Informace o kvalifikační práci Mapping Mangrove Forests: Processing and visualization of multi-sensor Earth Observation data for the Colombian Pacific coast
Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.
Anotace v angličtině
Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.
Klíčová slova
Sentinel 1; Sentinel 2; data fusion; forest monitoring; mangrove forests; remote sensing; satellite earth observation; time series analysis; Colombia
Klíčová slova v angličtině
Sentinel 1; Sentinel 2; data fusion; forest monitoring; mangrove forests; remote sensing; satellite earth observation; time series analysis; Colombia
Rozsah průvodní práce
67
Jazyk
AN
Anotace
Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.
Anotace v angličtině
Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.
Klíčová slova
Sentinel 1; Sentinel 2; data fusion; forest monitoring; mangrove forests; remote sensing; satellite earth observation; time series analysis; Colombia
Klíčová slova v angličtině
Sentinel 1; Sentinel 2; data fusion; forest monitoring; mangrove forests; remote sensing; satellite earth observation; time series analysis; Colombia
Zásady pro vypracování
The aim of this study is to process multi-sensor Earth Observation data for mangrove forest cover mapping and integrate the resulting products into a web-map. The pilot study will implement data from Copernicus (Sentinel) missions. The study area will be located in the Colombian Pacific coast. The student will investigate, evaluate and apply techniques for semi-automatic classification and validation, primarily within Google Earth Engine. Finally, the resulting products will be integrated in a web-map used to visualize the map outputs.
The student will attach all the collected datasets and all products of 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 secretary of the department.
Zásady pro vypracování
The aim of this study is to process multi-sensor Earth Observation data for mangrove forest cover mapping and integrate the resulting products into a web-map. The pilot study will implement data from Copernicus (Sentinel) missions. The study area will be located in the Colombian Pacific coast. The student will investigate, evaluate and apply techniques for semi-automatic classification and validation, primarily within Google Earth Engine. Finally, the resulting products will be integrated in a web-map used to visualize the map outputs.
The student will attach all the collected datasets and all products of 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 secretary of the department.
Seznam doporučené literatury
Google Earth Engine.[online] Available from URL: https://earthengine.google.com/ Shapiro, Aurelie. (2018). Mozambique Mangrove Extent 1995-present. 10.13140/RG.2.2.18470.55367. Perea-Ardila, M.A., Oviedo-Barrero, F., Leal-Villamil, J.2019. Mangrove forest mapping through remote sensing imagery: study case for Buenaventura, Colombia. Revista de Teledetección,53, 73-86. https://doi.org/10.4995/raet.2019.11684 Hu, Luojia & Xu, Nan & Liang, Jian & Li, Zhichao & Chen, Luzhen & Zhao, Feng. (2020). Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China. Remote Sensing. 10.3390/rs12193120. Yancho, J.M.M.; Jones, T.G.; Gandhi, S.R.; Ferster, C.; Lin, A.; Glass, L. (2020) The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sens. 12, 3758. https://doi.org/10.3390/rs12223758
Seznam doporučené literatury
Google Earth Engine.[online] Available from URL: https://earthengine.google.com/ Shapiro, Aurelie. (2018). Mozambique Mangrove Extent 1995-present. 10.13140/RG.2.2.18470.55367. Perea-Ardila, M.A., Oviedo-Barrero, F., Leal-Villamil, J.2019. Mangrove forest mapping through remote sensing imagery: study case for Buenaventura, Colombia. Revista de Teledetección,53, 73-86. https://doi.org/10.4995/raet.2019.11684 Hu, Luojia & Xu, Nan & Liang, Jian & Li, Zhichao & Chen, Luzhen & Zhao, Feng. (2020). Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China. Remote Sensing. 10.3390/rs12193120. Yancho, J.M.M.; Jones, T.G.; Gandhi, S.R.; Ferster, C.; Lin, A.; Glass, L. (2020) The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sens. 12, 3758. https://doi.org/10.3390/rs12223758
Přílohy volně vložené
1 CD ROM
Přílohy vázané v práci
-
Převzato z knihovny
Ano
Plný text práce
Přílohy
Posudek(y) oponenta
Hodnocení vedoucího
Záznam průběhu obhajoby
At the beginning of the master thesis defence, the student explained aims, methods, and results of the diploma thesis to the commission. After the presentation, the reviews were presented from the supervisor, co-supervisor, and opponent. During the discussion following topics were discussed - applicability of methodology to other tree species; other useable platforms; effect of tide in calculation
Exam:
- infographics; softwares; types of diagrams;
- satellite image time series; data cube concept; sentinel 5