ݺߣshows by User: PolinaLemenkova / http://www.slideshare.net/images/logo.gif ݺߣshows by User: PolinaLemenkova / Fri, 21 Jun 2024 17:25:00 GMT ݺߣShare feed for ݺߣshows by User: PolinaLemenkova Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria /slideshow/economic-assessment-of-landslide-risk-for-the-waidhofen-a-d-ybbs-region-alpine-foreland-lower-austria/269811756 presentationybbs-240621172500-032edace
These slides present a case study on landslide risk assessment for National Civil Protection Management in Low Austria. The research has been performed using ArcGIS-based approach. Research goal consists in improving the national landslide disaster control by evaluating the expected number of people affected by landslides. Research aim is to assess potential risk and estimate economic damage caused by landslides. Study area is focused on the Ybbs valley, Lower Austria. The research was done using Arc GIS-based spatial analysis and estimation of the potential monetary losses caused by landslides. The technical approach consists in the geospatial and economic data processing by Arc GIS using spatial and economic analysis. Methodology includes defining the elements at risk located in the risk zone of 100 m near landslides, and assessment of the potential consequences. The presented results included calculated possible losses caused by the destruction of immobility and transport, such as costs for buildings demolition, restoration, roads rebuilding, debris transport, excavation and removal in the study area.]]>
These slides present a case study on landslide risk assessment for National Civil Protection Management in Low Austria. The research has been performed using ArcGIS-based approach. Research goal consists in improving the national landslide disaster control by evaluating the expected number of people affected by landslides. Research aim is to assess potential risk and estimate economic damage caused by landslides. Study area is focused on the Ybbs valley, Lower Austria. The research was done using Arc GIS-based spatial analysis and estimation of the potential monetary losses caused by landslides. The technical approach consists in the geospatial and economic data processing by Arc GIS using spatial and economic analysis. Methodology includes defining the elements at risk located in the risk zone of 100 m near landslides, and assessment of the potential consequences. The presented results included calculated possible losses caused by the destruction of immobility and transport, such as costs for buildings demolition, restoration, roads rebuilding, debris transport, excavation and removal in the study area.]]> Fri, 21 Jun 2024 17:25:00 GMT /slideshow/economic-assessment-of-landslide-risk-for-the-waidhofen-a-d-ybbs-region-alpine-foreland-lower-austria/269811756 PolinaLemenkova@slideshare.net(PolinaLemenkova) Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria PolinaLemenkova These slides present a case study on landslide risk assessment for National Civil Protection Management in Low Austria. The research has been performed using ArcGIS-based approach. Research goal consists in improving the national landslide disaster control by evaluating the expected number of people affected by landslides. Research aim is to assess potential risk and estimate economic damage caused by landslides. Study area is focused on the Ybbs valley, Lower Austria. The research was done using Arc GIS-based spatial analysis and estimation of the potential monetary losses caused by landslides. The technical approach consists in the geospatial and economic data processing by Arc GIS using spatial and economic analysis. Methodology includes defining the elements at risk located in the risk zone of 100 m near landslides, and assessment of the potential consequences. The presented results included calculated possible losses caused by the destruction of immobility and transport, such as costs for buildings demolition, restoration, roads rebuilding, debris transport, excavation and removal in the study area. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationybbs-240621172500-032edace-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> These slides present a case study on landslide risk assessment for National Civil Protection Management in Low Austria. The research has been performed using ArcGIS-based approach. Research goal consists in improving the national landslide disaster control by evaluating the expected number of people affected by landslides. Research aim is to assess potential risk and estimate economic damage caused by landslides. Study area is focused on the Ybbs valley, Lower Austria. The research was done using Arc GIS-based spatial analysis and estimation of the potential monetary losses caused by landslides. The technical approach consists in the geospatial and economic data processing by Arc GIS using spatial and economic analysis. Methodology includes defining the elements at risk located in the risk zone of 100 m near landslides, and assessment of the potential consequences. The presented results included calculated possible losses caused by the destruction of immobility and transport, such as costs for buildings demolition, restoration, roads rebuilding, debris transport, excavation and removal in the study area.
Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria from Universit辰t Salzburg
]]> 8 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationybbs-240621172500-032edace-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Flood Hazard and Natural Risk Assessment: A Case Study of Bangladesh /slideshow/flood-hazard-and-natural-risk-assessment-a-case-study-of-bangladesh/269810954 lemenkovaassignement4-240621161252-e012ac67
This work presents a case study of floods in Bangladesh using GIS analysis and deep learning. Floods are the natural hazard with the globe coverage and high frequency. Flood risk management involves segments of water use (potable water, industrial use, irrigation, recreation, energy production) and analysis. Flood risk analysis is computed in terms of the risk equation using parameters of hazard, vulnerability, exposure and capacity. Flood correlates with large-scale events and climate change which results in multi-hazard scenarios. The major reasons of flood include climate factors such as heavy rainfall. Second factor is hydrological and geological capacities of water permeability when land surface lacks the capacity to convey excess water. Flooding can also result from other phenomena, storm surge, tropical cyclone, tsunami, high tide. There are diverse flood types including riverine floods, flash floods, urban floods, glacial lake outburst floods and coastal floods (climatic and non-climatic processes cause these different types of floods).]]>
This work presents a case study of floods in Bangladesh using GIS analysis and deep learning. Floods are the natural hazard with the globe coverage and high frequency. Flood risk management involves segments of water use (potable water, industrial use, irrigation, recreation, energy production) and analysis. Flood risk analysis is computed in terms of the risk equation using parameters of hazard, vulnerability, exposure and capacity. Flood correlates with large-scale events and climate change which results in multi-hazard scenarios. The major reasons of flood include climate factors such as heavy rainfall. Second factor is hydrological and geological capacities of water permeability when land surface lacks the capacity to convey excess water. Flooding can also result from other phenomena, storm surge, tropical cyclone, tsunami, high tide. There are diverse flood types including riverine floods, flash floods, urban floods, glacial lake outburst floods and coastal floods (climatic and non-climatic processes cause these different types of floods).]]> Fri, 21 Jun 2024 16:12:52 GMT /slideshow/flood-hazard-and-natural-risk-assessment-a-case-study-of-bangladesh/269810954 PolinaLemenkova@slideshare.net(PolinaLemenkova) Flood Hazard and Natural Risk Assessment: A Case Study of Bangladesh PolinaLemenkova This work presents a case study of floods in Bangladesh using GIS analysis and deep learning. Floods are the natural hazard with the globe coverage and high frequency. Flood risk management involves segments of water use (potable water, industrial use, irrigation, recreation, energy production) and analysis. Flood risk analysis is computed in terms of the risk equation using parameters of hazard, vulnerability, exposure and capacity. Flood correlates with large-scale events and climate change which results in multi-hazard scenarios. The major reasons of flood include climate factors such as heavy rainfall. Second factor is hydrological and geological capacities of water permeability when land surface lacks the capacity to convey excess water. Flooding can also result from other phenomena, storm surge, tropical cyclone, tsunami, high tide. There are diverse flood types including riverine floods, flash floods, urban floods, glacial lake outburst floods and coastal floods (climatic and non-climatic processes cause these different types of floods). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaassignement4-240621161252-e012ac67-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This work presents a case study of floods in Bangladesh using GIS analysis and deep learning. Floods are the natural hazard with the globe coverage and high frequency. Flood risk management involves segments of water use (potable water, industrial use, irrigation, recreation, energy production) and analysis. Flood risk analysis is computed in terms of the risk equation using parameters of hazard, vulnerability, exposure and capacity. Flood correlates with large-scale events and climate change which results in multi-hazard scenarios. The major reasons of flood include climate factors such as heavy rainfall. Second factor is hydrological and geological capacities of water permeability when land surface lacks the capacity to convey excess water. Flooding can also result from other phenomena, storm surge, tropical cyclone, tsunami, high tide. There are diverse flood types including riverine floods, flash floods, urban floods, glacial lake outburst floods and coastal floods (climatic and non-climatic processes cause these different types of floods).
