On Monday, March 18, at 11:30 Hyeonmin Kang will present her master thesis on “Assessing the Mangrove Ecosystem in Eastern Obolo, Niger Delta” in seminar room 3, John-Skilton-Str. 4a.
From the abstract: This study focuses on assessing the mangrove ecosystem in Eastern Obolo, Niger Delta, utilizing object-based classification methods with machine learning algorithms from 2020 to 2022. The core objective was to analyze the expansion of Nypa palms and overall changes in the mangrove ecosystem. The research adopted an object-based approach within the Google Earth Engine (GEE) framework, integrating methodologies like the Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), and three machine learning algorithms (Random Forest, Classification and Regression Trees, Support Vector Machine) to classify the ecosystem. The study employed both Sentinel-1 and Sentinel-2 satellite data, evaluating their effectiveness in the object-based approach compared with the pixel-based approach.
1st supervisor: Dr. Insa Otte 2nd supervisor: Dr. Martin Wegmann
From the abstract: This study focuses on assessing the mangrove ecosystem in Eastern Obolo, Niger Delta, utilizing object-based classification methods with machine learning algorithms from 2020 to 2022. The core objective was to analyze the expansion of Nypa palms and overall changes in the mangrove ecosystem. The research adopted an object-based approach within the Google Earth Engine (GEE) framework, integrating methodologies like the Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), and three machine learning algorithms (Random Forest, Classification and Regression Trees, Support Vector Machine) to classify the ecosystem. The study employed both Sentinel-1 and Sentinel-2 satellite data, evaluating their effectiveness in the object-based approach compared with the pixel-based approach.
1st supervisor: Dr. Insa Otte 2nd supervisor: Dr. Martin Wegmann
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