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.

jc-barreto/Mapping-Mangrove-Change-in-Mozambique

Domaine:

environment and energygeospatial

Type de record:

project
Créateur:
jc-
Hôte:
# Mapping-Mangrove-Change-in-Mozambique This notebook originates from research conducted in the field of GIS Modeling at Universidade Nova de Lisboa - IMS. The code presented here corresponds to the implementation discussed in the article titled 'Mapping Mangrove Change in Mozambique: Exploring Drivers and Patterns.' We kindly request that you acknowledge our work by citing the article and referencing this code in your own research. Thank you for your consideration. Mangroves are vital coastal ecosystems found in tropical and subtropical regions. These complex habitats consist of trees and shrubs that flourish in brackish water and provide a plethora of benefits to both wildlife and humans. They are exceptional carbon sequesters and serve as nurseries for marine species, while also protecting shorelines from erosion. However, mangroves are under threat from human activities such as deforestation, urbanization, and aquaculture, as well as the impacts of climate change, including sea level rise and intensified storms. This study focuses on analyzing the evolution of the mangrove ecosystem over time, using the Random Forest (RF) and the Support Vector Machine (SVM) method. The model considers distance to rivers and roads as significant drivers and yields an impressive accuracy of 99%. The analysis is conducted as part of the MozambES project, which aims to promote sustainable management of mangrove ecosystems in Sofala Province and enhance the livelihoods of rural communities. By providing insight into the changes in mangrove ecosystems, this study will contribute to the project's goal of improving the management and conservation of these vital coastal habitats.

Visit

github.com

Tasks

computer visionimage classification

Languages

Ndau

Licenses

MIT

Similaires

Mangrove mapping in Saloum Delta, Senegal using Google Earth Engine Mangrove Mapping Methodology (GEEMMM)Mangrove mapping along the Lower Casamance River in Senegal, using the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)Mangrove carbon stocks in Zambezi River Delta, MozambiqueMangrove cover raster in Mozambique in 2013 and 2023Mapping of the mangrove zonation of the Salou Delta in SenegalAkajiaku11/Predictive-Mapping-of-Oil-Spill-Induced-Mangrove-Degradation-in-Nigeria

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

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

Mangrove forests are highly productive, complex coastal ecosystems that shelter a diverse range of s

Mangrove carbon stocks in Zambezi River Delta, Mozambique

Carbon stocks in mangroves in the Zambezi River Delta of Mozambique (East Africa) were inventoried u

Mangrove cover raster in Mozambique in 2013 and 2023

30 m resolution raster of mangrove cover in Mozambique in 2013 and 2023. Data are divided by coastal

Mapping of the mangrove zonation of the Salou Delta in Senegal

2021 mangrove forest extent mapping data products at 10m resolution for Senegal created using a comb

Akajiaku11/Predictive-Mapping-of-Oil-Spill-Induced-Mangrove-Degradation-in-Nigeria

# Predictive Mapping of Oil Spill‑Induced Mangrove Degradation in Nigeria **Remote Sensing + Machine