Flood Hazard and Natural Risk Assessment: A Case Study of Bangladesh from Universit辰t Salzburg
]]> 37 0 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaassignement4-240621161252-e012ac67-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Exploitation of historical analog seismological records by image processing and machine learning /slideshow/exploitation-of-historical-analog-seismological-records-by-image-processing-and-machine-learning/269687399 presentationlemenkova2may2023compressed-240614152033-0f90830e
This project addresses the challenges of vectorising the old seismograms which revitalise the existing archives by R2V algorithms using ML methods. Challenge of big data in seismic studies: massif volumes of historical seismograms from ROB exist and present a source of information. Archive old data must be processed, digitised and ‘revitalised’. In this view, this research contributes to these goals by presenting the developed automated ML methods of vectorising seismograms with minimised human interaction and maximised programming approach in trace vectorisation.]]>
This project addresses the challenges of vectorising the old seismograms which revitalise the existing archives by R2V algorithms using ML methods. Challenge of big data in seismic studies: massif volumes of historical seismograms from ROB exist and present a source of information. Archive old data must be processed, digitised and ‘revitalised’. In this view, this research contributes to these goals by presenting the developed automated ML methods of vectorising seismograms with minimised human interaction and maximised programming approach in trace vectorisation.]]> Fri, 14 Jun 2024 15:20:33 GMT /slideshow/exploitation-of-historical-analog-seismological-records-by-image-processing-and-machine-learning/269687399 PolinaLemenkova@slideshare.net(PolinaLemenkova) Exploitation of historical analog seismological records by image processing and machine learning PolinaLemenkova This project addresses the challenges of vectorising the old seismograms which revitalise the existing archives by R2V algorithms using ML methods. Challenge of big data in seismic studies: massif volumes of historical seismograms from ROB exist and present a source of information. Archive old data must be processed, digitised and ‘revitalised’. In this view, this research contributes to these goals by presenting the developed automated ML methods of vectorising seismograms with minimised human interaction and maximised programming approach in trace vectorisation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova2may2023compressed-240614152033-0f90830e-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This project addresses the challenges of vectorising the old seismograms which revitalise the existing archives by R2V algorithms using ML methods. Challenge of big data in seismic studies: massif volumes of historical seismograms from ROB exist and present a source of information. Archive old data must be processed, digitised and ‘revitalised’. In this view, this research contributes to these goals by presenting the developed automated ML methods of vectorising seismograms with minimised human interaction and maximised programming approach in trace vectorisation.
Exploitation of historical analog seismological records by image processing and machine learning from Universit辰t Salzburg
]]> 13 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova2may2023compressed-240614152033-0f90830e-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Portfolio in Cartography and Remote Sensing /slideshow/portfolio-in-cartography-and-remote-sensing/269686606 portfoliorspolina-240614141748-3d480a93
My professional portfolio briefly summarizes my skills in in Cartography and Remote Sensing. It includes the selected examples of my works on RS data processing, including satellite image processing, cartographic modelling using DEM and terrain data, case studies on thematic mapping and modelling Earth Observation (EO) data. The illustrations are copied from my published papers.]]>
My professional portfolio briefly summarizes my skills in in Cartography and Remote Sensing. It includes the selected examples of my works on RS data processing, including satellite image processing, cartographic modelling using DEM and terrain data, case studies on thematic mapping and modelling Earth Observation (EO) data. The illustrations are copied from my published papers.]]> Fri, 14 Jun 2024 14:17:48 GMT /slideshow/portfolio-in-cartography-and-remote-sensing/269686606 PolinaLemenkova@slideshare.net(PolinaLemenkova) Portfolio in Cartography and Remote Sensing PolinaLemenkova My professional portfolio briefly summarizes my skills in in Cartography and Remote Sensing. It includes the selected examples of my works on RS data processing, including satellite image processing, cartographic modelling using DEM and terrain data, case studies on thematic mapping and modelling Earth Observation (EO) data. The illustrations are copied from my published papers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/portfoliorspolina-240614141748-3d480a93-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> My professional portfolio briefly summarizes my skills in in Cartography and Remote Sensing. It includes the selected examples of my works on RS data processing, including satellite image processing, cartographic modelling using DEM and terrain data, case studies on thematic mapping and modelling Earth Observation (EO) data. The illustrations are copied from my published papers.
Portfolio in Cartography and Remote Sensing from Universit辰t Salzburg
]]> 22 0 https://cdn.slidesharecdn.com/ss_thumbnails/portfoliorspolina-240614141748-3d480a93-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces /slideshow/vectorising-analog-seismograms-by-techniques-of-machine-learning-for-automated-discriminating-of-seismic-signal-traces/269685618 presentationlemenkova10dec2021compressed-240614130448-836e15c2
This presentation shows the case of digitising old historical scanned seismograms into vector format. The original paper-based seismograms in TIFF format were obtained from the archives of Royal Observatory of Belgium (ROB), Department of Seismology & Gravimetry. The data were recorded in 1954 by the Galitzine seismometer in Uccle station, Belgium. The aim was to digitise large archive of the old paper-based seismograms from ROB quickly, accurately and automatically. The data were processed using automatic computer-based methods of digitising and vectorising raster formats. The advantages of the machine learning (ML)/ deep learning (DL) methods are discussed and the case study is presented. Challenge of big data in seismic studies consists in the massif volumes of historical seismograms which exist in ROB and present a source of valuable information for geophysics. These archive old data must be processed, digitised and ‘revitalised’. This study contributed to the development of methods of signal processing by ML methods.]]>
This presentation shows the case of digitising old historical scanned seismograms into vector format. The original paper-based seismograms in TIFF format were obtained from the archives of Royal Observatory of Belgium (ROB), Department of Seismology & Gravimetry. The data were recorded in 1954 by the Galitzine seismometer in Uccle station, Belgium. The aim was to digitise large archive of the old paper-based seismograms from ROB quickly, accurately and automatically. The data were processed using automatic computer-based methods of digitising and vectorising raster formats. The advantages of the machine learning (ML)/ deep learning (DL) methods are discussed and the case study is presented. Challenge of big data in seismic studies consists in the massif volumes of historical seismograms which exist in ROB and present a source of valuable information for geophysics. These archive old data must be processed, digitised and ‘revitalised’. This study contributed to the development of methods of signal processing by ML methods.]]> Fri, 14 Jun 2024 13:04:48 GMT /slideshow/vectorising-analog-seismograms-by-techniques-of-machine-learning-for-automated-discriminating-of-seismic-signal-traces/269685618 PolinaLemenkova@slideshare.net(PolinaLemenkova) Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces PolinaLemenkova This presentation shows the case of digitising old historical scanned seismograms into vector format. The original paper-based seismograms in TIFF format were obtained from the archives of Royal Observatory of Belgium (ROB), Department of Seismology & Gravimetry. The data were recorded in 1954 by the Galitzine seismometer in Uccle station, Belgium. The aim was to digitise large archive of the old paper-based seismograms from ROB quickly, accurately and automatically. The data were processed using automatic computer-based methods of digitising and vectorising raster formats. The advantages of the machine learning (ML)/ deep learning (DL) methods are discussed and the case study is presented. Challenge of big data in seismic studies consists in the massif volumes of historical seismograms which exist in ROB and present a source of valuable information for geophysics. These archive old data must be processed, digitised and ‘revitalised’. This study contributed to the development of methods of signal processing by ML methods. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova10dec2021compressed-240614130448-836e15c2-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation shows the case of digitising old historical scanned seismograms into vector format. The original paper-based seismograms in TIFF format were obtained from the archives of Royal Observatory of Belgium (ROB), Department of Seismology &amp; Gravimetry. The data were recorded in 1954 by the Galitzine seismometer in Uccle station, Belgium. The aim was to digitise large archive of the old paper-based seismograms from ROB quickly, accurately and automatically. The data were processed using automatic computer-based methods of digitising and vectorising raster formats. The advantages of the machine learning (ML)/ deep learning (DL) methods are discussed and the case study is presented. Challenge of big data in seismic studies consists in the massif volumes of historical seismograms which exist in ROB and present a source of valuable information for geophysics. These archive old data must be processed, digitised and ‘revitalised’. This study contributed to the development of methods of signal processing by ML methods.
Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces from Universit辰t Salzburg
]]> 9 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova10dec2021compressed-240614130448-836e15c2-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Studium oder Ausbildung? Die Auswahl der Jugendlichen heute in Deutschland https://de.slideshare.net/slideshow/studium-oder-ausbildung-die-auswahl-der-jugendlichen-heute-in-deutschland/269684413 presentationpolinaausbildung-240614114828-52149821
This presentation deas with the problem of careeer building and perspectives in germany. The problem discussed is that today's working life is complex and complicated which makes it difficult to find a way in working life. The main question discussed is on existing dilemma for young people today - careers or training? The reviewed statistics revealed existing variety of careers in Germany. Sources of information of finding the possibilities for about possible careers are discussed. Besides, factors the that influence career choice are discussed (e.g., personal interests, talents, well-paid jobs, advice from parents/friends/teachers, advertising). The statistics on the career choices of girls and boys in Germany is compared. Quantitative importance of vocational training is illustrated in the statitstical tables (reported statistics for 15-year-old German young people). The conclusion includes questions on what profession do students want to work, which vocational training they prefer, e.g., social norms and young people's expectations, and how they build career path with the goal of a constant development.]]>
This presentation deas with the problem of careeer building and perspectives in germany. The problem discussed is that today's working life is complex and complicated which makes it difficult to find a way in working life. The main question discussed is on existing dilemma for young people today - careers or training? The reviewed statistics revealed existing variety of careers in Germany. Sources of information of finding the possibilities for about possible careers are discussed. Besides, factors the that influence career choice are discussed (e.g., personal interests, talents, well-paid jobs, advice from parents/friends/teachers, advertising). The statistics on the career choices of girls and boys in Germany is compared. Quantitative importance of vocational training is illustrated in the statitstical tables (reported statistics for 15-year-old German young people). The conclusion includes questions on what profession do students want to work, which vocational training they prefer, e.g., social norms and young people's expectations, and how they build career path with the goal of a constant development.]]> Fri, 14 Jun 2024 11:48:28 GMT https://de.slideshare.net/slideshow/studium-oder-ausbildung-die-auswahl-der-jugendlichen-heute-in-deutschland/269684413 PolinaLemenkova@slideshare.net(PolinaLemenkova) Studium oder Ausbildung? Die Auswahl der Jugendlichen heute in Deutschland PolinaLemenkova This presentation deas with the problem of careeer building and perspectives in germany. The problem discussed is that today's working life is complex and complicated which makes it difficult to find a way in working life. The main question discussed is on existing dilemma for young people today - careers or training? The reviewed statistics revealed existing variety of careers in Germany. Sources of information of finding the possibilities for about possible careers are discussed. Besides, factors the that influence career choice are discussed (e.g., personal interests, talents, well-paid jobs, advice from parents/friends/teachers, advertising). The statistics on the career choices of girls and boys in Germany is compared. Quantitative importance of vocational training is illustrated in the statitstical tables (reported statistics for 15-year-old German young people). The conclusion includes questions on what profession do students want to work, which vocational training they prefer, e.g., social norms and young people's expectations, and how they build career path with the goal of a constant development. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationpolinaausbildung-240614114828-52149821-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation deas with the problem of careeer building and perspectives in germany. The problem discussed is that today&#39;s working life is complex and complicated which makes it difficult to find a way in working life. The main question discussed is on existing dilemma for young people today - careers or training? The reviewed statistics revealed existing variety of careers in Germany. Sources of information of finding the possibilities for about possible careers are discussed. Besides, factors the that influence career choice are discussed (e.g., personal interests, talents, well-paid jobs, advice from parents/friends/teachers, advertising). The statistics on the career choices of girls and boys in Germany is compared. Quantitative importance of vocational training is illustrated in the statitstical tables (reported statistics for 15-year-old German young people). The conclusion includes questions on what profession do students want to work, which vocational training they prefer, e.g., social norms and young people&#39;s expectations, and how they build career path with the goal of a constant development.
from Universit辰t Salzburg
]]> 11 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationpolinaausbildung-240614114828-52149821-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Tourisme en Belgique: Tourism in Belgium https://fr.slideshare.net/slideshow/tourisme-en-belgique-tourism-in-belgium/269682417 presentationpolina06122022-240614094106-59741bf3
This presentation describes current state of tourism development in Belgium. ]]>
This presentation describes current state of tourism development in Belgium. ]]> Fri, 14 Jun 2024 09:41:06 GMT https://fr.slideshare.net/slideshow/tourisme-en-belgique-tourism-in-belgium/269682417 PolinaLemenkova@slideshare.net(PolinaLemenkova) Tourisme en Belgique: Tourism in Belgium PolinaLemenkova This presentation describes current state of tourism development in Belgium. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationpolina06122022-240614094106-59741bf3-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation describes current state of tourism development in Belgium.
from Universit辰t Salzburg
]]> 26 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationpolina06122022-240614094106-59741bf3-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images /slideshow/mapping-landscapes-of-africa-using-remote-sensing-data-detecting-spatio-temporal-environmental-dynamics-from-the-satellite-images/269671004 presentation13062024polinacompressed-240613184455-5185707a
This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting]]>
This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting]]> Thu, 13 Jun 2024 18:44:55 GMT /slideshow/mapping-landscapes-of-africa-using-remote-sensing-data-detecting-spatio-temporal-environmental-dynamics-from-the-satellite-images/269671004 PolinaLemenkova@slideshare.net(PolinaLemenkova) Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images PolinaLemenkova This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation13062024polinacompressed-240613184455-5185707a-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting
Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images from Universit辰t Salzburg
]]> 52 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentation13062024polinacompressed-240613184455-5185707a-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean /slideshow/accurate-and-rapid-big-spatial-data-processing-by-scripting-cartographic-algorithms-advanced-seafloor-mapping-of-the-deepsea-trenches-along-the-margins-of-the-pacific-ocean/248522498 agulemenkova-210525095812
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean]]>
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean]]> Tue, 25 May 2021 09:58:12 GMT /slideshow/accurate-and-rapid-big-spatial-data-processing-by-scripting-cartographic-algorithms-advanced-seafloor-mapping-of-the-deepsea-trenches-along-the-margins-of-the-pacific-ocean/248522498 PolinaLemenkova@slideshare.net(PolinaLemenkova) Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean PolinaLemenkova Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agulemenkova-210525095812-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean from Universit辰t Salzburg
]]> 33 0 https://cdn.slidesharecdn.com/ss_thumbnails/agulemenkova-210525095812-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Presentation lemenkova /slideshow/presentation-lemenkova/248490470 presentationlemenkova-210524113321
Morphostructure features of the deep-sea trenches in the Pacific Ocean: the problem of their origin]]>
Morphostructure features of the deep-sea trenches in the Pacific Ocean: the problem of their origin]]> Mon, 24 May 2021 11:33:21 GMT /slideshow/presentation-lemenkova/248490470 PolinaLemenkova@slideshare.net(PolinaLemenkova) Presentation lemenkova PolinaLemenkova Morphostructure features of the deep-sea trenches in the Pacific Ocean: the problem of their origin <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova-210524113321-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Morphostructure features of the deep-sea trenches in the Pacific Ocean: the problem of their origin
Presentation lemenkova from Universit辰t Salzburg
]]> 38 0 https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova-210524113321-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Risks of Cryogenic Landslide Hazards and Their Impact on Ecosystems in Cold Environments /slideshow/risks-of-cryogenic-landslide-hazards-and-their-impact-on-ecosystems-in-cold-environments/205945600 lemenkova-patras-191215111444
Research focuses on monitoring landscapes downgrading in specific conditions of Arctic ecosystems with cold climate conditions (marshes, permafrost, high humidity and moisture). Specific case study: cryogenic landslides typical for cold environments with permafrost. Area: Yamal Peninsula. Aim: analysis of the environmental changes caused by cryogenic landslides in northern land- scapes affecting sensitive Arctic ecosystems. Thaw of the permafrost layer causes destruction of the ground soil layer and activates cryogenic landslide processes. After disaster, vegetation coverage needs a long time to recover, due to the sensitivity of the specific northern environment, and land cover types change. ILWIS GIS was used to process 2 satellite images Landsat TM taken at 1988 and 2011, to assess spatiotemporal changes in the land cover types. Research shown ILWIS GIS based spatial analysis for environmental mapping.]]>
