Informace o kvalifikační práci Monitoring the Evolution of the Kaiwhata Landslide in New Zealand using Object-based Image Analysis and Sentinel-2 Time Series
Landslides are geological events that occur frequently in mountainous and hilly areas of New Zealand, causing significant damage and changes to the landscape. These events can be disastrous, and monitoring their evolution and subsequent impacts is essential to mitigate hazards that could arise in later reactivation phases or similar cases. The abundance of time-series remote sensing data has facilitated the mapping and monitoring of landslides. Applying object-based image analysis (OBIA) allows for a more detailed understanding of the complex natural phenomena and help to integrate several type of features that are essential in the analysis and as the result, it facilitates to semi-automatically map the evolution of natural phenomena such as landslides. In this particular case, the focus is on the Kaiwhata landslide, which is located in the Wairarapa region of New Zealand. The data from Sentinel-2 and PlanetScope have been used from 2017 to 2021 to semi-automatically map the Kaiwhata landslide and monitor its evolution and impacts on the upstream area. To achieve this, the author has defined an OBIA workflow, consisting of rulesets that applied to the time-series data to extract the landslides and landslide-dammed lakes. The workflow has been defined using different multiresolution segmentation (MRS) values, classification parameters, and class refinements. The desired classes have been visualized not only in static two-dimensional (2D) maps but also in interactive three-dimensional (3D) models to influence the augmented visual impression and semantic augmentation of the time-series landslide and landslide-dammed lake results. The results of the OBIA mapping reveal the extent and the gradual increase in the area of the landslides, with two major changes occurring in June 2019 and November 2020. These changes were followed by the formation of temporary landslide-dammed upstream lakes along the Kaiwhata River, due to the intense rainfall. Overall, OBIA allows for a more detailed understanding of the landslides and their evolution and help to identify patterns and features that are not easily detectable by pixel-based approaches. The use of OBIA and time-series remote sensing data can provide valuable insights into the evolution of landslides and their impacts on the landscape.
Anotace v angličtině
Landslides are geological events that occur frequently in mountainous and hilly areas of New Zealand, causing significant damage and changes to the landscape. These events can be disastrous, and monitoring their evolution and subsequent impacts is essential to mitigate hazards that could arise in later reactivation phases or similar cases. The abundance of time-series remote sensing data has facilitated the mapping and monitoring of landslides. Applying object-based image analysis (OBIA) allows for a more detailed understanding of the complex natural phenomena and help to integrate several type of features that are essential in the analysis and as the result, it facilitates to semi-automatically map the evolution of natural phenomena such as landslides. In this particular case, the focus is on the Kaiwhata landslide, which is located in the Wairarapa region of New Zealand. The data from Sentinel-2 and PlanetScope have been used from 2017 to 2021 to semi-automatically map the Kaiwhata landslide and monitor its evolution and impacts on the upstream area. To achieve this, the author has defined an OBIA workflow, consisting of rulesets that applied to the time-series data to extract the landslides and landslide-dammed lakes. The workflow has been defined using different multiresolution segmentation (MRS) values, classification parameters, and class refinements. The desired classes have been visualized not only in static two-dimensional (2D) maps but also in interactive three-dimensional (3D) models to influence the augmented visual impression and semantic augmentation of the time-series landslide and landslide-dammed lake results. The results of the OBIA mapping reveal the extent and the gradual increase in the area of the landslides, with two major changes occurring in June 2019 and November 2020. These changes were followed by the formation of temporary landslide-dammed upstream lakes along the Kaiwhata River, due to the intense rainfall. Overall, OBIA allows for a more detailed understanding of the landslides and their evolution and help to identify patterns and features that are not easily detectable by pixel-based approaches. The use of OBIA and time-series remote sensing data can provide valuable insights into the evolution of landslides and their impacts on the landscape.
