIS

Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GIS

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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova March 19, 2015
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Bibliography
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Research Goals Distribution of different types of landscapes in the wetland tundra of the Yamal Peninsula Monitoring changes in the landscapes of tundra Analysis of the landscape dynamics for 2 decades (1988-2011). Data: Landsat TM satellite images for 1988 and 2011 Application of ILWIS GIS for spatial analysis and data processing on the region of Bovanenkovo, Yamal. Technical approach: Remote sensing data processing by ILWIS GIS. Methods: Supervised classification of Landsat TM images Study area: tundra landscapes in the wetlands of the Yamal Peninsula in the Far North of Russia
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Geomorphology of the Yamal Peninsula Key points on the Yamal geomorphology: - Elevations almost flat, terrain less than 90 m. - Seasonal flooding - Active processes of erosion - Permafrost distribution - Local formation of ground cryogenic landslides - Specific ecological and climatic conditions (Arctic)
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Landscapes of the Yamal Peninsula * Cryogenic landslides are formed as a result of the soil erosion are typical processes in the Yamal Peninsula * Soil erosion develop as a result of the soil subsidence and soil thawing * Cryogen landslides have a negative impact on the local ecosystems * Cryogen landslides disrupt the strata of the soil and slow down restoration of vegetation after the landslide
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Cryogenic Landslides on the Yamal Peninsula – The negative effect of cryogenic landslides - changes in types of vegetation cover at the place of their formation. – For 10 years after active cryogenic landslide processes, the area of their occurrence remains uncovered. – Then, over the next few years, a process of slow restoration of the soil and vegetation cover takes place – Vegetation succession: plant communities with dominant herbs, mosses, lichens and sedge, willow and meadows with short shrubs. – Vegetation in the early stages of restoration (mosses, lichens) indirectly indicates recent formation of the cryogenic landslides – Meadows and willow shrub, on the contrary, indicate a relatively developed and restored plant community. – Areas subjected to the formation of cryogenic landslides in past 2-3 decades are usually characterized by the spread of willow and shrubbery, an indirect indicator of these processes in the past.
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Data Processing Algorithm Examples of various types of the vegetation typical for the Yamal tundra, Arctic. Algorithms of the data processing in ILWIS GIS: i Data collection, import and conversion ii Data: 2 Landsat TM images, 1988 & 2011 iii Data pre-processing iv Georeferencing: WGS 1984 ellipsoid to UTM, E42, NW v 3 spectral channels for image processing: color composite& multi-band layers vi Clustering segmentation and classification vii GIS mapping, spatial analysis viii Google Earth imagery verification ix Results interpretation
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Research Questions and Aims Research questions and aims: (I) The aim of the work is the use of GIS and RS data (Landsat TM) for monitoring tundra land cover types (II) Approaches: images classification, visualization and mapping (III) Have landscapes within the test territory of the study region changed over the past 14 years (1988-2011)? (IV) What types of land cover types were dominating previously, and which ones are now ? (V) Methodologically, how ILWIS GIS can be used to process RS data ?
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Landsat TM images AOI mask: 67◦ 00’-72◦ 00’E-70◦ 00’- 71◦ 00’N Time span: 23 years (1988-2011) Images taken during June to assess vegetation Original Landsat TM images (.tiff) were converted to the Erdas Imagine .img format.
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Image Georeferencing Georeference Corner Editor of ILWIS GIS
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Spectral Reflectance (SR) SR i. Image classification is grouping pixels into classes (merging pixels) SR ii. Clusters correspond to the types of vegetation cover according to the AOI settings SR iii. Classification is based on using spectral brightness of the image pixels SR iv. Spectral and texture characteristics of various land cover types are displayed on the image as different spectral brightnesses of the pixels SR v. Spectral reflectances show spectral reflectivity of the land cover types (through pixels’ spectral brightness) and individual properties of the vegetation objects detected on a raster image
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Image Clustering (a) Cluster analysis is a statistical procedure for grouping objects (pixels on a raster image) (b) Pixels are ordered into homogeneous thematic groups (clusters) (c) Each digital pixel in the image is assigned to the corresponding land cover type group (d) Grouping is based on the proximity of the spectral brightness value (Digital Number, DN) of the pixel to the centroid. (e) The logical segmentation algorithm consists of grouping the pixels in the image (merging pixels) into clusters. (f) Grouping pixels occurs in semi-automatic mode based on the distinctness from neighboring (neighbor pixels). (g) The process is repeated interactively until optimal values of the classes (and pixels attached to these classes) are reached.