Research focuses on monitoring landscapes downgrading in specific conditions of Arctic ecosystems with cold climate conditions (marshes, permafrost, high humidity and moisture). Specific case study: cryogenic landslides typical for cold environments with permafrost. Area: Yamal Peninsula. Aim: analysis of the environmental changes caused by cryogenic landslides in northern land- scapes affecting sensitive Arctic ecosystems. Thaw of the permafrost layer causes destruction of the ground soil layer and activates cryogenic landslide processes. After disaster, vegetation coverage needs a long time to recover, due to the sensitivity of the specific northern environment, and land cover types change. ILWIS GIS was used to process 2 satellite images Landsat TM taken at 1988 and 2011, to assess spatiotemporal changes in the land cover types. Research shown ILWIS GIS based spatial analysis for environmental mapping.]]> Sun, 15 Dec 2019 11:14:44 GMT /slideshow/risks-of-cryogenic-landslide-hazards-and-their-impact-on-ecosystems-in-cold-environments/205945600 PolinaLemenkova@slideshare.net(PolinaLemenkova) Risks of Cryogenic Landslide Hazards and Their Impact on Ecosystems in Cold Environments PolinaLemenkova Research focuses on monitoring landscapes downgrading in specific conditions of Arctic ecosystems with cold climate conditions (marshes, permafrost, high humidity and moisture). Specific case study: cryogenic landslides typical for cold environments with permafrost. Area: Yamal Peninsula. Aim: analysis of the environmental changes caused by cryogenic landslides in northern land- scapes affecting sensitive Arctic ecosystems. Thaw of the permafrost layer causes destruction of the ground soil layer and activates cryogenic landslide processes. After disaster, vegetation coverage needs a long time to recover, due to the sensitivity of the specific northern environment, and land cover types change. ILWIS GIS was used to process 2 satellite images Landsat TM taken at 1988 and 2011, to assess spatiotemporal changes in the land cover types. Research shown ILWIS GIS based spatial analysis for environmental mapping. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-patras-191215111444-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Research focuses on monitoring landscapes downgrading in specific conditions of Arctic ecosystems with cold climate conditions (marshes, permafrost, high humidity and moisture). Specific case study: cryogenic landslides typical for cold environments with permafrost. Area: Yamal Peninsula. Aim: analysis of the environmental changes caused by cryogenic landslides in northern land- scapes affecting sensitive Arctic ecosystems. Thaw of the permafrost layer causes destruction of the ground soil layer and activates cryogenic landslide processes. After disaster, vegetation coverage needs a long time to recover, due to the sensitivity of the specific northern environment, and land cover types change. ILWIS GIS was used to process 2 satellite images Landsat TM taken at 1988 and 2011, to assess spatiotemporal changes in the land cover types. Research shown ILWIS GIS based spatial analysis for environmental mapping.
Risks of Cryogenic Landslide Hazards and Their Impact on Ecosystems in Cold Environments from Universit辰t Salzburg
]]> 77 5 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-patras-191215111444-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Bringing Geospatial Analysis to the Social Studies: an Assessment of the City Sprawl in China /slideshow/bringing-geospatial-analysis-to-the-social-studies-an-assessment-of-the-city-sprawl-in-china/205735172 lemenkova-thessaloniki-191214085326
Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.]]>
Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.]]> Sat, 14 Dec 2019 08:53:26 GMT /slideshow/bringing-geospatial-analysis-to-the-social-studies-an-assessment-of-the-city-sprawl-in-china/205735172 PolinaLemenkova@slideshare.net(PolinaLemenkova) Bringing Geospatial Analysis to the Social Studies: an Assessment of the City Sprawl in China PolinaLemenkova Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-thessaloniki-191214085326-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population &gt;2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.
Bringing Geospatial Analysis to the Social Studies: an Assessment of the City Sprawl in China from Universit辰t Salzburg
]]> 45 4 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-thessaloniki-191214085326-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Detection of Vegetation Coverage in Urban Agglomeration of Brussels by NDVI Indicator Using eCognition Software and Remote Sensing Measurements /slideshow/detection-of-vegetation-coverage-in-urban-agglomeration-of-brussels-by-ndvi-indicator-using-ecognition-software-and-remote-sensing-measurements/205573757 lemenkova-armenia-191214013828
Detection of vegetation coverage in urban agglomeration of Brussels by NDVI indicator using eCognition software and remote sensing measurements Lemenkova Polina Introduction The study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike in some other European cities, where the most evident problem is urbanization and expansion of the city margins to the suburbia, the urban structure Brussels is the intensification of the buildings density in the city centre and the existing dwelling districts. Thus, the city structure tends to become more intense and dense, due to the process of filling the empty spaces in the urban patterns and high level housing. Another example of urban processes in Brussels is reorganisation of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital city. The lack of the green spaces may cause ecological instability and increase atmospheric pollution. For studies of the specific problems of the Brussels city the remote sensing data (raster image) was used together with NDVI function, in order to detect areas covered by city parks. Acknowledgement: Current work has been supported by Bourse d'excellence, Service de Bourse d' ́ etude, Wallonie-Bruxelles International for research stay of Polina Lemenkova at l'Université libre de Bruxelles.]]>
Detection of vegetation coverage in urban agglomeration of Brussels by NDVI indicator using eCognition software and remote sensing measurements Lemenkova Polina Introduction The study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike in some other European cities, where the most evident problem is urbanization and expansion of the city margins to the suburbia, the urban structure Brussels is the intensification of the buildings density in the city centre and the existing dwelling districts. Thus, the city structure tends to become more intense and dense, due to the process of filling the empty spaces in the urban patterns and high level housing. Another example of urban processes in Brussels is reorganisation of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital city. The lack of the green spaces may cause ecological instability and increase atmospheric pollution. For studies of the specific problems of the Brussels city the remote sensing data (raster image) was used together with NDVI function, in order to detect areas covered by city parks. Acknowledgement: Current work has been supported by Bourse d'excellence, Service de Bourse d' ́ etude, Wallonie-Bruxelles International for research stay of Polina Lemenkova at l'Université libre de Bruxelles.]]> Sat, 14 Dec 2019 01:38:28 GMT /slideshow/detection-of-vegetation-coverage-in-urban-agglomeration-of-brussels-by-ndvi-indicator-using-ecognition-software-and-remote-sensing-measurements/205573757 PolinaLemenkova@slideshare.net(PolinaLemenkova) Detection of Vegetation Coverage in Urban Agglomeration of Brussels by NDVI Indicator Using eCognition Software and Remote Sensing Measurements PolinaLemenkova Detection of vegetation coverage in urban agglomeration of Brussels by NDVI indicator using eCognition software and remote sensing measurements Lemenkova Polina Introduction The study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike in some other European cities, where the most evident problem is urbanization and expansion of the city margins to the suburbia, the urban structure Brussels is the intensification of the buildings density in the city centre and the existing dwelling districts. Thus, the city structure tends to become more intense and dense, due to the process of filling the empty spaces in the urban patterns and high level housing. Another example of urban processes in Brussels is reorganisation of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital city. The lack of the green spaces may cause ecological instability and increase atmospheric pollution. For studies of the specific problems of the Brussels city the remote sensing data (raster image) was used together with NDVI function, in order to detect areas covered by city parks. Acknowledgement: Current work has been supported by Bourse d'excellence, Service de Bourse d' ́ etude, Wallonie-Bruxelles International for research stay of Polina Lemenkova at l'Université libre de Bruxelles. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-armenia-191214013828-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Detection of vegetation coverage in urban agglomeration of Brussels by NDVI indicator using eCognition software and remote sensing measurements Lemenkova Polina Introduction The study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike in some other European cities, where the most evident problem is urbanization and expansion of the city margins to the suburbia, the urban structure Brussels is the intensification of the buildings density in the city centre and the existing dwelling districts. Thus, the city structure tends to become more intense and dense, due to the process of filling the empty spaces in the urban patterns and high level housing. Another example of urban processes in Brussels is reorganisation of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital city. The lack of the green spaces may cause ecological instability and increase atmospheric pollution. For studies of the specific problems of the Brussels city the remote sensing data (raster image) was used together with NDVI function, in order to detect areas covered by city parks. Acknowledgement: Current work has been supported by Bourse d&#39;excellence, Service de Bourse d&#39; ́ etude, Wallonie-Bruxelles International for research stay of Polina Lemenkova at l&#39;Université libre de Bruxelles.
