Unsupervised phytoplankton community detection and analysis of environmental and satellite parameters on each community
Marine dynamics largely affects the phytoplankton community com- position. The distribution characteristics of phytoplankton can reflect spatio-temporal variability in the marine ecosystem, on the other way around. In this work, we study the relation between remote sensing satellite observations, en...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/394183 |
| Acceso en línea: | https://hdl.handle.net/2117/394183 |
| Access Level: | acceso abierto |
| Palabra clave: | Cluster analysis Remote sensing Phytoplankton clustering community detection remote sensing phytoplankton Anàlisi de conglomerats Teledetecció Fitoplàncton Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció |
| Sumario: | Marine dynamics largely affects the phytoplankton community com- position. The distribution characteristics of phytoplankton can reflect spatio-temporal variability in the marine ecosystem, on the other way around. In this work, we study the relation between remote sensing satellite observations, environmental factors and phytoplankton com- munities. First, we employ network-based unsupervised clustering approaches to identify representative communities using metabarcoding data that has been collected both across depth and surface. Next, we investigate the relation between the detected phytoplankton communities and the environmental parameters (e.g., temperature, salinity, nutrients and so on). Our results show that phytoplankton communities are segregated based on the depth and basin. Additionally, for communities where the majority of samples are gathered from the Atlantic ocean, the nutrient levels are much higher than other communities. To extend this analysis to other years, a scientific ship should collect water samples in different years. This would be very costly and even infeasible for many applications such as analyzing the seasonal changes in plankton communities. Therefore, in the second part of our work, we utilize the fact that the reflected light from the ocean's surface that is captured by a remote sensing satellite has a specific relationship with the plankton composition. To this end, we first cluster the samples that are collected at the surface of the ocean. Next, we apply several machine learning algorithms to classify these representative communities from satellite data. Our top performing classifier reached 0.94 accuracy in leave-one-out cross vali- dation setting. The results show three top important features in predict- ing communities are surface temperature, chlorophyll and particulate organic carbon. |
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