MAPPING GLOBAL TIDAL MARSH SOIL CARBON
Effective action on climate change includes a priority to protect, enhance and restore natural carbon sinks, and tidal marshes are an extensive coastal vegetated system with a strong capacity for this. This project aims to generate the first global map of soil carbon in tidal marshes, in order to quantify the current soil carbon stocks in this ecosystem. To do so, we will employ a variety of data extraction, modelling and analysis methods. We are currently developing a training dataset from studies which have published soil carbon or organic matter content in tidal salt marshes. Using key covariate layers, we will develop a machine learning-based model to predict soil organic carbon density globally pixels identified as having tidal salt marshes. Our aim is to make this global map reproducible and updateable as more data get added to the model’s training dataset.
If you are interested in collaborating and/or have data you would like to contribute to our training dataset, please email Tania at tlgm2 [at] cam.ac.uk
MANGROVE ECOSYSTEM SERVICES: LINKING LOCAL AND GLOBAL DATA
Mangroves have been highlighted as supporting multiple ecosystem services including coastal protection, fisheries enhancement, timber and fuelwood production, tourism and climate regulation. This has stimulated increased interest in their conservation and restoration both to protect biodiversity and safeguard and increase provision of these benefits to human well-being. These ecosystem service values have been quantified at a variety of scales, from global scale models of fisheries and coastal protection, to more regional and local scale case studies. However, the challenge remains linking these data to on-the-ground conservation actions. This project aims to identify if global and regional scale data are accurate enough at local scales to quantify potential return for investment from conservation and restoration activities, and assess to what extent local-scale studies can be used to quantify ecosystem services values in other locations with similar biophysical, economic, social, or political settings.