Detection of Vegetation Coverage in Urban Agglomeration of Brussels by NDVI Indicator Using eCognition Software and Remote Sensing Measurements from Universit辰t Salzburg
]]> 44 1 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-armenia-191214013828-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Investigation of the Lake Victoria Region (Africa: Tanzania, Kenya and Uganda) /slideshow/investigation-of-the-lake-victoria-region-africa-tanzania-kenya-and-uganda/205304506 lemenkovaposterlakevictoria-191213084115
This poster is a student assignment for a course 'GISA 02 GIS: Geographical Information Systems - Advanced Course 0701', a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations). ]]>
This poster is a student assignment for a course 'GISA 02 GIS: Geographical Information Systems - Advanced Course 0701', a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations). ]]> Fri, 13 Dec 2019 08:41:15 GMT /slideshow/investigation-of-the-lake-victoria-region-africa-tanzania-kenya-and-uganda/205304506 PolinaLemenkova@slideshare.net(PolinaLemenkova) Investigation of the Lake Victoria Region (Africa: Tanzania, Kenya and Uganda) PolinaLemenkova This poster is a student assignment for a course 'GISA 02 GIS: Geographical Information Systems - Advanced Course 0701', a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaposterlakevictoria-191213084115-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This poster is a student assignment for a course &#39;GISA 02 GIS: Geographical Information Systems - Advanced Course 0701&#39;, a part of the MSc studies. It presents an ArcGIS based spatial analysis of the Victoria Lake region including environmental, biological, social and economic characteristics of the region. The methodology includes data organizing and management in ArcGIS 9.3. Operations and technique: ArcGIS Spatial Analyst. Project architecture: ArcCatalog. Spatial referencing and re-projection: ArcToolbox. Data include DEMs: elevations (USGS). 2 tiles of the USGS DEM, Land cover data (raster), Population data: UNEP, ArcGIS vector.shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Data preprocessing include following data preparation. Initial vector data: UNEP .shp. Spatial reference properties: Africa Albers Equal Area Conic projection, standard parallels 20 and -23, central meridian 25 and Datum WGS-84, Projection GEOGRAPHIC, Spheroid CLARKE1866. Data conversion from ASCII text data format to raster using ArcToolbox / Conversion Tools / ASCII to Raster (Climate precipitation data). Data were projected, processed and several layer formatting and overlays were created. Mapping was created using ArcMap. Victoria Lake has unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface. Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations).
Investigation of the Lake Victoria Region (Africa: Tanzania, Kenya and Uganda) from Universit辰t Salzburg
]]> 102 3 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaposterlakevictoria-191213084115-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Interpretation of Landscape Values, Typology and Quality Using Methods of Spatial Metrics for Ecological Planning /slideshow/interpretation-of-landscape-values-typology-and-quality-using-methods-of-spatial-metrics-for-ecological-planning/205150872 klaucoetalposterrtulatvia-191213012218
The main result of this work consists in determined ecological significant areas of habitats that are under protection´s system of Natura 2000 Sites. The patches quantification of habitats is the partial result that influences process of determination of ecological significance. The interpretative process examines land cover patches by the set of landscape metrics for the area, size, density and shape (NP, PD, MPS, PSSD and MSI). The output values could express a spatial processes in the landscape, such as perforation, dissection, fragmentation, shrinkage or attrition. The final ecological significance of the study area-Sitno Natura 2000 site-is at degree 3, what means that the area is represented by moderately significant land cover patches-habitats. It indicates the same value as the one at the initial level. According to the value of the ecological significance, the study area has been diversified into three zones, where each one indicates specific level of conservation. The zones and the final degree of the ecological significance of habitats are retroactively compared to historical and cultural human development that started in this area as early as in 1st century BC. Theoretically, such a long period of intense human impacts on the local environment should completely destroy natural environment. Nevertheless, this area demonstrates rather good natural ecosystems conditions and well functioning ecological processes within the habitats. The human impact is now observed only in small range of size not more than 1,50% from total area of Sitno Natura 2000 Site. It can be explained, first, by low population density within the study area comparing to other EU areas, secondly, by accurate usage of the living area by the local population in general, and thirdly, by high resilience of the elements of landscapes towards any human impacts.]]>
The main result of this work consists in determined ecological significant areas of habitats that are under protection´s system of Natura 2000 Sites. The patches quantification of habitats is the partial result that influences process of determination of ecological significance. The interpretative process examines land cover patches by the set of landscape metrics for the area, size, density and shape (NP, PD, MPS, PSSD and MSI). The output values could express a spatial processes in the landscape, such as perforation, dissection, fragmentation, shrinkage or attrition. The final ecological significance of the study area-Sitno Natura 2000 site-is at degree 3, what means that the area is represented by moderately significant land cover patches-habitats. It indicates the same value as the one at the initial level. According to the value of the ecological significance, the study area has been diversified into three zones, where each one indicates specific level of conservation. The zones and the final degree of the ecological significance of habitats are retroactively compared to historical and cultural human development that started in this area as early as in 1st century BC. Theoretically, such a long period of intense human impacts on the local environment should completely destroy natural environment. Nevertheless, this area demonstrates rather good natural ecosystems conditions and well functioning ecological processes within the habitats. The human impact is now observed only in small range of size not more than 1,50% from total area of Sitno Natura 2000 Site. It can be explained, first, by low population density within the study area comparing to other EU areas, secondly, by accurate usage of the living area by the local population in general, and thirdly, by high resilience of the elements of landscapes towards any human impacts.]]> Fri, 13 Dec 2019 01:22:18 GMT /slideshow/interpretation-of-landscape-values-typology-and-quality-using-methods-of-spatial-metrics-for-ecological-planning/205150872 PolinaLemenkova@slideshare.net(PolinaLemenkova) Interpretation of Landscape Values, Typology and Quality Using Methods of Spatial Metrics for Ecological Planning PolinaLemenkova The main result of this work consists in determined ecological significant areas of habitats that are under protection´s system of Natura 2000 Sites. The patches quantification of habitats is the partial result that influences process of determination of ecological significance. The interpretative process examines land cover patches by the set of landscape metrics for the area, size, density and shape (NP, PD, MPS, PSSD and MSI). The output values could express a spatial processes in the landscape, such as perforation, dissection, fragmentation, shrinkage or attrition. The final ecological significance of the study area-Sitno Natura 2000 site-is at degree 3, what means that the area is represented by moderately significant land cover patches-habitats. It indicates the same value as the one at the initial level. According to the value of the ecological significance, the study area has been diversified into three zones, where each one indicates specific level of conservation. The zones and the final degree of the ecological significance of habitats are retroactively compared to historical and cultural human development that started in this area as early as in 1st century BC. Theoretically, such a long period of intense human impacts on the local environment should completely destroy natural environment. Nevertheless, this area demonstrates rather good natural ecosystems conditions and well functioning ecological processes within the habitats. The human impact is now observed only in small range of size not more than 1,50% from total area of Sitno Natura 2000 Site. It can be explained, first, by low population density within the study area comparing to other EU areas, secondly, by accurate usage of the living area by the local population in general, and thirdly, by high resilience of the elements of landscapes towards any human impacts. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/klaucoetalposterrtulatvia-191213012218-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The main result of this work consists in determined ecological significant areas of habitats that are under protection´s system of Natura 2000 Sites. The patches quantification of habitats is the partial result that influences process of determination of ecological significance. The interpretative process examines land cover patches by the set of landscape metrics for the area, size, density and shape (NP, PD, MPS, PSSD and MSI). The output values could express a spatial processes in the landscape, such as perforation, dissection, fragmentation, shrinkage or attrition. The final ecological significance of the study area-Sitno Natura 2000 site-is at degree 3, what means that the area is represented by moderately significant land cover patches-habitats. It indicates the same value as the one at the initial level. According to the value of the ecological significance, the study area has been diversified into three zones, where each one indicates specific level of conservation. The zones and the final degree of the ecological significance of habitats are retroactively compared to historical and cultural human development that started in this area as early as in 1st century BC. Theoretically, such a long period of intense human impacts on the local environment should completely destroy natural environment. Nevertheless, this area demonstrates rather good natural ecosystems conditions and well functioning ecological processes within the habitats. The human impact is now observed only in small range of size not more than 1,50% from total area of Sitno Natura 2000 Site. It can be explained, first, by low population density within the study area comparing to other EU areas, secondly, by accurate usage of the living area by the local population in general, and thirdly, by high resilience of the elements of landscapes towards any human impacts.
