Ennouri et al. (2025) Advancing change detection and climate risk assessment through remote sensing

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-13
• Authors: Karim Ennouri, Monia Ennouri, Mohamed Gouiaa, Fathi Hertelli, Mohamed Ali Triki
• DOI: 10.1016/b978-0-443-30204-6.00012-7

Research Groups

• Laboratory of Genetic Resources of Olive tree: Characterization, Valorization and Phytosanitary Protection, Olive Tree Institute, University of Sfax, Sfax, Tunisia
• Higher Institute of Applied Science and Technology of Mahdia, University of Monastir, Monastir, Tunisia
• Higher Institute of Agronomic Sciences of Chott Mariem, University of Sousse, Sousse, Tunisia
• Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia

Short Summary

This chapter introduces how remote sensing technologies, particularly satellite constellations like Landsat and Sentinel, are advancing change detection and climate risk assessment by enabling large-scale, timely, and reliable monitoring through long image time series analysis.

Objective

• To explore how remote sensing technologies, especially the use of long image time series from satellite constellations, advance change detection and climate risk assessment.

Study Configuration

• Spatial Scale: Large scale, covering different parts of the Earth, with geometric resolutions up to 10 meters.
• Temporal Scale: Continuous and repeated monitoring over extended periods, with data acquisition intervals as short as a few days, enabling long image time series analysis.

Methodology and Data

• Models used: Change detection (CD) methods, including traditional binary CD techniques and advanced CD frameworks utilizing long time series for automated information extraction.
• Data sources: Satellite remote sensing data from polar-orbiting satellites, including optical and synthetic aperture radars, specifically mentioning Sentinel constellations and the Landsat program.

Main Results

• Remote sensing, particularly with data from satellite constellations like Landsat and Sentinel, offers enhanced capabilities for large-scale, timely, and reliable monitoring of land dynamics.
• The availability of long image time series facilitates advanced change detection frameworks and automated information extraction, moving beyond traditional binary methods.
• These technological advancements are critical for improving climate research and hazard assessment.

Contributions

• This chapter highlights the paradigm shift in change detection from binary comparisons to the analysis of long image time series, enabled by advancements in satellite remote sensing. It underscores the critical role of these evolving methodologies in enhancing climate risk assessment and achieving sustainable development goals.

Funding

• Not specified in the provided text.

Citation

@article{Ennouri2025Advancing,
  author = {Ennouri, Karim and Ennouri, Monia and Gouiaa, Mohamed and Hertelli, Fathi and Triki, Mohamed Ali},
  title = {Advancing change detection and climate risk assessment through remote sensing},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-30204-6.00012-7},
  url = {https://doi.org/10.1016/b978-0-443-30204-6.00012-7}
}