Thesis subject
MSc thesis topic: Use of Early Warning forest disturbance data in the Republic of Congo
Globally forests are important for the provision of goods and services such as carbon storage and watershed protection. Donors such as the World Bank are supporting sustainable forest management and restoration in the major forest biomes including the Congo Basin. Available Earth Observation data, including Early Warning information can potentially help to assess progress in these projects.
In the Republic of Congo, the World Bank through their Forest Carbon Partnership Facility (FCPF) are implementing an Emission Reductions Program in Sangha-Likouala. The project aims to support reduced impact logging and forest protection in forest concessions, avoiding the conversion of forests with high conservation value in palm oil concessions as well as livelihood activities in communities (climate smart agriculture (agro-forestry) in degraded forest areas, smallholder outgrower schemes for palm oil , Payment for Environmental Services).
Forest disturbance alerts in near-real time (NRT) – so called Early Warning data are being provided for the region, and can potentially provide useful information to support the World Bank project activities. A NRT system provides timely information on the location and intensity of forest canopy disturbances. Actions can then be planned based on User needs, and ancillary data – such as information on forest concessions or protected areas.
Relevance
This thesis is linked to the ESA funded project, in which WU is a partner: https://www.eo4sd-forest.info/
Objectives
- Assess the User needs within the project for Early Warning data
- Provide a demonstration of the utility for Early Warning data to meet one or two of the user needs identified in the Project
- Make recommendations for the future integration of Early Warning data in similar international projects
Literature
- https://projects.worldbank.org/en/projects-operations/project-detail/P163361
- https://www.wur.nl/en/Research-Results/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Sensing-measuring/RADD-Forest-Disturbance-Alert.htm
Requirements
- Good use of GIS software – or alternative such as R
- No prerequisite courses