Open Collections

Mangrove mapping in Saloum Delta, Senegal using Google Earth Engine Mangrove Mapping Methodology (GEEMMM)

Li, Yatian

Mangroves provide crucial biodiversity values to the ecosystem and the carbon dioxide sequestration capacity of mangrove forests helps mitigate global climate change. Given the diverse ecosystem and economic values, mapping how mangroves change over time is important for mangrove forest management and protection. The traditional mangrove mapping methodology using remote sensing requires a comprehensive understanding of mangrove ecology, remote sensing knowledge, and programming skills. The lack of specialists may stop the mapping of mangrove forests in many areas. In 2020, the Google Earth Engine Mangrove Mapping Methodology was introduced to accessibly map and monitor mangroves with random forest classifier and Landsat satellite imagery. This study applied Google Earth Engine Mangrove Mapping Methodology in Saloum Delta, Senegal for land cover classification with an overall accuracy of 96.45% in 2013 and 97.51% in 2023. Mangrove forests in Saloum Delta experienced heavy harvesting since 1950, and conservation projects have been conducted since 2004 by international organizations and the local government. The results suggest that 93.9% of mangrove forests remained unchanged, while 3.1% were lost and 2.9% saw an increase over the last decade. The results highlighted the mangrove loss areas that need more conservation attention and provided a valuable mangrove forest dynamic map to help the local communities in Saloum Delta with mangrove forest management. For the future development of Google Earth Engine Mangrove Mapping Methodology, the addition of more classifiers for land cover classification and higher resolution Sentinel satellite imagery should be considered. With a user-friendly interface and detailed guidance, Google Earth Engine Mangrove Mapping Methodology has a bright potential to help people with mangrove forest mapping for sustainable management globally in the future.

Dataset

2024-04-03

CC BY-NC-SA 4.0

https://doi.org/10.5683/SP3/7R1LL0

http://creativecommons.org/licenses/by-nc-sa/4.0

Dataverse