Improving monitoring and management of low-lying coastal areas with Sentinel-2 data: the Ebro Delta showcase
(English) Coastal areas support important ecosystems with great ecological value, giving rise to countless resources increasingly exploited by humans. Understanding the processes occurring in both inland and aquatic ecosystems, as well as their mutual interactions, and with anthropic activities is r...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/689618 |
| Acceso en línea: | http://hdl.handle.net/10803/689618 https://dx.doi.org/10.5821/dissertation-2117-398826 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria agroalimentària 502 639 |
| Sumario: | (English) Coastal areas support important ecosystems with great ecological value, giving rise to countless resources increasingly exploited by humans. Understanding the processes occurring in both inland and aquatic ecosystems, as well as their mutual interactions, and with anthropic activities is required. In this line, the new generation of high-resolution multispectral Sentinel satellites (Sentinel-2; S2) extend the capabilities for the integrated monitoring of coastal areas thanks to their spatial and temporal resolutions (up to 10 m and 5 days). However, global remote sensing issues/limitations (e.g., cloud screening, spectral mixing, atmospheric correction) and regional-specific characteristics (e.g., involved ecosystems, economic fabric, interlinkages), make up challenges of diverse nature. The basis of the research presented in this thesis is to explore the potential of S2 for the monitoring of coastal areas, and the associated technical and scientific questions. The work is focused on the processing of S2 imagery for characterizing Ebro Delta (Spain) coastal features and their dynamics, involving aquaculture, agriculture, spatial planning, environmental monitoring, and preparedness for natural hazards. From atmospheric correction and image pre-processing (first steps) to data modelling and analysis (last steps), a number of technical and scientific challenges have been addressed. In coastal waters, different atmospheric correction levels, processors, spectral combinations, and statistical models have been used for mapping water quality (i.e., chlorophyll-a, Secchi disk depth) and macrophytes (i.e., seagrass, macroalgae). For estimating chlorophyll-a (proxy of phytoplankton biomass), the best results were obtained using simple ratios including visible and/or red-edge bands applied to Rayleigh or full-atmospheric corrected imagery, by fitting either linear or 2nd-degree polynomial (MAE ~ 0.6 mg/m3), obtaining time series which allowed to relate the distribution of phytoplankton with the environmental and anthropic forcing. The relationship between Secchi disk depth and light attenuation products (r > 0.75) demonstrated the feasibility of monitoring water clarity with S2. In relation to the estimation of macrophytes coverage, machine-learning supervised classification of S2 VIS-NIR composites was used for assessing the spatial coverage of seagrass and macroalgae communities in shallow waters, unveiling a negative impact of agricultural runoff on macrophytes’ communities. Inland, an automatic method was developed based on the identification of key points in the combined temporal profiles of three common vegetation and land surface water indexes for the extraction of rice phenological metrics, irrigation management, and crop yield proxy. The results provided information on significant rice phenological stages and field status (e.g., rice maturity, hydroperiod), also showing that crop yield is better estimated during the rice heading period (r = - 0.8). Finally, for assessing storms’ effects with S2, a combined flooding-water quality monitoring method was defined and implemented, reaffirming the capabilities of S2 for depicting the different grades of land and aquatic environments’ resilience. The conducted research has been applied to the estimation of phytoplankton biomass at coastal bays (aquaculture), the generation of information on crop dynamics and management (agriculture), the assessment of agriculture runoff disturbance in coastal waters (environmental monitoring), and the characterization of storms’ effects on land and water ecosystems (natural hazards). A work that brings the application of satellite image processing to scientists, engineers, coastal managers, and stakeholders by providing results that demonstrate the usefulness of these viable and low-cost techniques for high-quality coastal monitoring. |
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