Klíčová slova
Landslide; New Zealand; Earth Observation (EO), Object-based Image-Analysis (OBIA); Time Series Analysis; 3D Visualization
Klíčová slova v angličtině
Landslide; New Zealand; Earth Observation (EO), Object-based Image-Analysis (OBIA); Time Series Analysis; 3D Visualization
Rozsah průvodní práce
-
Jazyk
AN
Anotace
Landslides are geological events that occur frequently in mountainous and hilly areas of New Zealand, causing significant damage and changes to the landscape. These events can be disastrous, and monitoring their evolution and subsequent impacts is essential to mitigate hazards that could arise in later reactivation phases or similar cases. The abundance of time-series remote sensing data has facilitated the mapping and monitoring of landslides. Applying object-based image analysis (OBIA) allows for a more detailed understanding of the complex natural phenomena and help to integrate several type of features that are essential in the analysis and as the result, it facilitates to semi-automatically map the evolution of natural phenomena such as landslides. In this particular case, the focus is on the Kaiwhata landslide, which is located in the Wairarapa region of New Zealand. The data from Sentinel-2 and PlanetScope have been used from 2017 to 2021 to semi-automatically map the Kaiwhata landslide and monitor its evolution and impacts on the upstream area. To achieve this, the author has defined an OBIA workflow, consisting of rulesets that applied to the time-series data to extract the landslides and landslide-dammed lakes. The workflow has been defined using different multiresolution segmentation (MRS) values, classification parameters, and class refinements. The desired classes have been visualized not only in static two-dimensional (2D) maps but also in interactive three-dimensional (3D) models to influence the augmented visual impression and semantic augmentation of the time-series landslide and landslide-dammed lake results. The results of the OBIA mapping reveal the extent and the gradual increase in the area of the landslides, with two major changes occurring in June 2019 and November 2020. These changes were followed by the formation of temporary landslide-dammed upstream lakes along the Kaiwhata River, due to the intense rainfall. Overall, OBIA allows for a more detailed understanding of the landslides and their evolution and help to identify patterns and features that are not easily detectable by pixel-based approaches. The use of OBIA and time-series remote sensing data can provide valuable insights into the evolution of landslides and their impacts on the landscape.
Anotace v angličtině
Landslides are geological events that occur frequently in mountainous and hilly areas of New Zealand, causing significant damage and changes to the landscape. These events can be disastrous, and monitoring their evolution and subsequent impacts is essential to mitigate hazards that could arise in later reactivation phases or similar cases. The abundance of time-series remote sensing data has facilitated the mapping and monitoring of landslides. Applying object-based image analysis (OBIA) allows for a more detailed understanding of the complex natural phenomena and help to integrate several type of features that are essential in the analysis and as the result, it facilitates to semi-automatically map the evolution of natural phenomena such as landslides. In this particular case, the focus is on the Kaiwhata landslide, which is located in the Wairarapa region of New Zealand. The data from Sentinel-2 and PlanetScope have been used from 2017 to 2021 to semi-automatically map the Kaiwhata landslide and monitor its evolution and impacts on the upstream area. To achieve this, the author has defined an OBIA workflow, consisting of rulesets that applied to the time-series data to extract the landslides and landslide-dammed lakes. The workflow has been defined using different multiresolution segmentation (MRS) values, classification parameters, and class refinements. The desired classes have been visualized not only in static two-dimensional (2D) maps but also in interactive three-dimensional (3D) models to influence the augmented visual impression and semantic augmentation of the time-series landslide and landslide-dammed lake results. The results of the OBIA mapping reveal the extent and the gradual increase in the area of the landslides, with two major changes occurring in June 2019 and November 2020. These changes were followed by the formation of temporary landslide-dammed upstream lakes along the Kaiwhata River, due to the intense rainfall. Overall, OBIA allows for a more detailed understanding of the landslides and their evolution and help to identify patterns and features that are not easily detectable by pixel-based approaches. The use of OBIA and time-series remote sensing data can provide valuable insights into the evolution of landslides and their impacts on the landscape.
Klíčová slova
Landslide; New Zealand; Earth Observation (EO), Object-based Image-Analysis (OBIA); Time Series Analysis; 3D Visualization
Klíčová slova v angličtině
Landslide; New Zealand; Earth Observation (EO), Object-based Image-Analysis (OBIA); Time Series Analysis; 3D Visualization
Zásady pro vypracování
The main aim of the thesis is to verify the matter of multi-scale object-based analysis for increasing the accuracy of landslide mapping and monitoring. The student will identify the main spectral, spatial, and contextual parameters to apply for landslide and landslide-dammed lake classification for selected landslide and modify a single semi-automated object-based classification workflow for landslide time series analysis. The results will be temporal delimitations of landslides, maps, and interactive 3D web-based visualization application.
