Mapping Mangrove Forests: Processing and visualization of multi-sensor Earth Observation data for the Colombian Pacific coast

Sayana DE GOROSTIZAGA MOXON

Master's thesis

Abstract:

Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Abstract:

Mangrove forests are among the most productive ecosystems on Earth and are essential for the preservation of biodiversity and the livelihoods of coastal communities around the world. However, they are facing severe threats from anthropogenic activities, which are having an impact on them both in a direct (human development, pollution, etc) and indirect (sea level rise, changing climatic conditions, etc) form. Remote sensing has become an essential instrument to monitor mangrove forest distributions and land use/cover dynamics within and around these ecosystems. The technological advancements in cloud-computing services such as the Google Earth Engine (GEE), are helping reduce the practical limitations concerning processing power and data availability. This study makes use of data acquired by the Copernicus Sentinel-1 (radar) and Sentinel-2 (optical) missions and combines it with the capabilities of GEE and state-of-the-art classification approaches to derive mangrove forest distributions along the Colombian Pacific coast. The results demonstrate its application and value to uncover the distribution of mangrove forests in a tropical region, where cloud-prevalence poses a common limitation to using optical imagery alone. The findings reveal the distribution and extent of mangrove cover over the entire Colombian Pacific coast for the year 2020. The study contributes to a growing body of research advocating full exploitation of the Copernicus Sentinel-1 and Sentinel-2 imagery in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Keywords

Sentinel 1, Sentinel 2, data fusion, forest monitoring, mangrove forests, remote sensing, satellite earth observation, time series analysis, Colombia

Language used: English

Date on which the thesis was submitted / produced: 20. 5. 2021

Thesis defence

• Supervisor: Mgr. Rostislav Nétek, Ph.D.

Palacký University Olomouc

Faculty of Science

Master programme / field:
Geoinformatics and Cartography / Geoinformatics and Cartography