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Image Classification (IC) IC-1 Thematic mapping is based on the results of the classification of images IC-2 Visualization of the landscapes’ structure and vegetation types within the AOI. IC-3 To classify land cover types, image pixels were identified for each category and grouped into different land categories. IC-4 Land cover types were evaluated and identified with each land cover class IC-5 Number of cluster groups is 13 representing vegetation land cover types of the Yamal tundra
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Mapping Results 1988 2011
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Results Interpretation Statistical results of calculations of types of vegetation cover were obtained in a semi-automatic mode in ILWIS GIS 1988 ’willow shrubs’ type covered 412,292 pixels from the total part of the AOI, and ’high willow’ class is 823,430 pixels 2011: willow increased to 651427 pixels, (’willow shrubs’), and 893092 pixels (’high willows’) Both combined classes of willows, typical for AOI with a high water content, cover total 1544519 pixels, which is 40.27 %. Area of grasses decreased compared to shrub and willow Max area covered by class ’heather and dry grass’ is 933798 pixels
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Google Earth Verification The selected area represents one of the most diversified part of the tundra landscapes of Yamal AOI has a complex structure of boggy landscapes and unique types of vegetation Therefore, in order to control the most difficult areas, the images were verified by Google Earth Visualization of the same area in the satellite image and Google Earth image at the same time. This made it possible to visually check heterogeneous areas with mixed land cover types and landscapes
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Conclusions I. Monitoring landscape changes is an important tool for assessing the ecological stability of a region II. Spatial analysis of the multi-temporal satellite images by ILWIS GIS algorithms is an effective tool III. Research demonstrated how Yamal wetland tundra landscapes changed over a 23-year period of time IV. Data included LandsatTM satellite imagery covering the Yamal Peninsula, Far North of Russia V. Image processing was done by classification methods. VI. Results shown changes in the landscapes from 1988 to 2011 VII. Results confirm presence of the destructive processes caused changes in tundra boggy landscapes. VIII. Research demonstrated successful ILWIS GIS based of the RS data analysis, effective for tundra monitoring
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks 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 (2012).
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks Bibliography [1] M. Klaučo, B. Gregorová, U. Stankov, V. Marković, and P. Lemenkova. “Landscape metrics as indicator for ecological significance: assessment of Sitno Natura 2000 sites, Slovakia”. In: Ecology and Environmental Protection. Proceedings of International Conference (Belarusian State University, Mar. 19–20, 2014). Minsk, Belarus: BSU Press, pp. 85–90. DOI: 10.6084/m9.figshare.7434200. URL: http://elib.bsu.by/handle/123456789/103362. [2] M. Klaučo, B. Gregorová, U. Stankov, V. Marković, and P. Lemenkova. “Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area”. In: Central European Journal of Geosciences 5.1 (2013), pp. 28–42. ISSN: 1896-1517. DOI: 10.2478/s13533-012-0120-0. URL: https://www.degruyter.com/view/j/geo.2013.5.issue-1/s13533-012-0120-0/s13533-012-0120- 0.xml?format=INT. [3] P. Lemenkova. “Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GIS”. Russian. In: Actual Problems of the State and Management of Water Resources. Proceedings of the International Conference (Volga State University of Technology, Mar. 19, 2015). Ed. by A. V. Kusakin and T. N. Efimova. Yoshkar-Ola, Mari El, Russia: VolgaTech Press, pp. 110–113. ISBN: 978-5-9903856-9-6. DOI: 10.6084/m9.figshare.7435520. URL: https://elibrary.ru/item.asp?id=24613025. [4] P. Lemenkova. “Mapping agricultural lands by means of GIS for monitoring use of natural resources”. Russian. In: Actual Problems of the Conservation and Development of Biological Resources. Proceedings of the International Conference (Ural State Agrarian University UrGAU, Feb. 27–28, 2015). Ed. by I. M. Donnik, B. A. Voronin, I. P. Zorina, and N. V. Roshchina. Yekaterinburg, Russia: UrGAU Press, pp. 226–229. ISBN: 978-5-87203-374-5. DOI: 10.6084/m9.figshare.7211804. [5] P. Lemenkova. “Spatial