Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Mangrove mapping along the Lower Casamance River in Senegal, using the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)

Domaine:

geospatialenvironment and energy

Type de record:

dataset
Créateur:
Zha
Éditeur:
Zha
Éditeur:
Bor
Hôte:avatar
Mangrove forests are highly productive, complex coastal ecosystems that shelter a diverse range of species and offer important ecological services. In recent decades, mangrove forests in Africa have been experiencing drastic degradation and decline in population due to heavy anthropogenic pressures and climate change-induced sea level rise. To ensure the future long-term well-being of these unique ecosystems, and to reflect past conservation efforts, quantitative analysis of the past and current mangrove forest condition on a regional scale is much needed. In this study, we chose to conduct systematic mapping of mangrove forests along the Lower Casamance River region, using Google Earth Engine Mangrove Mapping Methodology (GEEMMM), for the years 1990 and 2021. Overall, we achieve a validation accuracy of 87.13% for the 2021 classification and 90.25% for the 1990 classification. Our study found that, across our study regions, there was a net loss of 123.99 km2 in our open-canopy mangrove I class and a net gain of 63.04 km2 in our closed-canopy mangrove class. This suggested that while some areas had seen a slight increase in mangrove populations either due to natural regeneration or ongoing conservation efforts, the overall mangrove forests in our study region had experienced major mangrove forest degradation. Furthermore, the majority of mangrove forest loss happened within 10km of the shoreline, suggesting that fishing activity might be one of the biggest contributors to mangrove forest loss.

Visit

doi.orgborealisdata.ca

Tasks

computer visionimage classification

Tags

Earth and Environmental SciencesMangrove

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Mangrove mapping in Saloum Delta, Senegal using Google Earth Engine Mangrove Mapping Methodology (GEEMMM)Comparison Between Sentinel-2 and Landsat in Mapping Mangroves in The Gambia Using the Google Earth Engine Mangrove Mapping Methodology (GEM v2)Monitoring Mangrove Recovery in the Bay of Assassins, Madagascar Using Sentinel-2 Imagery and the Google Earth Mangrove Mapping Methodology (GEM)

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

Mangroves provide crucial biodiversity values to the ecosystem and the carbon dioxide sequestration

Comparison Between Sentinel-2 and Landsat in Mapping Mangroves in The Gambia Using the Google Earth Engine Mangrove Mapping Methodology (GEM v2)

Mangroves play a vital role in coastal environments by supporting biodiversity, protecting shoreline

Monitoring Mangrove Recovery in the Bay of Assassins, Madagascar Using Sentinel-2 Imagery and the Google Earth Mangrove Mapping Methodology (GEM)

Mangroves are unique plant species that support livelihoods and provide critical ecosystem services.