Mapping the dynamic health of Sundarbans mangrove forests in the Bengal delta using satellite remote sensing and in-situ sensor data

Theme: Biodiversity, Ecology & Conservation

Primary Supervisor:

Mohammad Shamsudduha

Institute for Risk and Disaster Reduction, UCL

Mohammad Shamsudduha's Profile Picture

Secondary Supervisor:

Emma Tebbs

Department of Geography, KCL

Emma Tebbs's Profile Picture

Additional Supervisor(s):
Michael Williamson (Zoological Society of London)
Clare Duncan (Zoological Society of London)

Project Description:

Mangroves play a critical role in providing wide-ranging ecosystem services, supporting livelihoods, and acting as nature-based protective barriers for cyclones. Because of the critical role in carbon sequestration, mangrove forests are now firmly placed on the international climate mitigation and adaptation agenda. Sundarbans, comprising an area of 10,200 sq km and located in Bangladesh (62% of Sundarbans) and West Bengal, India (38%) within the Ganges, Brahmaputra, and Meghna (GBM) delta (also known as the Bengal delta), is the world’s largest single stretch of mangrove forests. Changes in land-use, sea-level rise and increased hydrometeorological hazards (e.g., Cyclone Sidr in 2007) linked to climate change are threatening the health of Sundarbans and its ecosystem.

Research so far has focused on changes in the spatiotemporal extent of Sundarbans using satellite remote sensing. To evaluate the health of the mangrove ecosystem and predict future changes, it is imperative to understand the environmental drivers impacting mangrove extent and condition. This can be achieved by combining satellite-derived environmental variables with in-situ monitoring of water and soil salinity in and around the forests. This project will use cloud-computing platforms such as the Google Earth Engine (GEE) to access satellite data and calculate a range of land and water-quality indices (e.g., Normalized Difference Salinity Index). In-situ soil and water quality will be monitored using spot sampling and telemetry-enabled, real-time data loggers. This project will apply a range of remote sensing and Geographic Information System (GIS) methods, and programming (e.g., R programming language) for data processing and analysis.

Policy Impact of Research:

Proposed research is expected to improve monitoring of land and water quality in and around Sundarbans mangrove forests, increase public awareness, inform policy making processes around climate adaptation and forest management. Furthermore, this project is expected to strengthen local research and monitoring capacity by working closely with concerned stakeholders in Bangladesh and India.

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