Analysis for Environmental Mapping of Šumava National Park”. In: 6th Annual PGS Conference. Conference Abstracts (Benátská 2, Praha 2, Czechia, Jan. 27, 2015). Inst. for Environmental studies, Charles University in Prague (CU): CU Press, p. 53. DOI: 10.6084/m9.figshare.7211843. URL: https://www.natur.cuni.cz/fakulta/zivotni- prostredi/aktuality/prilohy-a-obrazky/konference/pgs-koference-2015-program. [6] P. Lemenkova. “Risks of Cryogenic Landslide Hazards and Their Impact on Ecosystems in Cold Environments”. In: The Effects of Irrigation and Drainage on Rural and Urban Landscapes. Book of Abstracts, 1st International Symposium. IRLA2014 (Technological Educational Institution (TEIEP) of Epirus, Nov. 26–28, 2014). Patras, Greece: IRLA, p. 27. DOI: 10.6084/m9.figshare.7211846. URL: https://www.irrigation-Management.eu/. [7] P. Lemenkova. “Detection of Vegetation Coverage in Urban Agglomeration of Brussels by NDVI Indicator Using eCognition Software and Remote Sensing Measurements”. In: GIS and Remote Sensing. GIS Day. Proceedings of the 3rd International Conference (Environmental Res. & GIS Centre, Nov. 17–19, 2014). Ed. by H. Manandyan. Tsaghkadzor, Armenia: Print Way, pp. 112–119. DOI: 10.6084/m9.figshare.7434215.
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks [8] P. Lemenkova. “Cost-Effective Raster Image Processing for Geoecological Analysis using ISOCLUST Classifier: a Case Study of Estonian Landscapes”. In: Modern Problems of Geoecology and Landscapes Studies. Proceedings of the 5th International Conference (Belarus State University (BSU), Oct. 14–17, 2014). Ed. by A. N. Vitchenko, G. I. Martsinkevich, B. P. Vlasov, N. V. Gagina, and V. M. Yatsukhno. Minsk, Belarus: BSU Press, pp. 74–76. ISBN: 978-985-476-629-4. DOI: 10.6084/m9.figshare.7211870. URL: https://www.elib.bsu.by/bitstream/123456789/103641/1/geoconf80.pdf. [9] P. Lemenkova. “Opportunities for Classes of Geography in the High School: the Use of ’CORINE’ Project Data, Satellite Images and IDRISI GIS for Geovisualization”. In: Perspectives for the Development of Higher Education. Proceedings of 7th International Conference (Grodno State Agrarian University GGAU, Apr. 24–25, 2014). Ed. by V. Pestis, A. A. Duduk, A. V. Sviridov, and S. I. Yurgel. Grodno, Belarus: GGAU Press, pp. 284–286. ISBN: 978-985-537-042-1. DOI: 10.6084/m9.figshare.7211933. URL: https://www.ggau.by/downloads/ prints/Sbornik_72014_konferencii_perspektivy_razvitija_vysshej_shkoly.pdf. [10] P. Lemenkova. “Monitoring changes in agricultural landscapes of Central Europe, Hungary: application of ILWIS GIS for image processing”. In: Geoinformatics: Theoretical and Applied Aspects. Proceedings of 12th International Conference (Great Conference Hall National Academy of Science of Ukraine, May 13–16, 2013). Kiev, Ukraine. DOI: 10.3997/2214-4609.20142479. [11] P. Lemenkova. “Processing Remote Sensing Data Using Erdas Imagine for Mapping Aegean Sea Region, Turkey”. In: Informatics. Problems, Methodology, Technologies. Proceedings of 15th International Conference (Voronezh State University, Feb. 12–13, 2015). Vol. 3. Voronezh, Russia: VGU Press, pp. 11–15. ISBN: 5-9273-0681-0. DOI: 10.6084/m9.figshare.7434191. URL: https://elibrary.ru/item.asp?id=26663916. [12] P. Lemenkova. “Impacts of Climate Change on Landscapes in Central Europe, Hungary”. In: Current Problems of Ecology. Ecological Monitoring and Management of Natural Protection. Proceedings of 8th International Conference (Yanka Kupala State University of Grodno YKSUG, Oct. 24–26, 2012). Vol. 2. Grodno, Belarus: YKSUG Press, pp. 134–136. DOI: 10.6084/m9.figshare.7211993. URL: https://elib.grsu.by/katalog/173327-393652.pdf. [13] P. Lemenkova. “Google Earth web service as a support for GIS mapping in geospatial research at universities”. Russian and English. In: Web-Technologies in the Educational Space. Problems, Approaches, Perspectives. Proceedings of the International Conference (Arzamas branch of the N. I. Lobachevsky State University of Nizhny Novgorod, Mar. 26–27, 2015). Ed. by S. V. Aryutkina and S. V. Napalkov. Arzamas, Russia: OOO Rastr-NN, Mar. 2015, pp. 460–464. ISBN: 978-5-9906469-1-9. DOI: 10.6084/m9.figshare.7211798. URL: https://elibrary.ru/item.asp?id=23426340. [14] P. Lemenkova. “Geospatial Technology for Land Cover Analysis”. In: Middle East and Africa (MEA) Geospatial Digest (Nov. 2013). DOI: 10.6084/m9.figshare.7439228. URL: https://www.geospatialworld.net/article/geospatial-technology-for-land-cover-analysis/. e-magazine (periodical).