Interpretation of Landscape Values, Typology and Quality Using Methods of Spatial Metrics for Ecological Planning from Universit辰t Salzburg
]]> 51 2 https://cdn.slidesharecdn.com/ss_thumbnails/klaucoetalposterrtulatvia-191213012218-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mapping Land Cover Changes Using Landsat TM: a Case Study of Yamal Ecosystems, Arctic Russia /slideshow/mapping-land-cover-changes-using-landsat-tm-a-case-study-of-yamal-ecosystems-arctic-russia/205146494 lemenkovaetalposteryamal-191213010939
This poster presents image processing by ILWIS GIS. It demonstrates changes in land cover types in tundra landscapes (Yamal) since 1988 to 2011. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Arctic, Bovanenkovo region. The poster demonstrates techniques of the remote sensing data processing by ILWIS GIS.]]>
This poster presents image processing by ILWIS GIS. It demonstrates changes in land cover types in tundra landscapes (Yamal) since 1988 to 2011. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Arctic, Bovanenkovo region. The poster demonstrates techniques of the remote sensing data processing by ILWIS GIS.]]> Fri, 13 Dec 2019 01:09:38 GMT /slideshow/mapping-land-cover-changes-using-landsat-tm-a-case-study-of-yamal-ecosystems-arctic-russia/205146494 PolinaLemenkova@slideshare.net(PolinaLemenkova) Mapping Land Cover Changes Using Landsat TM: a Case Study of Yamal Ecosystems, Arctic Russia PolinaLemenkova This poster presents image processing by ILWIS GIS. It demonstrates changes in land cover types in tundra landscapes (Yamal) since 1988 to 2011. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Arctic, Bovanenkovo region. The poster demonstrates techniques of the remote sensing data processing by ILWIS GIS. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaetalposteryamal-191213010939-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This poster presents image processing by ILWIS GIS. It demonstrates changes in land cover types in tundra landscapes (Yamal) since 1988 to 2011. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Arctic, Bovanenkovo region. The poster demonstrates techniques of the remote sensing data processing by ILWIS GIS.
Mapping Land Cover Changes Using Landsat TM: a Case Study of Yamal Ecosystems, Arctic Russia from Universit辰t Salzburg
]]> 64 2 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaetalposteryamal-191213010939-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria /slideshow/economic-assessment-of-landslide-risk-for-the-waidhofen-ad-ybbs-region-alpine-foreland-lower-austria/204383418 lemenkovaetalposterybbs-191211101218
The research focuses on the monetary estimation of the possible losses caused by landslides. Estimation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing potential damage of landslides confusion arises due to these factors. 1. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. 2. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. 3. The term “landslide” is not differentiated between debris flows and shallow or rotational landslides. This is an important source for uncertainty, as movement characteristics of these landslides are different. 4. Confusing over different method approaches in the risk assessment may generate various results: difference in magnitude and occurrence of landslides, risk perception and vulnerability assessment. The estimation of landslide risk should be based on complex investigations. The data about landslide probability should be gained from monitoring programmes. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. The real-life situations may vary depending on the exact price of the individual object.]]>
The research focuses on the monetary estimation of the possible losses caused by landslides. Estimation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing potential damage of landslides confusion arises due to these factors. 1. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. 2. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. 3. The term “landslide” is not differentiated between debris flows and shallow or rotational landslides. This is an important source for uncertainty, as movement characteristics of these landslides are different. 4. Confusing over different method approaches in the risk assessment may generate various results: difference in magnitude and occurrence of landslides, risk perception and vulnerability assessment. The estimation of landslide risk should be based on complex investigations. The data about landslide probability should be gained from monitoring programmes. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. The real-life situations may vary depending on the exact price of the individual object.]]> Wed, 11 Dec 2019 10:12:18 GMT /slideshow/economic-assessment-of-landslide-risk-for-the-waidhofen-ad-ybbs-region-alpine-foreland-lower-austria/204383418 PolinaLemenkova@slideshare.net(PolinaLemenkova) Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria PolinaLemenkova The research focuses on the monetary estimation of the possible losses caused by landslides. Estimation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing potential damage of landslides confusion arises due to these factors. 1. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. 2. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. 3. The term “landslide” is not differentiated between debris flows and shallow or rotational landslides. This is an important source for uncertainty, as movement characteristics of these landslides are different. 4. Confusing over different method approaches in the risk assessment may generate various results: difference in magnitude and occurrence of landslides, risk perception and vulnerability assessment. The estimation of landslide risk should be based on complex investigations. The data about landslide probability should be gained from monitoring programmes. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. The real-life situations may vary depending on the exact price of the individual object. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaetalposterybbs-191211101218-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The research focuses on the monetary estimation of the possible losses caused by landslides. Estimation of the economic damages is performed using existing simplified methodologies. Calculations were based on real estate and market price of the elements at risk. While assessing potential damage of landslides confusion arises due to these factors. 1. First, the temporal probability of the landslides occurrence is highly difficult to assess: it can only be estimated based on the reliable and obtainable data. This includes historical data continuously reporting the occurrence of the landslides. 2. Secondly, difficulties arise by estimation of the indirect losses and partially damaged objects. The amount of the damages can be assessed based on elements vulnerability, which is very uncertain to estimate exactly. Thus, the vulnerability may differ depending on object location, individual characteristics and external factors. 3. The term “landslide” is not differentiated between debris flows and shallow or rotational landslides. This is an important source for uncertainty, as movement characteristics of these landslides are different. 4. Confusing over different method approaches in the risk assessment may generate various results: difference in magnitude and occurrence of landslides, risk perception and vulnerability assessment. The estimation of landslide risk should be based on complex investigations. The data about landslide probability should be gained from monitoring programmes. The elements at risk are defined based on spatial analysis and infrastructure inventory. The vulnerability estimation should include census data and social questionnaire. The real-life situations may vary depending on the exact price of the individual object.
