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Ocean-DC: An analysis ready data cube framework for environmental and climate change monitoring over the port areas

Ioannis Kavouras, Ioannis Rallis, Nikolaos Doulamis, Anastasios Doulamis

TL;DR

The paper addresses the challenge of monitoring coastal environments with heterogeneous, multi-source Earth Observation data. It introduces Ocean-DC, a data-harmonization framework that builds a 4D data cube from multiple data sources and precomputes common remote-sensing indices to produce an analysis-ready NetCDF product. A case study on the 2017 oil spill in the Saronikos gulf demonstrates the framework's ability to integrate Landsat-8/9 and Sentinel-2 data for time-series analysis and visualization in GIS tools. The work offers a practical, scalable tool for rapid coastal monitoring and disaster response, with plans to extend capabilities via machine learning and broader coastal applications.

Abstract

The environmental hazards and climate change effects causes serious problems in land and coastal areas. A solution to this problem can be the periodic monitoring over critical areas, like coastal region with heavy industrial activity (i.e., ship-buildings) or areas where a disaster (i.e., oil-spill) has occurred. Today there are several Earth and non-Earth Observation data available from several data providers. These data are huge in size and usually it is needed to combine several data from multiple sources (i.e., data with format differences) for a more effective evaluation. For addressing these issues, this work proposes the Ocean-DC framework as a solution in data harmonization and homogenization. A strong advantage of this Data Cube implementation is the generation of a single NetCDF product that contains Earth Observation data of several data types (i.e., Landsat-8 and Sentinel-2). To evaluate the effectiveness and efficiency of the Ocean-DC implementation, it is examined a case study of an oil-spill in Saronic gulf in September of 2017. The generated 4D Data Cube considers both Landsat-8,9 and Sentinel-2 products for a time-series analysis, before, during, and after the oil-spill event. The Ocean-DC framework successfully generated a NetCDF product, containing all the necessary remote sensing products for monitoring the oil-spill disaster in the Saronic gulf.

Ocean-DC: An analysis ready data cube framework for environmental and climate change monitoring over the port areas

TL;DR

The paper addresses the challenge of monitoring coastal environments with heterogeneous, multi-source Earth Observation data. It introduces Ocean-DC, a data-harmonization framework that builds a 4D data cube from multiple data sources and precomputes common remote-sensing indices to produce an analysis-ready NetCDF product. A case study on the 2017 oil spill in the Saronikos gulf demonstrates the framework's ability to integrate Landsat-8/9 and Sentinel-2 data for time-series analysis and visualization in GIS tools. The work offers a practical, scalable tool for rapid coastal monitoring and disaster response, with plans to extend capabilities via machine learning and broader coastal applications.

Abstract

The environmental hazards and climate change effects causes serious problems in land and coastal areas. A solution to this problem can be the periodic monitoring over critical areas, like coastal region with heavy industrial activity (i.e., ship-buildings) or areas where a disaster (i.e., oil-spill) has occurred. Today there are several Earth and non-Earth Observation data available from several data providers. These data are huge in size and usually it is needed to combine several data from multiple sources (i.e., data with format differences) for a more effective evaluation. For addressing these issues, this work proposes the Ocean-DC framework as a solution in data harmonization and homogenization. A strong advantage of this Data Cube implementation is the generation of a single NetCDF product that contains Earth Observation data of several data types (i.e., Landsat-8 and Sentinel-2). To evaluate the effectiveness and efficiency of the Ocean-DC implementation, it is examined a case study of an oil-spill in Saronic gulf in September of 2017. The generated 4D Data Cube considers both Landsat-8,9 and Sentinel-2 products for a time-series analysis, before, during, and after the oil-spill event. The Ocean-DC framework successfully generated a NetCDF product, containing all the necessary remote sensing products for monitoring the oil-spill disaster in the Saronic gulf.
Paper Structure (8 sections, 3 equations, 3 figures, 1 table)

This paper contains 8 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contribution
  4. Proposed Ocean-DC framework
  5. Case Study Analysis of the oil-spill in Saronikos gulf at Septempber 2017
  6. Discussion
  7. Conclusions
  8. Acknowledgments

Figures (3)

  • Figure 1: An example of a Data Cube's dimension expanding.
  • Figure 2: The workflow architecture of the Ocean-DC implementation.
  • Figure 3: Time series monitoring of the water quality of Saronikos gulf before and after the oil-spill using the RGB, NDWI, WRI, and OSI products of the Ocean-DC implementation file. The green box indicates the area of the oil-spill.