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  Satellite Image Based Mappingof Wetland Tundra Landscapes Using ILWIS GIS Polina Lemenkova Outline Introduction Research Goals Geographic Settings Geomorphology of the Yamal Peninsula Landscapes of the Yamal Peninsula Cryogenic Landslides on the Yamal Peninsula Methods Data Processing Algorithm Research Questions and Aims Landsat TM images Image Georeferencing Spectral Reflectance Image Clustering Image Classification Results Mapping Results Results Interpretation Google Earth Verification Conclusions Thanks [15] P. Lemenkova. “Seagrass Mapping and Monitoring Along the Coasts of Crete, Greece”. M.Sc. Thesis. Enschede, Netherands: University of Twente, Faculty of Earth Observation and Geoinformation (ITC), Mar. 8, 2011. 158 pp. DOI: 10.13140/RG.2.2.16945.22881. URL: https://thesiscommons.org/p4h9v. [16] P. Lemenkova. “Using ArcGIS in Teaching Geosciences”. Russian. B.Sc. Thesis. Moscow, Russia: Lomonosov Moscow State University, Faculty of Educational Studies, June 5, 2007. 58 pp. DOI: 10.13140/RG.2.2.12357.70885. URL: https://thesiscommons.org/nmjgz. [17] P. Lemenkova. “Geoecological Mapping of the Barents and Pechora Seas”. Russian. B.Sc. Thesis. Moscow, Russia: Lomonosov Moscow State University, Faculty of Geography, Deparmnet of Cartography and Geoinformatics, May 18, 2004. 78 pp. DOI: 10.13140/RG.2.2.25360.05122. URL: https://thesiscommons.org/bvwcr. [18] P. Lemenkova and I. Elek. “Clustering Algorithm in ILWIS GIS for Classification of Landsat TM Scenes: a Case Study of Mecsek Hills Region, Hungary”. In: Geosciences and Environment. Near-Surface Geophysics. Proceedings 3rd International Conference (Association of Geophysicists & Environmentalists of Serbia (AGES), May 27–29, 2012). Ed. by S. Komatina-Petrovic. Belgrade, Serbia. DOI: 10.6084/m9.figshare.7434218.v1. [19] P. Lemenkova, B. Forbes, and T. Kumpula. “Mapping Land Cover Changes Using Landsat TM: A Case Study of Yamal Ecosystems, Arctic Russia”. In: Geoinformatics: Theoretical and Applied Aspects. Proceedings of the 11th International Conference (Great Conference Hall National Academy of Science of Ukraine, May 14–17, 2012). Kiev, Ukraine. DOI: 10.6084/m9.figshare.7434242.v1. URL: https://elibrary.ru/item.asp?id=24527736. [20] I. Suetova, L. Ushakova, and P. Lemenkova. “Geoecological Mapping of the Barents Sea Using GIS”. In: Digital Cartography & GIS for Sustainable Development of Territories. Proceedings of the International Cartographic Conference. ICC (July 9–16, 2005). La Coruña, España, 2005. DOI: 10.6084/m9.figshare.7435529. URL: https://icaci.org/icc2005/. [21] I. Suetova, L. Ushakova, and P. Lemenkova. “Geoinformation mapping of the Barents and Pechora Seas”. In: Geography and Natural Resources 4 (Dec. 2005). Ed. by V. A. Snytko, pp. 138–142. ISSN: 1875-3728. DOI: 10.6084/m9.figshare.7435535. URL: http://www.izdatgeo.ru/journal.php?action=output&id=3&lang_num=2&id_dop=68.