Economic assessment of landslide risk for the Waidhofen a.d. Ybbs region, Alpine Foreland, Lower Austria from Universit辰t Salzburg
]]> 34 1 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaetalposterybbs-191211101218-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Quality assessment of data from CHRIS/PROBA /slideshow/quality-assessment-of-data-from-chrisproba/204369474 posterchris-191211091104
Current poster presents a student assignment for the CHRIS/PROBA image processing by ENVI GIS. Study Area: Thorney Island, Chichester harbour (UK): unique wetland environment, a place for rare bird colonies. Quality of CHRIS images is affected by two types of noises: vertical noise (vertical stripes; can be corrected by comparing values of neighbouring pixels) and horizontal noise (easy to detect and correct using the horizontal profile of each file. Correction of noises can be made through DIELMO 3D Methodology. PROBA (Project for On-Board Autonomy) and CHRIS (Compact High Resolution Imaging Spectrometer) image was taken with characteristics: 18 bands, 07/10/2004, 17m ground resolution. To obtain a good-quality natural-coloured image of wetlands a need: nadir-taken colour CHRIS image with bands combination of corresponding spectral channels was selected and processed. Comparing images taken at +55° dgr (47A2_41) and nadir images (479F_41) right Images taken at the nadir are of good quality, while those at different angles have defects: Images taken at +36° dgr (47A0_41), left and nadir images (479F_41) right. Images taken at +36° and-36° (CHRIS 47A0_41 and CHRIS 47A1_41) both have inverted direction. Several bands were tried, processed and visualized. Spectral bands assessed and visually compared. This is a student poster as a part of MSc studies, University of Southampton.]]>
Current poster presents a student assignment for the CHRIS/PROBA image processing by ENVI GIS. Study Area: Thorney Island, Chichester harbour (UK): unique wetland environment, a place for rare bird colonies. Quality of CHRIS images is affected by two types of noises: vertical noise (vertical stripes; can be corrected by comparing values of neighbouring pixels) and horizontal noise (easy to detect and correct using the horizontal profile of each file. Correction of noises can be made through DIELMO 3D Methodology. PROBA (Project for On-Board Autonomy) and CHRIS (Compact High Resolution Imaging Spectrometer) image was taken with characteristics: 18 bands, 07/10/2004, 17m ground resolution. To obtain a good-quality natural-coloured image of wetlands a need: nadir-taken colour CHRIS image with bands combination of corresponding spectral channels was selected and processed. Comparing images taken at +55° dgr (47A2_41) and nadir images (479F_41) right Images taken at the nadir are of good quality, while those at different angles have defects: Images taken at +36° dgr (47A0_41), left and nadir images (479F_41) right. Images taken at +36° and-36° (CHRIS 47A0_41 and CHRIS 47A1_41) both have inverted direction. Several bands were tried, processed and visualized. Spectral bands assessed and visually compared. This is a student poster as a part of MSc studies, University of Southampton.]]> Wed, 11 Dec 2019 09:11:04 GMT /slideshow/quality-assessment-of-data-from-chrisproba/204369474 PolinaLemenkova@slideshare.net(PolinaLemenkova) Quality assessment of data from CHRIS/PROBA PolinaLemenkova Current poster presents a student assignment for the CHRIS/PROBA image processing by ENVI GIS. Study Area: Thorney Island, Chichester harbour (UK): unique wetland environment, a place for rare bird colonies. Quality of CHRIS images is affected by two types of noises: vertical noise (vertical stripes; can be corrected by comparing values of neighbouring pixels) and horizontal noise (easy to detect and correct using the horizontal profile of each file. Correction of noises can be made through DIELMO 3D Methodology. PROBA (Project for On-Board Autonomy) and CHRIS (Compact High Resolution Imaging Spectrometer) image was taken with characteristics: 18 bands, 07/10/2004, 17m ground resolution. To obtain a good-quality natural-coloured image of wetlands a need: nadir-taken colour CHRIS image with bands combination of corresponding spectral channels was selected and processed. Comparing images taken at +55° dgr (47A2_41) and nadir images (479F_41) right Images taken at the nadir are of good quality, while those at different angles have defects: Images taken at +36° dgr (47A0_41), left and nadir images (479F_41) right. Images taken at +36° and-36° (CHRIS 47A0_41 and CHRIS 47A1_41) both have inverted direction. Several bands were tried, processed and visualized. Spectral bands assessed and visually compared. This is a student poster as a part of MSc studies, University of Southampton. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterchris-191211091104-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Current poster presents a student assignment for the CHRIS/PROBA image processing by ENVI GIS. Study Area: Thorney Island, Chichester harbour (UK): unique wetland environment, a place for rare bird colonies. Quality of CHRIS images is affected by two types of noises: vertical noise (vertical stripes; can be corrected by comparing values of neighbouring pixels) and horizontal noise (easy to detect and correct using the horizontal profile of each file. Correction of noises can be made through DIELMO 3D Methodology. PROBA (Project for On-Board Autonomy) and CHRIS (Compact High Resolution Imaging Spectrometer) image was taken with characteristics: 18 bands, 07/10/2004, 17m ground resolution. To obtain a good-quality natural-coloured image of wetlands a need: nadir-taken colour CHRIS image with bands combination of corresponding spectral channels was selected and processed. Comparing images taken at +55° dgr (47A2_41) and nadir images (479F_41) right Images taken at the nadir are of good quality, while those at different angles have defects: Images taken at +36° dgr (47A0_41), left and nadir images (479F_41) right. Images taken at +36° and-36° (CHRIS 47A0_41 and CHRIS 47A1_41) both have inverted direction. Several bands were tried, processed and visualized. Spectral bands assessed and visually compared. This is a student poster as a part of MSc studies, University of Southampton.
Quality assessment of data from CHRIS/PROBA from Universit辰t Salzburg
]]> 25 1 https://cdn.slidesharecdn.com/ss_thumbnails/posterchris-191211091104-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Conservation Area Designation in the Andes /slideshow/conservation-area-designation-in-the-andes/204054030 lemenkova-andes-191210115442
Current poster presents a student assignment on Course: 'GEOG6038 Calibration and Validation of Earth Observation Data'. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009.]]>
Current poster presents a student assignment on Course: 'GEOG6038 Calibration and Validation of Earth Observation Data'. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009.]]> Tue, 10 Dec 2019 11:54:42 GMT /slideshow/conservation-area-designation-in-the-andes/204054030 PolinaLemenkova@slideshare.net(PolinaLemenkova) Conservation Area Designation in the Andes PolinaLemenkova Current poster presents a student assignment on Course: 'GEOG6038 Calibration and Validation of Earth Observation Data'. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-andes-191210115442-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Current poster presents a student assignment on Course: &#39;GEOG6038 Calibration and Validation of Earth Observation Data&#39;. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009.
Conservation Area Designation in the Andes from Universit辰t Salzburg
]]> 248 2 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-andes-191210115442-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Seagrass mapping and monitoring along the coast of Crete, Greece /slideshow/seagrass-mapping-and-monitoring-along-the-coast-of-crete-greece/201848272 lemenkova-rostock-191205102924
Job interview for the Research Training Group (RTG) Baltic TRANSCOAST. topic ’B1: Impact of nutrient emissions from land on communities of macrophytes’. This research is presented at the job interview in the University of Rostock. Originally based on author's MSc thesis (2009-2011) summarizing research in marine observations using remote sensing and GIS methods. Study object is seagrass Posidonia oceanic (P. oceanica) along the coast of Crete, Greece. The most important facts about seagrass: endemic Mediterranean seagrass, P. oceanica is a main species in marine coastal environment of Greece. P. oceanica is the largest, the most widespread, homogeneous, dense “mattes” forming meadows between 5-40 m in Mediterranean Sea. Seagrass is a component of coastal ecosystems of high importance for the marine life, playing important functions in the marine environment. Seagrasses are subjects to external factors and therefore have environmental vulnerability. The study area is located in General research area: Island of Crete, Greece. Seagrass sampling will be performed at three stations at a depth of 6-7 m: Heraklio, Agia Pelagia, Xerokampos, Crete Island, Greece. The general research objectives of the MSc research includes GIS and environmental analysis: 1) Mapping the extent of the spatial distribution of seagrass P. oceanica along the northern coast of Crete; 2) Monitoring environmental changes in seagrass meadows in the selected fieldwork sites (Agia Pelagia, Xerokampos) over the 10-year period (2000-2010). There are various multi-sources data proposed for using in spatial analysis. data of the previous measurements received during the last year fieldwork, to analyze whether P.oceanica is spectrally distinct from other sea floor types, using differences in the spectral signatures on the graphs in a WASI, the Water Color Simulator software. Other data include satellite images from the open sources (Landsat TM), aerial images, Google Earth; underwater videographic measurements of 3 cameras Olympus ST 8000 made during the ship route (20 total in the selected areas of the research places) resulting in series of consequent images, covering area under the boat path; in-situ measurements of the seagrass in selected spots, using measurement frame and other devices for marine biological research for the validation of the results. Arc GIS vector layers of Crete island and surroundings (.shp files). Hypothesis testis is performed by ANOVA, SPSS. The results of WASI spectral analysis illustrating graphs of the spectral reflectance of different sea floor types (sand, P.oceanica, rocky, etc) at various depths (0.5-4 m), based on the results of 20. Precise, correct and up-to-date information about the seagrass distribution over the coasts is necessary for the sustainable conservation of marine environment.]]>