The student will attach all the collected datasets and all the animations to the thesis in digital form. The student will create a website about the thesis following the rules available on the department's website and a poster about the diploma thesis in A2 format. The student will submit the entire text (text, attachments, poster, outputs, input and output data) in digital form on a storage medium and the text of the thesis in two bound copies to the department's secretary.
Zásady pro vypracování
The main aim of the thesis is to verify the matter of multi-scale object-based analysis for increasing the accuracy of landslide mapping and monitoring. The student will identify the main spectral, spatial, and contextual parameters to apply for landslide and landslide-dammed lake classification for selected landslide and modify a single semi-automated object-based classification workflow for landslide time series analysis. The results will be temporal delimitations of landslides, maps, and interactive 3D web-based visualization application.
The student will attach all the collected datasets and all the animations to the thesis in digital form. The student will create a website about the thesis following the rules available on the department's website and a poster about the diploma thesis in A2 format. The student will submit the entire text (text, attachments, poster, outputs, input and output data) in digital form on a storage medium and the text of the thesis in two bound copies to the department's secretary.
Seznam doporučené literatury
Amatya, P., Kirschbaum, D., Stanley, T., & Tanyas, H. (2021). Landslide mapping using object-based image analysis and open-source tools. Engineering Geology, 282, 106000.
Ghorbanzadeh, O., Shahabi, H., Crivellari, A., Homayouni, S., Blaschke, T., & Ghamisi, P. (2022). Landslide detection using deep learning and object-based image analysis. Landslides, 19(4), 929-939.
Gudiyangada Nachappa, T., Kienberger, S., Meena, S. R., Hölbling, D., & Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1), 572-600.
Hölbling, D., Friedl, B., & Eisank, C. (2015). An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Science Informatics, 8, 327-335.
Morgenstern, R., Massey, C., Rosser, B., & Archibald, G. (2021). Landslide dam hazards: assessing their formation, failure modes, longevity and downstream impacts. Understanding and Reducing Landslide Disaster Risk: Volume 5 Catastrophic Landslides and Frontiers of Landslide Science 5th, 117-123.
Liashenko, D., Belenok, V., Spitsa, R., Pavlyuk, D., & Boiko, O. (2020, November). Landslide GIS modelling with QGIS software. In XIV International Scientific Conference "Monitoring of Geological Processes and Ecological Condition of the Environment” (Vol. 2020, No. 1, pp. 1-5). EAGE Publications BV.
Seznam doporučené literatury
Amatya, P., Kirschbaum, D., Stanley, T., & Tanyas, H. (2021). Landslide mapping using object-based image analysis and open-source tools. Engineering Geology, 282, 106000.
Ghorbanzadeh, O., Shahabi, H., Crivellari, A., Homayouni, S., Blaschke, T., & Ghamisi, P. (2022). Landslide detection using deep learning and object-based image analysis. Landslides, 19(4), 929-939.
Gudiyangada Nachappa, T., Kienberger, S., Meena, S. R., Hölbling, D., & Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1), 572-600.
Hölbling, D., Friedl, B., & Eisank, C. (2015). An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Science Informatics, 8, 327-335.
Morgenstern, R., Massey, C., Rosser, B., & Archibald, G. (2021). Landslide dam hazards: assessing their formation, failure modes, longevity and downstream impacts. Understanding and Reducing Landslide Disaster Risk: Volume 5 Catastrophic Landslides and Frontiers of Landslide Science 5th, 117-123.
Liashenko, D., Belenok, V., Spitsa, R., Pavlyuk, D., & Boiko, O. (2020, November). Landslide GIS modelling with QGIS software. In XIV International Scientific Conference "Monitoring of Geological Processes and Ecological Condition of the Environment” (Vol. 2020, No. 1, pp. 1-5). EAGE Publications BV.
Přílohy volně vložené
Poster, Website
Přílohy vázané v práci
ilustrace, mapy, grafy, tabulky
Převzato z knihovny
Ne
Plný text práce
Přílohy
Posudek(y) oponenta
Hodnocení vedoucího
Záznam průběhu obhajoby
Comprehensive overview of OBIA workflow
Results were presented through maps with tabular overview
Vegetation indexes described
Introduction of interactive storytelling application
Possibilities of implementation in another platforms