Job interview for the Research Training Group (RTG) Baltic TRANSCOAST. topic ’B1: Impact of nutrient emissions from land on communities of macrophytes’. This research is presented at the job interview in the University of Rostock. Originally based on author's MSc thesis (2009-2011) summarizing research in marine observations using remote sensing and GIS methods. Study object is seagrass Posidonia oceanic (P. oceanica) along the coast of Crete, Greece. The most important facts about seagrass: endemic Mediterranean seagrass, P. oceanica is a main species in marine coastal environment of Greece. P. oceanica is the largest, the most widespread, homogeneous, dense “mattes” forming meadows between 5-40 m in Mediterranean Sea. Seagrass is a component of coastal ecosystems of high importance for the marine life, playing important functions in the marine environment. Seagrasses are subjects to external factors and therefore have environmental vulnerability. The study area is located in General research area: Island of Crete, Greece. Seagrass sampling will be performed at three stations at a depth of 6-7 m: Heraklio, Agia Pelagia, Xerokampos, Crete Island, Greece. The general research objectives of the MSc research includes GIS and environmental analysis: 1) Mapping the extent of the spatial distribution of seagrass P. oceanica along the northern coast of Crete; 2) Monitoring environmental changes in seagrass meadows in the selected fieldwork sites (Agia Pelagia, Xerokampos) over the 10-year period (2000-2010). There are various multi-sources data proposed for using in spatial analysis. data of the previous measurements received during the last year fieldwork, to analyze whether P.oceanica is spectrally distinct from other sea floor types, using differences in the spectral signatures on the graphs in a WASI, the Water Color Simulator software. Other data include satellite images from the open sources (Landsat TM), aerial images, Google Earth; underwater videographic measurements of 3 cameras Olympus ST 8000 made during the ship route (20 total in the selected areas of the research places) resulting in series of consequent images, covering area under the boat path; in-situ measurements of the seagrass in selected spots, using measurement frame and other devices for marine biological research for the validation of the results. Arc GIS vector layers of Crete island and surroundings (.shp files). Hypothesis testis is performed by ANOVA, SPSS. The results of WASI spectral analysis illustrating graphs of the spectral reflectance of different sea floor types (sand, P.oceanica, rocky, etc) at various depths (0.5-4 m), based on the results of 20. Precise, correct and up-to-date information about the seagrass distribution over the coasts is necessary for the sustainable conservation of marine environment.]]> Thu, 05 Dec 2019 10:29:24 GMT /slideshow/seagrass-mapping-and-monitoring-along-the-coast-of-crete-greece/201848272 PolinaLemenkova@slideshare.net(PolinaLemenkova) Seagrass mapping and monitoring along the coast of Crete, Greece PolinaLemenkova Job interview for the Research Training Group (RTG) Baltic TRANSCOAST. topic ’B1: Impact of nutrient emissions from land on communities of macrophytes’. This research is presented at the job interview in the University of Rostock. Originally based on author's MSc thesis (2009-2011) summarizing research in marine observations using remote sensing and GIS methods. Study object is seagrass Posidonia oceanic (P. oceanica) along the coast of Crete, Greece. The most important facts about seagrass: endemic Mediterranean seagrass, P. oceanica is a main species in marine coastal environment of Greece. P. oceanica is the largest, the most widespread, homogeneous, dense “mattes” forming meadows between 5-40 m in Mediterranean Sea. Seagrass is a component of coastal ecosystems of high importance for the marine life, playing important functions in the marine environment. Seagrasses are subjects to external factors and therefore have environmental vulnerability. The study area is located in General research area: Island of Crete, Greece. Seagrass sampling will be performed at three stations at a depth of 6-7 m: Heraklio, Agia Pelagia, Xerokampos, Crete Island, Greece. The general research objectives of the MSc research includes GIS and environmental analysis: 1) Mapping the extent of the spatial distribution of seagrass P. oceanica along the northern coast of Crete; 2) Monitoring environmental changes in seagrass meadows in the selected fieldwork sites (Agia Pelagia, Xerokampos) over the 10-year period (2000-2010). There are various multi-sources data proposed for using in spatial analysis. data of the previous measurements received during the last year fieldwork, to analyze whether P.oceanica is spectrally distinct from other sea floor types, using differences in the spectral signatures on the graphs in a WASI, the Water Color Simulator software. Other data include satellite images from the open sources (Landsat TM), aerial images, Google Earth; underwater videographic measurements of 3 cameras Olympus ST 8000 made during the ship route (20 total in the selected areas of the research places) resulting in series of consequent images, covering area under the boat path; in-situ measurements of the seagrass in selected spots, using measurement frame and other devices for marine biological research for the validation of the results. Arc GIS vector layers of Crete island and surroundings (.shp files). Hypothesis testis is performed by ANOVA, SPSS. The results of WASI spectral analysis illustrating graphs of the spectral reflectance of different sea floor types (sand, P.oceanica, rocky, etc) at various depths (0.5-4 m), based on the results of 20. Precise, correct and up-to-date information about the seagrass distribution over the coasts is necessary for the sustainable conservation of marine environment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-rostock-191205102924-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Job interview for the Research Training Group (RTG) Baltic TRANSCOAST. topic ’B1: Impact of nutrient emissions from land on communities of macrophytes’. This research is presented at the job interview in the University of Rostock. Originally based on author&#39;s MSc thesis (2009-2011) summarizing research in marine observations using remote sensing and GIS methods. Study object is seagrass Posidonia oceanic (P. oceanica) along the coast of Crete, Greece. The most important facts about seagrass: endemic Mediterranean seagrass, P. oceanica is a main species in marine coastal environment of Greece. P. oceanica is the largest, the most widespread, homogeneous, dense “mattes” forming meadows between 5-40 m in Mediterranean Sea. Seagrass is a component of coastal ecosystems of high importance for the marine life, playing important functions in the marine environment. Seagrasses are subjects to external factors and therefore have environmental vulnerability. The study area is located in General research area: Island of Crete, Greece. Seagrass sampling will be performed at three stations at a depth of 6-7 m: Heraklio, Agia Pelagia, Xerokampos, Crete Island, Greece. The general research objectives of the MSc research includes GIS and environmental analysis: 1) Mapping the extent of the spatial distribution of seagrass P. oceanica along the northern coast of Crete; 2) Monitoring environmental changes in seagrass meadows in the selected fieldwork sites (Agia Pelagia, Xerokampos) over the 10-year period (2000-2010). There are various multi-sources data proposed for using in spatial analysis. data of the previous measurements received during the last year fieldwork, to analyze whether P.oceanica is spectrally distinct from other sea floor types, using differences in the spectral signatures on the graphs in a WASI, the Water Color Simulator software. Other data include satellite images from the open sources (Landsat TM), aerial images, Google Earth; underwater videographic measurements of 3 cameras Olympus ST 8000 made during the ship route (20 total in the selected areas of the research places) resulting in series of consequent images, covering area under the boat path; in-situ measurements of the seagrass in selected spots, using measurement frame and other devices for marine biological research for the validation of the results. Arc GIS vector layers of Crete island and surroundings (.shp files). Hypothesis testis is performed by ANOVA, SPSS. The results of WASI spectral analysis illustrating graphs of the spectral reflectance of different sea floor types (sand, P.oceanica, rocky, etc) at various depths (0.5-4 m), based on the results of 20. Precise, correct and up-to-date information about the seagrass distribution over the coasts is necessary for the sustainable conservation of marine environment.
Seagrass mapping and monitoring along the coast of Crete, Greece from Universit辰t Salzburg
]]> 175 3 https://cdn.slidesharecdn.com/ss_thumbnails/lemenkova-rostock-191205102924-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-PolinaLemenkova-48x48.jpg?cb=1718986326 www.linkedin.com/in/paulinelemenkova https://cdn.slidesharecdn.com/ss_thumbnails/presentationybbs-240621172500-032edace-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/economic-assessment-of-landslide-risk-for-the-waidhofen-a-d-ybbs-region-alpine-foreland-lower-austria/269811756 Economic assessment of... https://cdn.slidesharecdn.com/ss_thumbnails/lemenkovaassignement4-240621161252-e012ac67-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/flood-hazard-and-natural-risk-assessment-a-case-study-of-bangladesh/269810954 Flood Hazard and Natur... https://cdn.slidesharecdn.com/ss_thumbnails/presentationlemenkova2may2023compressed-240614152033-0f90830e-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/exploitation-of-historical-analog-seismological-records-by-image-processing-and-machine-learning/269687399 Exploitation of histor...